R-universe search: "rna-seq"

bioc

clustifyr:Classifier for Single-cell RNA-seq Using Cell Clusters

Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment.

Maintained by Rui Fu. Last updated 5 months ago.

singlecell annotation sequencing microarray geneexpression assign-identities clusters marker-genes rna-seq single-cell-rna-seq

80.7 match 119 stars 9.63 score 296 scripts

bioc

bambu:Context-Aware Transcript Quantification from Long Read RNA-Seq data

bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage.

Maintained by Ying Chen. Last updated 1 months ago.

alignment coverage differentialexpression featureextraction geneexpression genomeannotation genomeassembly immunooncology longread multiplecomparison normalization rnaseq regression sequencing software transcription transcriptomics bambu bioconductor long-reads nanopore nanopore-sequencing rna-seq rna-seq-analysis transcript-quantification transcript-reconstruction cpp

46.2 match 197 stars 9.03 score 91 scripts 1 dependents

bioc

BgeeDB:Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology

A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.

Maintained by Julien Wollbrett. Last updated 5 months ago.

software dataimport sequencing geneexpression microarray go genesetenrichment bioinformatics enrichment-analysis rna-seq scrna-seq single-cell

46.7 match 15 stars 8.46 score 19 scripts 1 dependents

bioc

scran:Methods for Single-Cell RNA-Seq Data Analysis

Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell clustering bioconductor-package human-cell-atlas single-cell-rna-seq openblas cpp

24.9 match 41 stars 13.14 score 7.6k scripts 36 dependents

bioc

pcaExplorer:Interactive Visualization of RNA-seq Data Using a Principal Components Approach

This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology visualization rnaseq dimensionreduction principalcomponent qualitycontrol gui reportwriting shinyapps bioconductor principal-components reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

33.3 match 56 stars 9.63 score 180 scripts

satijalab

Seurat:Tools for Single Cell Genomics

A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.

Maintained by Paul Hoffman. Last updated 1 years ago.

human-cell-atlas single-cell-genomics single-cell-rna-seq cpp

18.2 match 2.4k stars 16.86 score 50k scripts 73 dependents

bioc

scPipe:Pipeline for single cell multi-omic data pre-processing

A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

Maintained by Shian Su. Last updated 3 months ago.

immunooncology software sequencing rnaseq geneexpression singlecell visualization sequencematching preprocessing qualitycontrol genomeannotation dataimport curl bzip2 xz-utils zlib cpp

33.5 match 68 stars 9.02 score 84 scripts

bioc

FRASER:Find RAre Splicing Events in RNA-Seq Data

Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects.

Maintained by Christian Mertes. Last updated 5 months ago.

rnaseq alternativesplicing sequencing software genetics coverage aberrant-splicing diagnostics outlier-detection rare-disease rna-seq splicing openblas cpp

34.5 match 41 stars 8.50 score 155 scripts

bioc

BgeeCall:Automatic RNA-Seq present/absent gene expression calls generation

BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org).

Maintained by Julien Wollbrett. Last updated 5 months ago.

software geneexpression rnaseq biology gene-expression gene-level intergenic-regions present-absent-calls rna-seq rna-seq-libraries scrna-seq

52.5 match 3 stars 5.56 score 9 scripts

bioc

singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Maintained by Joshua David Campbell. Last updated 22 days ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

28.2 match 181 stars 10.16 score 252 scripts

pmartr

pmartR:Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data

Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>. Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>. Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>. Matzke et al. (2013) <doi:10.1002/pmic.201200269>. Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>. Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.

Maintained by Lisa Bramer. Last updated 2 days ago.

data-summarization lipids mass-spectrometry metabolites metabolomics-data peptides proteins rna-seq-analysis openblas cpp

33.8 match 40 stars 7.69 score 144 scripts

bioc

OUTRIDER:OUTRIDER - OUTlier in RNA-Seq fInDER

Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results.

Maintained by Christian Mertes. Last updated 5 months ago.

immunooncology rnaseq transcriptomics alignment sequencing geneexpression genetics count-data diagnostics expression-analysis mendelian-genetics outlier-detection rna-seq openblas cpp

28.6 match 49 stars 9.07 score 110 scripts 1 dependents

bioc

metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms

Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.

Maintained by Panagiotis Moulos. Last updated 3 days ago.

software geneexpression differentialexpression workflowstep preprocessing qualitycontrol normalization reportwriting rnaseq transcription sequencing transcriptomics bayesian clustering cellbiology biomedicalinformatics functionalgenomics systemsbiology immunooncology alternativesplicing differentialsplicing multiplecomparison timecourse dataimport atacseq epigenetics regression proprietaryplatforms genesetenrichment batcheffect chipseq

42.4 match 7 stars 6.05 score 3 scripts

bioc

M3Drop:Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

This package fits a model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. Also includes an method for calculating exact Pearson residuals in UMI-tagged data using a library-size aware negative binomial model.

Maintained by Tallulah Andrews. Last updated 5 months ago.

rnaseq sequencing transcriptomics geneexpression software differentialexpression dimensionreduction featureextraction human-cell-atlas rna-seq single-cell single-cell-rna-seq

27.5 match 29 stars 8.71 score 119 scripts 2 dependents

bioc

scuttle:Single-Cell RNA-Seq Analysis Utilities

Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology singlecell rnaseq qualitycontrol preprocessing normalization transcriptomics geneexpression sequencing software dataimport openblas cpp

23.2 match 10.21 score 1.7k scripts 80 dependents

bioc

scmap:A tool for unsupervised projection of single cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment.

Maintained by Vladimir Kiselev. Last updated 5 months ago.

immunooncology singlecell software classification supportvectormachine rnaseq visualization transcriptomics datarepresentation transcription sequencing preprocessing geneexpression dataimport bioconductor-package human-cell-atlas projection-mapping single-cell-rna-seq openblas cpp

26.2 match 95 stars 8.82 score 172 scripts

bioc

raer:RNA editing tools in R

Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources.

Maintained by Kent Riemondy. Last updated 5 months ago.

multiplecomparison rnaseq singlecell sequencing coverage epitranscriptomics featureextraction annotation alignment bioconductor-package rna-seq-analysis single-cell-analysis single-cell-rna-seq curl bzip2 xz-utils zlib

38.5 match 8 stars 5.98 score 6 scripts

bioc

DifferentialRegulation:Differentially regulated genes from scRNA-seq data

DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. From a mathematical point of view, DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. Furthermore, our method also deals with two major sources of mapping uncertainty: i) 'ambiguous' reads, compatible with both spliced and unspliced versions of a gene, and ii) reads mapping to multiple genes. In particular, ambiguous reads are treated separately from spliced and unsplced reads, while reads that are compatible with multiple genes are allocated to the gene of origin. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs).

Maintained by Simone Tiberi. Last updated 5 months ago.

differentialsplicing bayesian genetics rnaseq sequencing differentialexpression geneexpression multiplecomparison software transcription statisticalmethod visualization singlecell genetarget openblas cpp

43.4 match 10 stars 5.30 score 4 scripts

gfellerlab

SuperCell:Simplification of scRNA-seq data by merging together similar cells

Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells.

Maintained by The package maintainer. Last updated 8 months ago.

software coarse-graining scrna-seq-analysis scrna-seq-data

27.8 match 72 stars 8.08 score 93 scripts

bioc

GeneTonic:Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries.

Maintained by Federico Marini. Last updated 2 months ago.

gui geneexpression software transcription transcriptomics visualization differentialexpression pathways reportwriting genesetenrichment annotation go shinyapps bioconductor bioconductor-package data-exploration data-visualization functional-enrichment-analysis gene-expression pathway-analysis reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

26.7 match 77 stars 8.28 score 37 scripts 1 dependents

bioc

SC3:Single-Cell Consensus Clustering

A tool for unsupervised clustering and analysis of single cell RNA-Seq data.

Maintained by Vladimir Kiselev. Last updated 5 months ago.

immunooncology singlecell software classification clustering dimensionreduction supportvectormachine rnaseq visualization transcriptomics datarepresentation gui differentialexpression transcription bioconductor-package human-cell-atlas single-cell-rna-seq openblas cpp

21.9 match 122 stars 10.09 score 374 scripts 1 dependents

bioc

GARS:GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.

Maintained by Mattia Chiesa. Last updated 5 months ago.

classification featureextraction clustering openjdk

43.2 match 5.00 score 2 scripts

bioc

ORFik:Open Reading Frames in Genomics

R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more.

Maintained by Haakon Tjeldnes. Last updated 26 days ago.

immunooncology software sequencing riboseq rnaseq functionalgenomics coverage alignment dataimport cpp

19.9 match 33 stars 10.63 score 115 scripts 2 dependents

insightsengineering

teal.modules.hermes:RNA-Seq Analysis Modules to Add to a Teal Application

RNA-seq analysis teal modules based on the `hermes` package.

Maintained by Daniel Sabanés Bové. Last updated 1 years ago.

modules rna-seq-analysis shiny

38.0 match 7 stars 5.54 score 32 scripts

bioc

PROPER:PROspective Power Evaluation for RNAseq

This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data.

Maintained by Hao Wu. Last updated 5 months ago.

immunooncology sequencing rnaseq differentialexpression

41.1 match 5.08 score 20 scripts 1 dependents

bioc

SPsimSeq:Semi-parametric simulation tool for bulk and single-cell RNA sequencing data

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

Maintained by Joris Meys. Last updated 5 months ago.

geneexpression rnaseq singlecell sequencing dnaseq

28.4 match 10 stars 7.14 score 29 scripts 1 dependents

bioc

GEOquery:Get data from NCBI Gene Expression Omnibus (GEO)

The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor.

Maintained by Sean Davis. Last updated 5 months ago.

microarray dataimport onechannel twochannel sage bioconductor bioinformatics data-science genomics ncbi-geo

13.8 match 92 stars 14.46 score 4.1k scripts 44 dependents

bioc

decoupleR:decoupleR: Ensemble of computational methods to infer biological activities from omics data

Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase.

Maintained by Pau Badia-i-Mompel. Last updated 5 months ago.

differentialexpression functionalgenomics geneexpression generegulation network software statisticalmethod transcription

17.5 match 230 stars 11.27 score 316 scripts 3 dependents

bioc

maSigPro:Significant Gene Expression Profile Differences in Time Course Gene Expression Data

maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.

Maintained by Maria Jose Nueda. Last updated 5 months ago.

microarray rna-seq differential expression timecourse

38.0 match 5.18 score 76 scripts

bioc

ideal:Interactive Differential Expression AnaLysis

This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology geneexpression differentialexpression rnaseq sequencing visualization qualitycontrol gui genesetenrichment reportwriting shinyapps bioconductor differential-expression reproducible-research rna-seq rna-seq-analysis shiny user-friendly

28.8 match 29 stars 6.78 score 5 scripts

bioc

DEGreport:Report of DEG analysis

Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.

Maintained by Lorena Pantano. Last updated 5 months ago.

differentialexpression visualization rnaseq reportwriting geneexpression immunooncology bioconductor differential-expression qc report rna-seq smallrna

20.7 match 24 stars 9.42 score 354 scripts 1 dependents

bioc

dearseq:Differential Expression Analysis for RNA-seq data through a robust variance component test

Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093.

Maintained by Boris P. Hejblum. Last updated 5 months ago.

biomedicalinformatics cellbiology differentialexpression dnaseq geneexpression genetics genesetenrichment immunooncology kegg regression rnaseq sequencing systemsbiology timecourse transcription transcriptomics

30.8 match 8 stars 6.20 score 11 scripts 1 dependents

stemangiola

tidyseurat:Brings Seurat to the Tidyverse

It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.

Maintained by Stefano Mangiola. Last updated 8 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics dplyr ggplot2 pca purrr sct seurat single-cell single-cell-rna-seq tibble tidyr tidyverse transcripts tsne umap

19.0 match 158 stars 9.66 score 398 scripts 1 dependents

bioc

Linnorm:Linear model and normality based normalization and transformation method (Linnorm)

Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM.

Maintained by Shun Hang Yip. Last updated 5 months ago.

immunooncology sequencing chipseq rnaseq differentialexpression geneexpression genetics normalization software transcription batcheffect peakdetection clustering network singlecell cpp

29.3 match 6.26 score 61 scripts 5 dependents

mohuangx

SAVER:Single-Cell RNA-Seq Gene Expression Recovery

An implementation of a regularized regression prediction and empirical Bayes method to recover the true gene expression profile in noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018) <doi:10.1038/s41592-018-0033-z> for more details.

Maintained by Mo Huang. Last updated 4 months ago.

20.3 match 110 stars 8.88 score 231 scripts 2 dependents

samuel-marsh

scCustomize:Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing

Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.

Maintained by Samuel Marsh. Last updated 3 months ago.

customization ggplot2 scrna-seq seurat single-cell single-cell-genomics single-cell-rna-seq visualization

20.5 match 242 stars 8.75 score 1.1k scripts

bioc

RAIDS:Accurate Inference of Genetic Ancestry from Cancer Sequences

This package implements specialized algorithms that enable genetic ancestry inference from various cancer sequences sources (RNA, Exome and Whole-Genome sequences). This package also implements a simulation algorithm that generates synthetic cancer-derived data. This code and analysis pipeline was designed and developed for the following publication: Belleau, P et al. Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms. Cancer Res 1 January 2023; 83 (1): 49–58.

Maintained by Pascal Belleau. Last updated 5 months ago.

genetics software sequencing wholegenome principalcomponent geneticvariability dimensionreduction biocviews ancestry cancer-genomics exome-sequencing genomics inference r-language rna-seq rna-sequencing whole-genome-sequencing

28.2 match 5 stars 6.23 score 19 scripts

bioc

APAlyzer:A toolkit for APA analysis using RNA-seq data

Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data.

Maintained by Ruijia Wang. Last updated 5 months ago.

sequencing rnaseq differentialexpression geneexpression generegulation annotation dataimport software ative-polyadenylation bioinformatics-tool rna-seq

29.9 match 8 stars 5.81 score 9 scripts

bioc

SingleR:Reference-Based Single-Cell RNA-Seq Annotation

Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

Maintained by Aaron Lun. Last updated 27 days ago.

software singlecell geneexpression transcriptomics classification clustering annotation bioconductor singler cpp

13.7 match 182 stars 12.60 score 2.1k scripts 1 dependents

kharchenkolab

pagoda2:Single Cell Analysis and Differential Expression

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

21.5 match 222 stars 8.00 score 282 scripts

bioc

proActiv:Estimate Promoter Activity from RNA-Seq data

Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions.

Maintained by Joseph Lee. Last updated 5 months ago.

rnaseq geneexpression transcription alternativesplicing generegulation differentialsplicing functionalgenomics epigenetics transcriptomics preprocessing alternative-promoters genomics promoter-activity promoter-annotation rna-seq-data

25.8 match 51 stars 6.66 score 15 scripts

bioc

hermes:Preprocessing, analyzing, and reporting of RNA-seq data

Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available.

Maintained by Daniel Sabanés Bové. Last updated 5 months ago.

rnaseq differentialexpression normalization preprocessing qualitycontrol rna-seq statistical-engineering

21.9 match 11 stars 7.77 score 48 scripts 1 dependents

bioc

tidySingleCellExperiment:Brings SingleCellExperiment to the Tidyverse

'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks).

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression singlecell geneexpression normalization clustering qualitycontrol sequencing bioconductor dplyr ggplot2 plotly single-cell-rna-seq single-cell-sequencing singlecellexperiment tibble tidyr tidyverse

19.0 match 36 stars 8.86 score 125 scripts 2 dependents

bioc

infercnv:Infer Copy Number Variation from Single-Cell RNA-Seq Data

Using single-cell RNA-Seq expression to visualize CNV in cells.

Maintained by Christophe Georgescu. Last updated 5 months ago.

software copynumbervariation variantdetection structuralvariation genomicvariation genetics transcriptomics statisticalmethod bayesian hiddenmarkovmodel singlecell jags cpp

15.4 match 595 stars 10.91 score 674 scripts

person-c

easybio:Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit

Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.

Maintained by Wei Cui. Last updated 12 days ago.

limma geoquery edger fgsea bioinformatics cellmarker2 gsea rna-seq single-cell

25.2 match 10 stars 6.62 score 35 scripts

kharchenkolab

conos:Clustering on Network of Samples

Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

batch-correction scrna-seq single-cell-rna-seq openblas cpp openmp

22.6 match 204 stars 7.32 score 258 scripts

bioc

EDASeq:Exploratory Data Analysis and Normalization for RNA-Seq

Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010).

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology sequencing rnaseq preprocessing qualitycontrol differentialexpression

16.0 match 5 stars 10.24 score 594 scripts 9 dependents

ruzhangzhao

mixhvg:Mixture of Multiple Highly Variable Feature Selection Methods

Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>. Reference: <doi:10.1101/2024.08.25.608519>.

Maintained by Ruzhang Zhao. Last updated 13 days ago.

rna-seq-analysis rna-seq-pipeline single-cell single-cell-rna-seq variable-selection

39.2 match 5 stars 4.18 score 6 scripts

rezakj

iCellR:Analyzing High-Throughput Single Cell Sequencing Data

A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.

Maintained by Alireza Khodadadi-Jamayran. Last updated 8 months ago.

10xgenomics 3d batch-normalization cell-type-classification cite-seq clustering clustering-algorithm diffusion-maps dropout icellr imputation intractive-graph normalization pseudotime scrna-seq scvdj-seq singel-cell-sequencing umap cpp

29.1 match 121 stars 5.56 score 7 scripts 1 dependents

bioc

RUVSeq:Remove Unwanted Variation from RNA-Seq Data

This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology differentialexpression preprocessing rnaseq software

16.1 match 13 stars 9.90 score 482 scripts 5 dependents

bioc

pram:Pooling RNA-seq datasets for assembling transcript models

Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models.

Maintained by Peng Liu. Last updated 5 months ago.

software technology sequencing rnaseq biologicalquestion geneprediction genomeannotation researchfield transcriptomics bioconductor-package genome-annotation rna-seq transcript-model

37.9 match 1 stars 4.18 score 3 scripts

alexisvdb

singleCellHaystack:A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data

One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.

Maintained by Alexis Vandenbon. Last updated 1 years ago.

bioinformatics cite-seq pseudotime scatac-seq single-cell spatial-proteomics spatial-transcriptomics transcriptomics

23.5 match 81 stars 6.71 score 64 scripts

bioc

ERSSA:Empirical RNA-seq Sample Size Analysis

The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset.

Maintained by Zixuan Shao. Last updated 5 months ago.

immunooncology geneexpression transcription differentialexpression rnaseq multiplecomparison qualitycontrol

34.8 match 4.48 score 1 scripts

dosorio

rPanglaoDB:Download and Merge Single-Cell RNA-Seq Data from the PanglaoDB Database

Download and merge labeled single-cell RNA-seq data from the PanglaoDB <https://panglaodb.se/> into a Seurat object.

Maintained by Daniel Osorio. Last updated 2 years ago.

data-integration data-mining rna-seq single-cell single-cell-rna-seq

34.1 match 26 stars 4.41 score 20 scripts

bioc

DESeq2:Differential gene expression analysis based on the negative binomial distribution

Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

Maintained by Michael Love. Last updated 10 days ago.

sequencing rnaseq chipseq geneexpression transcription normalization differentialexpression bayesian regression principalcomponent clustering immunooncology openblas cpp

9.3 match 375 stars 16.11 score 17k scripts 115 dependents

bioc

scBubbletree:Quantitative visual exploration of scRNA-seq data

scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation.

Maintained by Simo Kitanovski. Last updated 5 months ago.

visualization clustering singlecell transcriptomics rnaseq big-data bigdata scrna-seq scrna-seq-analysis visual visual-exploration

25.6 match 6 stars 5.82 score 8 scripts

bioc

pathlinkR:Analyze and interpret RNA-Seq results

pathlinkR is an R package designed to facilitate analysis of RNA-Seq results. Specifically, our aim with pathlinkR was to provide a number of tools which take a list of DE genes and perform different analyses on them, aiding with the interpretation of results. Functions are included to perform pathway enrichment, with muliplte databases supported, and tools for visualizing these results. Genes can also be used to create and plot protein-protein interaction networks, all from inside of R.

Maintained by Travis Blimkie. Last updated 3 months ago.

genesetenrichment network pathways reactome rnaseq networkenrichment bioinformatics networks pathway-enrichment-analysis visualization

22.1 match 26 stars 6.62 score 2 scripts

waldronlab

SingleCellMultiModal:Integrating Multi-modal Single Cell Experiment datasets

SingleCellMultiModal is an ExperimentHub package that serves multiple datasets obtained from GEO and other sources and represents them as MultiAssayExperiment objects. We provide several multi-modal datasets including scNMT, 10X Multiome, seqFISH, CITEseq, SCoPE2, and others. The scope of the package is is to provide data for benchmarking and analysis. To cite, use the 'citation' function and see <https://doi.org/10.1371/journal.pcbi.1011324>.

Maintained by Marcel Ramos. Last updated 4 months ago.

experimentdata singlecelldata reproducibleresearch experimenthub geo bioconductor-package u24ca289073

19.5 match 17 stars 7.29 score 60 scripts

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 4 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

10.3 match 13.81 score 16k scripts 585 dependents

bioc

TREG:Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data

RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data.

Maintained by Louise Huuki-Myers. Last updated 3 months ago.

software singlecell rnaseq geneexpression transcriptomics transcription sequencing bioconductor deconvolution rnascope scrna-seq smfish snrna-seq treg

27.0 match 4 stars 5.20 score 5 scripts

bioc

zellkonverter:Conversion Between scRNA-seq Objects

Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk.

Maintained by Luke Zappia. Last updated 6 days ago.

singlecell dataimport datarepresentation bioconductor conversion scrna-seq

12.4 match 159 stars 11.25 score 660 scripts 4 dependents

zjufanlab

scCATCH:Single Cell Cluster-Based Annotation Toolkit for Cellular Heterogeneity

An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.

Maintained by Xin Shao. Last updated 2 years ago.

cell-markers cluster-annotation marker-genes rna-seq sequencing seurat single-cell transcriptome transcriptomics

19.5 match 225 stars 7.13 score 75 scripts

bioc

enrichViewNet:From functional enrichment results to biological networks

This package enables the visualization of functional enrichment results as network graphs. First the package enables the visualization of enrichment results, in a format corresponding to the one generated by gprofiler2, as a customizable Cytoscape network. In those networks, both gene datasets (GO terms/pathways/protein complexes) and genes associated to the datasets are represented as nodes. While the edges connect each gene to its dataset(s). The package also provides the option to create enrichment maps from functional enrichment results. Enrichment maps enable the visualization of enriched terms into a network with edges connecting overlapping genes.

Maintained by Astrid Deschênes. Last updated 5 months ago.

biologicalquestion software network networkenrichment go cystocape functional-enrichment

24.7 match 5 stars 5.54 score 6 scripts

bioc

monocle:Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq

Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well.

Maintained by Cole Trapnell. Last updated 5 months ago.

immunooncology sequencing rnaseq geneexpression differentialexpression infrastructure dataimport datarepresentation visualization clustering multiplecomparison qualitycontrol cpp

15.2 match 8.89 score 1.6k scripts 2 dependents

bioc

cqn:Conditional quantile normalization

A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method.

Maintained by Kasper Daniel Hansen. Last updated 5 months ago.

immunooncology rnaseq preprocessing differentialexpression

19.5 match 6.93 score 238 scripts 4 dependents

liuy12

SCdeconR:Deconvolution of Bulk RNA-Seq Data using Single-Cell RNA-Seq Data as Reference

Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions.

Maintained by Yuanhang Liu. Last updated 10 months ago.

bulk-rna-seq-deconvolution deconvolution differential-expression ffpe geneset-enrichment-analysis scdeconr single-cell

35.5 match 3 stars 3.78 score 4 scripts

bioc

getDEE2:Programmatic access to the DEE2 RNA expression dataset

Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022).

Maintained by Mark Ziemann. Last updated 2 months ago.

geneexpression transcriptomics sequencing bioinformatics data-mining genomics rna-expression rna-seq

31.5 match 4 stars 4.20 score 5 scripts

bioc

INSPEcT:Modeling RNA synthesis, processing and degradation with RNA-seq data

INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data.

Maintained by Stefano de Pretis. Last updated 5 months ago.

sequencing rnaseq generegulation timecourse systemsbiology

29.9 match 4.38 score 9 scripts

bioc

yarn:YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization

Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments.

Maintained by Joseph N Paulson. Last updated 5 months ago.

software qualitycontrol geneexpression sequencing preprocessing normalization annotation visualization clustering

29.2 match 4.49 score 31 scripts

bioc

QuasR:Quantify and Annotate Short Reads in R

This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files.

Maintained by Michael Stadler. Last updated 22 days ago.

genetics preprocessing sequencing chipseq rnaseq methylseq coverage alignment qualitycontrol immunooncology curl bzip2 xz-utils zlib cpp

14.3 match 6 stars 8.70 score 79 scripts 1 dependents

bioc

psichomics:Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included.

Maintained by Nuno Saraiva-Agostinho. Last updated 5 months ago.

sequencing rnaseq alternativesplicing differentialsplicing transcription gui principalcomponent survival biomedicalinformatics transcriptomics immunooncology visualization multiplecomparison geneexpression differentialexpression alternative-splicing bioconductor data-analyses differential-gene-expression differential-splicing-analysis gene-expression gtex recount2 rna-seq-data splicing-quantification sra tcga vast-tools cpp

17.9 match 36 stars 6.95 score 31 scripts

bioc

scDiagnostics:Cell type annotation diagnostics

The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes.

Maintained by Anthony Christidis. Last updated 5 months ago.

annotation classification clustering geneexpression rnaseq singlecell software transcriptomics

15.9 match 8 stars 7.77 score 46 scripts

bioc

DSS:Dispersion shrinkage for sequencing data

DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions.

Maintained by Hao Wu. Last updated 5 months ago.

sequencing rnaseq dnamethylation geneexpression differentialexpression differentialmethylation

17.5 match 7.02 score 248 scripts 5 dependents

bioc

Nebulosa:Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation

This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa.

Maintained by Jose Alquicira-Hernandez. Last updated 5 months ago.

software geneexpression singlecell visualization dimensionreduction single-cell single-cell-analysis single-cell-multiomics single-cell-rna-seq

12.5 match 99 stars 9.66 score 494 scripts

mathewchamberlain

SignacX:Cell Type Identification and Discovery from Single Cell Gene Expression Data

An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.

Maintained by Mathew Chamberlain. Last updated 2 years ago.

cellular-phenotypes seurat single-cell-rna-seq

18.7 match 24 stars 6.46 score 34 scripts

bioc

scQTLtools:An R package for single-cell eQTL analysis and visualization

This package specializes in analyzing and visualizing eQTL at the single-cell level. It can read gene expression matrices or Seurat data, or SingleCellExperiment object along with genotype data. It offers a function for cis-eQTL analysis to detect eQTL within a given range, and another function to fit models with three methods. Using this package, users can also generate single-cell level visualization result.

Maintained by Xiaofeng Wu. Last updated 2 months ago.

software geneexpression geneticvariability snp differentialexpression genomicvariation variantdetection genetics functionalgenomics systemsbiology regression singlecell normalization visualization rna-seq sc-eqtl

24.3 match 3 stars 4.95 score

kharchenkolab

numbat:Haplotype-Aware CNV Analysis from scRNA-Seq

A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.

Maintained by Teng Gao. Last updated 15 days ago.

cancer-genomics cnv-detection lineage-tracing phylogeny single-cell single-cell-analysis single-cell-rna-seq spatial-transcriptomics cpp

15.9 match 179 stars 7.41 score 120 scripts

bioc

GeneOverlap:Test and visualize gene overlaps

Test two sets of gene lists and visualize the results.

Maintained by António Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics. Last updated 5 months ago.

multiplecomparison visualization

18.3 match 6.43 score 266 scripts

bioc

scruff:Single Cell RNA-Seq UMI Filtering Facilitator (scruff)

A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics.

Maintained by Zhe Wang. Last updated 5 months ago.

software technology sequencing alignment rnaseq singlecell workflowstep preprocessing qualitycontrol visualization immunooncology bioinformatics scrna-seq single-cell umi

18.9 match 8 stars 6.20 score 22 scripts

bioc

EWCE:Expression Weighted Celltype Enrichment

Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.

Maintained by Alan Murphy. Last updated 29 days ago.

geneexpression transcription differentialexpression genesetenrichment genetics microarray mrnamicroarray onechannel rnaseq biomedicalinformatics proteomics visualization functionalgenomics singlecell deconvolution single-cell single-cell-rna-seq transcriptomics

12.5 match 55 stars 9.28 score 99 scripts

bioc

GSVA:Gene Set Variation Analysis for Microarray and RNA-Seq Data

Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner.

Maintained by Robert Castelo. Last updated 3 days ago.

functionalgenomics microarray rnaseq pathways genesetenrichment gene-set-enrichment genomics pathway-enrichment-analysis

7.9 match 210 stars 14.72 score 1.6k scripts 19 dependents

bioc

scMultiSim:Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

Maintained by Hechen Li. Last updated 5 months ago.

singlecell transcriptomics geneexpression sequencing experimentaldesign

16.1 match 23 stars 7.15 score 11 scripts

simonlabcode

bakR:Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets

Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.

Maintained by Isaac Vock. Last updated 4 months ago.

cpp

18.7 match 6 stars 6.12 score 21 scripts

bioc

netZooR:Unified methods for the inference and analysis of gene regulatory networks

netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.

Maintained by Tara Eicher. Last updated 8 days ago.

networkinference network generegulation geneexpression transcription microarray graphandnetwork gene-regulatory-network transcription-factors

14.0 match 105 stars 7.98 score

bioc

HybridExpress:Comparative analysis of RNA-seq data for hybrids and their progenitors

HybridExpress can be used to perform comparative transcriptomics analysis of hybrids (or allopolyploids) relative to their progenitor species. The package features functions to perform exploratory analyses of sample grouping, identify differentially expressed genes in hybrids relative to their progenitors, classify genes in expression categories (N = 12) and classes (N = 5), and perform functional analyses. We also provide users with graphical functions for the seamless creation of publication-ready figures that are commonly used in the literature.

Maintained by Fabricio Almeida-Silva. Last updated 5 months ago.

software functionalgenomics geneexpression transcriptomics rnaseq classification differentialexpression gene-expression hybrid polyploidy rna-seq

20.7 match 12 stars 5.38 score 2 scripts

bioc

RNAAgeCalc:A multi-tissue transcriptional age calculator

It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data.

Maintained by Xu Ren. Last updated 5 months ago.

rnaseq geneexpression biological-age elastic-net gene-expression genotype-tissue-expression prediction regularized-regression rna-seq

21.4 match 8 stars 5.20 score 10 scripts

bioc

velociraptor:Toolkit for Single-Cell Velocity

This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

singlecell geneexpression sequencing coverage rna-velocity

13.8 match 54 stars 8.06 score 52 scripts

bioc

tidySummarizedExperiment:Brings SummarizedExperiment to the Tidyverse

The tidySummarizedExperiment package provides a set of tools for creating and manipulating tidy data representations of SummarizedExperiment objects. SummarizedExperiment is a widely used data structure in bioinformatics for storing high-throughput genomic data, such as gene expression or DNA sequencing data. The tidySummarizedExperiment package introduces a tidy framework for working with SummarizedExperiment objects. It allows users to convert their data into a tidy format, where each observation is a row and each variable is a column. This tidy representation simplifies data manipulation, integration with other tidyverse packages, and enables seamless integration with the broader ecosystem of tidy tools for data analysis.

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics

13.1 match 26 stars 8.44 score 196 scripts 1 dependents

bioc

DaMiRseq:Data Mining for RNA-seq data: normalization, feature selection and classification

The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.

Maintained by Mattia Chiesa. Last updated 5 months ago.

sequencing rnaseq classification immunooncology openjdk

20.8 match 5.32 score 7 scripts 1 dependents

bioc

vidger:Create rapid visualizations of RNAseq data in R

The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR.

Maintained by Brandon Monier. Last updated 5 months ago.

immunooncology visualization rnaseq differentialexpression geneexpression data-munging differential-expression gene-expression rna-seq-analysis

16.5 match 19 stars 6.61 score 27 scripts

enblacar

SCpubr:Generate Publication Ready Visualizations of Single Cell Transcriptomics Data

A system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a “publication ready” format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.

Maintained by Enrique Blanco-Carmona. Last updated 30 days ago.

software singlecell visualization data-visualization ggplot2 publication-quality-plots seurat single-cell single-cell-genomics single-cell-rna-seq

12.5 match 178 stars 8.71 score 194 scripts

bioc

GenomicSuperSignature:Interpretation of RNA-seq experiments through robust, efficient comparison to public databases

This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package.

Maintained by Sehyun Oh. Last updated 5 months ago.

transcriptomics systemsbiology principalcomponent rnaseq sequencing pathways clustering bioconductor-package exploratory-data-analysis gsea mesh principal-component-analysis rna-sequencing-profiles transferlearning

15.6 match 16 stars 6.97 score 59 scripts

bioc

SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq sequencing geneexpression spatial singlecell transcriptomics preprocessing rna-seq spatial-transcriptomics

16.8 match 28 stars 6.48 score 36 scripts

katrionagoldmann

volcano3D:3D Volcano Plots and Polar Plots for Three-Class Data

Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.

Maintained by Katriona Goldmann. Last updated 2 years ago.

bioinformatics differential-expression differential-expression-analysis gene-expression interactive omics plotly rna-seq transcriptomics volcanoplots

18.3 match 36 stars 5.90 score 37 scripts

bioc

splatter:Simple Simulation of Single-cell RNA Sequencing Data

Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.

Maintained by Luke Zappia. Last updated 4 months ago.

singlecell rnaseq transcriptomics geneexpression sequencing software immunooncology bioconductor bioinformatics scrna-seq simulation

10.9 match 224 stars 9.92 score 424 scripts 1 dependents

bioc

InPAS:Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data

Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites.

Maintained by Jianhong Ou. Last updated 2 months ago.

alternative polyadenylation differential polyadenylation site usage rna-seq gene regulation transcription

24.9 match 4.30 score 1 scripts

ranbi1990

ssizeRNA:Sample Size Calculation for RNA-Seq Experimental Design

We propose a procedure for sample size calculation while controlling false discovery rate for RNA-seq experimental design. Our procedure depends on the Voom method proposed for RNA-seq data analysis by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size calculation method proposed for microarray experiments by Liu and Hwang (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions that calculates appropriate sample sizes for two-sample t-test for RNA-seq experiments with fixed or varied set of parameters. The outputs also contain a plot of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. To install this package, please use 'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. For R version 3.5 or greater, please use 'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.

Maintained by Ran Bi. Last updated 6 years ago.

geneexpression differentialexpression experimentaldesign sequencing rnaseq dnaseq microarray

29.9 match 1 stars 3.53 score 28 scripts 1 dependents

frederikziebell

RNAseqQC:Quality Control for RNA-Seq Data

Functions for semi-automated quality control of bulk RNA-seq data.

Maintained by Frederik Ziebell. Last updated 8 months ago.

20.1 match 2 stars 5.21 score 27 scripts

bioc

TCC:TCC: Differential expression analysis for tag count data with robust normalization strategies

This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.

Maintained by Jianqiang Sun. Last updated 5 months ago.

immunooncology sequencing differentialexpression rnaseq

21.3 match 4.91 score 41 scripts

bioc

TFEA.ChIP:Analyze Transcription Factor Enrichment

Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments.

Maintained by Laura Puente Santamaría. Last updated 5 months ago.

transcription generegulation genesetenrichment transcriptomics sequencing chipseq rnaseq immunooncology

30.3 match 3.45 score 14 scripts

bioc

MultiRNAflow:An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions

Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

Maintained by Rodolphe Loubaton. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription timecourse preprocessing visualization normalization principalcomponent clustering differentialexpression genesetenrichment pathways

19.7 match 6 stars 5.26 score 4 scripts

bioc

EventPointer:An effective identification of alternative splicing events using junction arrays and RNA-Seq data

EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation.

Maintained by Juan A. Ferrer-Bonsoms. Last updated 5 months ago.

alternativesplicing differentialsplicing mrnamicroarray rnaseq transcription sequencing timecourse immunooncology

17.2 match 4 stars 6.00 score 6 scripts

immunogenomics

harmony:Fast, Sensitive, and Accurate Integration of Single Cell Data

Implementation of the Harmony algorithm for single cell integration, described in Korsunsky et al <doi:10.1038/s41592-019-0619-0>. Package includes a standalone Harmony function and interfaces to external frameworks.

Maintained by Ilya Korsunsky. Last updated 4 months ago.

algorithm data-integration scrna-seq openblas cpp

7.5 match 554 stars 13.74 score 5.5k scripts 8 dependents

bioc

GEOfastq:Downloads ENA Fastqs With GEO Accessions

GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA.

Maintained by Alex Pickering. Last updated 5 months ago.

rnaseq dataimport bioinformatics fastq gene-expression geo rna-seq

21.9 match 4 stars 4.60 score 6 scripts

aalhendi1707

countToFPKM:Convert Counts to Fragments per Kilobase of Transcript per Million (FPKM)

Implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using 'biomaRt' package, and the mean fragment lengths which can be calculated using the 'CollectInsertSizeMetrics(Picard)' tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.

Maintained by Ahmed Alhendi. Last updated 4 years ago.

gene-expression normalization rna-seq

19.7 match 62 stars 5.09 score 20 scripts

dcgerard

seqgendiff:RNA-Seq Generation/Modification for Simulation

Generates/modifies RNA-seq data for use in simulations. We provide a suite of functions that will add a known amount of signal to a real RNA-seq dataset. The advantage of using this approach over simulating under a theoretical distribution is that common/annoying aspects of the data are more preserved, giving a more realistic evaluation of your method. The main functions are select_counts(), thin_diff(), thin_lib(), thin_gene(), thin_2group(), thin_all(), and effective_cor(). See Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the implemented methods.

Maintained by David Gerard. Last updated 10 months ago.

16.9 match 10 stars 5.86 score 72 scripts

martinloza

Canek:Batch Correction of Single Cell Transcriptome Data

Non-linear/linear hybrid method for batch-effect correction that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.

Maintained by Martin Loza. Last updated 1 years ago.

batch-effects bioinformatics single-cell-rna-seq transcriptomics

19.3 match 5 stars 5.06 score 23 scripts

bioc

peco:A Supervised Approach for Predicting cell Cycle Progression using scRNA-seq data

Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data.

Maintained by Chiaowen Joyce Hsiao. Last updated 5 months ago.

sequencing rnaseq geneexpression transcriptomics singlecell software statisticalmethod classification visualization cell-cycle single-cell-rna-seq

15.9 match 12 stars 6.09 score 34 scripts

bioc

DEXSeq:Inference of differential exon usage in RNA-Seq

The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

Maintained by Alejandro Reyes. Last updated 15 days ago.

immunooncology sequencing rnaseq differentialexpression alternativesplicing differentialsplicing geneexpression visualization

12.4 match 7.75 score 330 scripts 6 dependents

bioc

scone:Single Cell Overview of Normalized Expression data

SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

Maintained by Davide Risso. Last updated 24 days ago.

immunooncology normalization preprocessing qualitycontrol geneexpression rnaseq software transcriptomics sequencing singlecell coverage

10.5 match 53 stars 9.12 score 104 scripts

bioc

GRaNIE:GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data

Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.

Maintained by Christian Arnold. Last updated 5 months ago.

software geneexpression generegulation networkinference genesetenrichment biomedicalinformatics genetics transcriptomics atacseq rnaseq graphandnetwork regression transcription chipseq

17.5 match 5.40 score 24 scripts

bioc

TAPseq:Targeted scRNA-seq primer design for TAP-seq

Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn.

Maintained by Andreas R. Gschwind. Last updated 5 months ago.

singlecell sequencing technology crispr pooledscreens

17.5 match 4 stars 5.38 score 9 scripts

diystat

NBPSeq:Negative Binomial Models for RNA-Sequencing Data

Negative Binomial (NB) models for two-group comparisons and regression inferences from RNA-Sequencing Data.

Maintained by Yanming Di. Last updated 11 years ago.

19.1 match 1 stars 4.88 score 17 scripts 3 dependents

jgasmits

AnanseSeurat:Construct ANANSE GRN-Analysis Seurat

Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' implemented in 'snakemake'. Finally, incorporate results for visualization and interpretation.

Maintained by Jos Smits. Last updated 1 years ago.

grn-analysis seurat-objects single-cell single-cell-atac-seq single-cell-rna-seq

18.8 match 8 stars 4.90 score 4 scripts

hugheylab

seeker:Simplified Fetching and Processing of Microarray and RNA-Seq Data

Wrapper around various existing tools and command-line interfaces, providing a standard interface, simple parallelization, and detailed logging. For microarray data, maps probe sets to standard gene IDs, building on 'GEOquery' Davis and Meltzer (2007) <doi:10.1093/bioinformatics/btm254>, 'ArrayExpress' Kauffmann et al. (2009) <doi:10.1093/bioinformatics/btp354>, Robust multi-array average 'RMA' Irizarry et al. (2003) <doi:10.1093/biostatistics/4.2.249>, and 'BrainArray' Dai et al. (2005) <doi:10.1093/nar/gni179>. For RNA-seq data, fetches metadata and raw reads from National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA), performs standard adapter and quality trimming using 'TrimGalore' Krueger <https://github.com/FelixKrueger/TrimGalore>, performs quality control checks using 'FastQC' Andrews <https://github.com/s-andrews/FastQC>, quantifies transcript abundances using 'salmon' Patro et al. (2017) <doi:10.1038/nmeth.4197> and potentially 'refgenie' Stolarczyk et al. (2020) <doi:10.1093/gigascience/giz149>, aggregates the results using 'MultiQC' Ewels et al. (2016) <doi:10.1093/bioinformatics/btw354>, maps transcripts to genes using 'biomaRt' Durinkck et al. (2009) <doi:10.1038/nprot.2009.97>, and summarizes transcript-level quantifications for gene-level analyses using 'tximport' Soneson et al. (2015) <doi:10.12688/f1000research.7563.2>.

Maintained by Jake Hughey. Last updated 7 months ago.

19.2 match 3 stars 4.78 score 1 scripts

bioc

CircSeqAlignTk:A toolkit for end-to-end analysis of RNA-seq data for circular genomes

CircSeqAlignTk is designed for end-to-end RNA-Seq data analysis of circular genome sequences, from alignment to visualization. It mainly targets viroids which are composed of 246-401 nt circular RNAs. In addition, CircSeqAlignTk implements a tidy interface to generate synthetic sequencing data that mimic real RNA-Seq data, allowing developers to evaluate the performance of alignment tools and workflows.

Maintained by Jianqiang Sun. Last updated 5 months ago.

sequencing smallrna alignment software

20.5 match 4.40 score 3 scripts

bioc

pipeComp:pipeComp pipeline benchmarking framework

A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data.

Maintained by Pierre-Luc Germain. Last updated 5 months ago.

geneexpression transcriptomics clustering datarepresentation benchmark bioconductor pipeline-benchmarking pipelines single-cell-rna-seq

12.5 match 41 stars 7.02 score 43 scripts

bioc

MEB:A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq and scRNA-seq data

This package provides a method to identify differential expression genes in the same or different species. Given that non-DE genes have some similarities in features, a scaling-free minimum enclosing ball (SFMEB) model is built to cover those non-DE genes in feature space, then those DE genes, which are enormously different from non-DE genes, being regarded as outliers and rejected outside the ball. The method on this package is described in the article 'A minimum enclosing ball method to detect differential expression genes for RNA-seq data'. The SFMEB method is extended to the scMEB method that considering two or more potential types of cells or unknown labels scRNA-seq dataset DEGs identification.

Maintained by Jiadi Zhu. Last updated 5 months ago.

differentialexpression geneexpression normalization classification sequencing

18.3 match 4.78 score 1 scripts

bioc

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Maintained by Catalina Vallejos. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology bioconductor-package gene-expression rcpp rcpparmadillo scrna-seq single-cell openblas cpp openmp

8.5 match 83 stars 10.26 score 368 scripts 1 dependents

stemangiola

tidygate:Interactively Gate Points

Interactively gate points on a scatter plot. Interactively drawn gates are recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is based on the package gatepoints by Wajid Jawaid (who is also an author of this package).

Maintained by Stefano Mangiola. Last updated 6 months ago.

assaydomain infrastructure clustering datavis dataviz dplyr drawing facs gate ggplot2 interactive pipe programmatic seurat single-cell single-cell-rna-seq tibble tidy-data tidyverse

12.5 match 23 stars 6.89 score 14 scripts 1 dependents

bioc

SGSeq:Splice event prediction and quantification from RNA-seq data

SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation.

Maintained by Leonard Goldstein. Last updated 5 months ago.

alternativesplicing immunooncology rnaseq transcription

14.2 match 5.91 score 45 scripts 3 dependents

bioc

ggbio:Visualization tools for genomic data

The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries.

Maintained by Michael Lawrence. Last updated 5 months ago.

infrastructure visualization

6.8 match 111 stars 12.26 score 734 scripts 17 dependents

bioc

SplicingGraphs:Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them

This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways.

Maintained by H. Pagès. Last updated 5 months ago.

genetics annotation datarepresentation visualization sequencing rnaseq geneexpression alternativesplicing transcription immunooncology bioconductor-package

15.9 match 2 stars 5.26 score 8 scripts

bioc

DegNorm:DegNorm: degradation normalization for RNA-seq data

This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy.

Maintained by Ji-Ping Wang. Last updated 5 months ago.

rnaseq normalization geneexpression alignment coverage differentialexpression batcheffect software sequencing immunooncology qualitycontrol dataimport openblas cpp openmp

16.0 match 1 stars 5.20 score 3 scripts

bioc

mitch:Multi-Contrast Gene Set Enrichment Analysis

mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. We have an established routine for pathway enrichment of Infinium Methylation Array data (see vignette). The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments.

Maintained by Mark Ziemann. Last updated 4 months ago.

geneexpression genesetenrichment singlecell transcriptomics epigenetics proteomics differentialexpression reactome dnamethylation methylationarray gene-regulation gene-seq-analysis pathway-analysis

11.7 match 16 stars 7.06 score 15 scripts

bioc

kissDE:Retrieves Condition-Specific Variants in RNA-Seq Data

Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data.

Maintained by Aurélie Siberchicot. Last updated 5 months ago.

alternativesplicing differentialsplicing experimentaldesign genomicvariation rnaseq transcriptomics

13.6 match 3 stars 5.98 score 7 scripts

bioc

gDNAx:Diagnostics for assessing genomic DNA contamination in RNA-seq data

Provides diagnostics for assessing genomic DNA contamination in RNA-seq data, as well as plots representing these diagnostics. Moreover, the package can be used to get an insight into the strand library protocol used and, in case of strand-specific libraries, the strandedness of the data. Furthermore, it provides functionality to filter out reads of potential gDNA origin.

Maintained by Robert Castelo. Last updated 1 months ago.

transcription transcriptomics rnaseq sequencing preprocessing software geneexpression coverage differentialexpression functionalgenomics splicedalignment alignment

15.8 match 1 stars 5.08 score 3 scripts

bioc

roar:Identify differential APA usage from RNA-seq alignments

Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments.

Maintained by Elena Grassi. Last updated 5 months ago.

sequencing highthroughputsequencing rnaseq transcription

15.9 match 4 stars 5.05 score 14 scripts

tacazares

SeedMatchR:Find Matches to Canonical SiRNA Seeds in Genomic Features

On-target gene knockdown using siRNA ideally results from binding fully complementary regions in mRNA transcripts to induce cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ~8 nucleotides at the 5’ end of the guide strand, called a seed region, bind the 3’ untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with potential off-target effects driven by the seed region. 'SeedMatchR' provides tools for exploring and detecting potential seed-mediated off-target effects of siRNA in RNA-seq experiments. 'SeedMatchR' is designed to extend current differential expression analysis tools, such as 'DESeq2', by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of 'SeedMatchR' to detect cumulative changes in differential gene expression attributed to siRNA seed regions.

Maintained by Tareian Cazares. Last updated 1 years ago.

deseq2-analysis mirna rna-seq sirna transcriptomics

17.7 match 7 stars 4.54 score 7 scripts

bioc

granulator:Rapid benchmarking of methods for in silico deconvolution of bulk RNA-seq data

granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well.

Maintained by Sabina Pfister. Last updated 5 months ago.

rnaseq geneexpression differentialexpression transcriptomics singlecell statisticalmethod regression

17.9 match 3 stars 4.48 score 7 scripts

bioc

fgsea:Fast Gene Set Enrichment Analysis

The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.

Maintained by Alexey Sergushichev. Last updated 3 months ago.

geneexpression differentialexpression genesetenrichment pathways cpp

4.9 match 387 stars 16.25 score 3.9k scripts 101 dependents

bioc

scRecover:scRecover for imputation of single-cell RNA-seq data

scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results.

Maintained by Zhun Miao. Last updated 5 months ago.

geneexpression singlecell rnaseq transcriptomics sequencing preprocessing software

15.3 match 8 stars 5.20 score 9 scripts

bioc

MuData:Serialization for MultiAssayExperiment Objects

Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.

Maintained by Ilia Kats. Last updated 19 days ago.

dataimport anndata bioconductor mudata multi-omics multimodal-omics scrna-seq

13.5 match 5 stars 5.89 score 26 scripts

bioc

broadSeq:broadSeq : for streamlined exploration of RNA-seq data

This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.

Maintained by Rishi Das Roy. Last updated 5 months ago.

geneexpression differentialexpression rnaseq transcriptomics sequencing coverage genesetenrichment go

16.2 match 2 stars 4.90 score 7 scripts

bioc

NeuCA:NEUral network-based single-Cell Annotation tool

NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types.

Maintained by Hao Feng. Last updated 5 months ago.

singlecell software classification neuralnetwork rnaseq transcriptomics datarepresentation transcription sequencing preprocessing geneexpression dataimport

23.9 match 3.30 score 3 scripts

bioc

derfinder:Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach

This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks.

Maintained by Leonardo Collado-Torres. Last updated 3 months ago.

differentialexpression sequencing rnaseq chipseq differentialpeakcalling software immunooncology coverage annotation-agnostic bioconductor derfinder

7.9 match 42 stars 10.03 score 78 scripts 6 dependents

bioc

scds:In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data

In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally.

Maintained by Dennis Kostka. Last updated 5 months ago.

singlecell rnaseq qualitycontrol preprocessing transcriptomics geneexpression sequencing software classification

12.0 match 6.57 score 176 scripts 1 dependents

bioc

Rsubread:Mapping, quantification and variant analysis of sequencing data

Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads.

Maintained by Wei Shi. Last updated 14 hours ago.

sequencing alignment sequencematching rnaseq chipseq singlecell geneexpression generegulation genetics immunooncology snp geneticvariability preprocessing qualitycontrol genomeannotation genefusiondetection indeldetection variantannotation variantdetection multiplesequencealignment zlib

8.5 match 9.24 score 892 scripts 10 dependents

bioc

SplicingFactory:Splicing Diversity Analysis for Transcriptome Data

The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions.

Maintained by Endre Sebestyen. Last updated 5 months ago.

transcriptomics rnaseq differentialsplicing alternativesplicing transcriptomevariant gini-index rna-seq shannon-entropy simpson-index splicing

15.0 match 4 stars 5.20 score 1 scripts

bioc

EBSeq:An R package for gene and isoform differential expression analysis of RNA-seq data

Differential Expression analysis at both gene and isoform level using RNA-seq data

Maintained by Xiuyu Ma. Last updated 2 months ago.

immunooncology statisticalmethod differentialexpression multiplecomparison rnaseq sequencing cpp

9.9 match 7.77 score 162 scripts 6 dependents

bioc

VaSP:Quantification and Visualization of Variations of Splicing in Population

Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.)

Maintained by Huihui Yu. Last updated 5 months ago.

rnaseq alternativesplicing differentialsplicing statisticalmethod visualization preprocessing clustering differentialexpression kegg immunooncology 3s-scores alternative-splicing ballgown rna-seq splicing sqtl statistics

16.0 match 3 stars 4.78 score 3 scripts

bioc

icetea:Integrating Cap Enrichment with Transcript Expression Analysis

icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis.

Maintained by Vivek Bhardwaj. Last updated 5 months ago.

immunooncology transcription geneexpression sequencing rnaseq transcriptomics differentialexpression cage expression rna-seq

15.0 match 2 stars 5.08 score 7 scripts

stephenslab

fastglmpca:Fast Algorithms for Generalized Principal Component Analysis

Implements fast, scalable optimization algorithms for fitting generalized principal components analysis (GLM-PCA) models, as described in "A Generalization of Principal Components Analysis to the Exponential Family" Collins M, Dasgupta S, Schapire RE (2002, ISBN:9780262271738), and subsequently "Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model" Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.

Maintained by Eric Weine. Last updated 3 days ago.

openblas cpp

13.2 match 11 stars 5.72 score 16 scripts

bioc

DRIMSeq:Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq

The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results.

Maintained by Malgorzata Nowicka. Last updated 5 months ago.

immunooncology snp alternativesplicing differentialsplicing genetics rnaseq sequencing workflowstep multiplecomparison geneexpression differentialexpression

11.0 match 6.91 score 136 scripts 2 dependents

eonurk

cinaR:A Computational Pipeline for Bulk 'ATAC-Seq' Profiles

Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data analyses. This package combines different packages to have an ultimate package for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 'Karakaslar et al.' (2021) <doi:10.1101/2021.03.05.434143>.

Maintained by Onur Karakaslar. Last updated 10 months ago.

atac-seq differential-analysis enrichment-analysis gene-sets

13.6 match 13 stars 5.52 score 51 scripts

vivianstats

MAAPER:Analysis of Alternative Polyadenylation Using 3' End-Linked Reads

A computational method developed for model-based analysis of alternative polyadenylation (APA) using 3' end-linked reads. It accurately assigns 3' RNA-seq reads to polyA sites through statistical modeling, and generates multiple statistics for APA analysis. Please also see Li WV, Zheng D, Wang R, Tian B (2021) <doi:10.1186/s13059-021-02429-5>.

Maintained by Wei Vivian Li. Last updated 4 years ago.

alternative-polyadenylation bioinformatics-tool rna-seq

16.0 match 9 stars 4.65 score 7 scripts

bioc

ViSEAGO:ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.

Maintained by Aurelien Brionne. Last updated 2 months ago.

software annotation go genesetenrichment multiplecomparison clustering visualization

11.2 match 6.64 score 22 scripts

bioc

SVMDO:Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology

It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets.

Maintained by Mustafa Erhan Ozer. Last updated 5 months ago.

genesetenrichment differentialexpression gui classification rnaseq transcriptomics survival machine-learning rna-seq shiny

16.0 match 4.60 score 2 scripts

bioc

RBM:RBM: a R package for microarray and RNA-Seq data analysis

Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets.

Maintained by Dongmei Li. Last updated 5 months ago.

microarray differentialexpression

16.7 match 4.41 score 13 scripts

bioc

mastR:Markers Automated Screening Tool in R

mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of group markers in various biological and medical applications, including cancer research and developmental biology.

Maintained by Jinjin Chen. Last updated 5 months ago.

software geneexpression transcriptomics differentialexpression visualization

14.4 match 4 stars 5.08 score 3 scripts

bioc

scRNAseqApp:A single-cell RNAseq Shiny app-package

The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.

Maintained by Jianhong Ou. Last updated 2 days ago.

visualization singlecell rnaseq interactive-visualizations multiple-users shiny-apps single-cell-rna-seq

12.5 match 4 stars 5.76 score 3 scripts

bioc

sva:Surrogate Variable Analysis

The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics).

Maintained by Jeffrey T. Leek. Last updated 5 months ago.

immunooncology microarray statisticalmethod preprocessing multiplecomparison sequencing rnaseq batcheffect normalization

7.1 match 10.05 score 3.2k scripts 50 dependents

bioc

CleanUpRNAseq:Detect and Correct Genomic DNA Contamination in RNA-seq Data

RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes.

Maintained by Haibo Liu. Last updated 4 months ago.

qualitycontrol sequencing geneexpression

13.1 match 5 stars 5.44 score 4 scripts

bioc

weitrix:Tools for matrices with precision weights, test and explore weighted or sparse data

Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported.

Maintained by Paul Harrison. Last updated 5 months ago.

software datarepresentation dimensionreduction geneexpression transcriptomics rnaseq singlecell regression

15.2 match 4.70 score 8 scripts

papatheodorou-group

scGOclust:Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq

Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.

Maintained by Yuyao Song. Last updated 1 years ago.

14.8 match 9 stars 4.80 score 14 scripts

jbengler

tidyplots:Tidy Plots for Scientific Papers

The goal of 'tidyplots' is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.

Maintained by Jan Broder Engler. Last updated 2 days ago.

7.5 match 482 stars 9.40 score 85 scripts

bioc

zinbwave:Zero-Inflated Negative Binomial Model for RNA-Seq Data

Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data.

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology dimensionreduction geneexpression rnaseq software transcriptomics sequencing singlecell

6.6 match 43 stars 10.53 score 190 scripts 6 dependents

felixthestudent

cellpypes:Cell Type Pipes for Single-Cell RNA Sequencing Data

Annotate single-cell RNA sequencing data manually based on marker gene thresholds. Find cell type rules (gene+threshold) through exploration, use the popular piping operator '%>%' to reconstruct complex cell type hierarchies. 'cellpypes' models technical noise to find positive and negative cells for a given expression threshold and returns cell type labels or pseudobulks. Cite this package as Frauhammer (2022) <doi:10.5281/zenodo.6555728> and visit <https://github.com/FelixTheStudent/cellpypes> for tutorials and newest features.

Maintained by Felix Frauhammer. Last updated 1 years ago.

celltype-annotation classification-algorithm scrnaseq single-cell-rna-seq

15.8 match 51 stars 4.41 score 8 scripts

rcannood

SCORPIUS:Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data

An accurate and easy tool for performing linear trajectory inference on single cells using single-cell RNA sequencing data. In addition, 'SCORPIUS' provides functions for discovering the most important genes with respect to the reconstructed trajectory, as well as nice visualisation tools. Cannoodt et al. (2016) <doi:10.1101/079509>.

Maintained by Robrecht Cannoodt. Last updated 2 years ago.

8.5 match 59 stars 8.17 score 126 scripts

pln-team

PLNmodels:Poisson Lognormal Models

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

Maintained by Julien Chiquet. Last updated 3 days ago.

count-data multivariate-analysis network-inference pca poisson-lognormal-model openblas cpp

7.2 match 56 stars 9.50 score 226 scripts

bioc

variancePartition:Quantify and interpret drivers of variation in multilevel gene expression experiments

Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures.

Maintained by Gabriel E. Hoffman. Last updated 2 months ago.

rnaseq geneexpression genesetenrichment differentialexpression batcheffect qualitycontrol regression epigenetics functionalgenomics transcriptomics normalization preprocessing microarray immunooncology software

5.8 match 7 stars 11.69 score 1.1k scripts 3 dependents

bioc

SimBu:Simulate Bulk RNA-seq Datasets from Single-Cell Datasets

SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.

Maintained by Alexander Dietrich. Last updated 5 months ago.

software rnaseq singlecell

9.9 match 14 stars 6.81 score 29 scripts

feiyoung

ProFAST:Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction

Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.

Maintained by Wei Liu. Last updated 1 months ago.

openblas cpp

11.3 match 2 stars 5.86 score 12 scripts 1 dependents

bioc

SeqGSEA:Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing

The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.

Maintained by Xi Wang. Last updated 5 months ago.

sequencing rnaseq genesetenrichment geneexpression differentialexpression differentialsplicing immunooncology

15.3 match 4.34 score 11 scripts

bioc

DropletUtils:Utilities for Handling Single-Cell Droplet Data

Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.

Maintained by Jonathan Griffiths. Last updated 3 months ago.

immunooncology singlecell sequencing rnaseq geneexpression transcriptomics dataimport coverage zlib cpp

6.6 match 10.08 score 2.7k scripts 9 dependents

bioc

speckle:Statistical methods for analysing single cell RNA-seq data

The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.

Maintained by Belinda Phipson. Last updated 5 months ago.

singlecell rnaseq regression geneexpression

12.2 match 5.41 score 258 scripts

bioc

MGFR:Marker Gene Finder in RNA-seq data

The package is designed to detect marker genes from RNA-seq data.

Maintained by Khadija El Amrani. Last updated 5 months ago.

immunooncology genetics geneexpression rnaseq

17.5 match 3.78 score 2 scripts 1 dependents

bioc

muscat:Multi-sample multi-group scRNA-seq data analysis tools

`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.

Maintained by Helena L. Crowell. Last updated 5 months ago.

immunooncology differentialexpression sequencing singlecell software statisticalmethod visualization

6.4 match 181 stars 10.26 score 686 scripts

bioc

scTensor:Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

dimensionreduction singlecell software geneexpression

15.6 match 4.18 score 2 scripts

bioc

goseq:Gene Ontology analyser for RNA-seq and other length biased data

Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data.

Maintained by Federico Marini. Last updated 5 months ago.

immunooncology sequencing go geneexpression transcription rnaseq differentialexpression annotation genesetenrichment kegg pathways software

6.7 match 1 stars 9.67 score 636 scripts 9 dependents

bioc

quantiseqr:Quantification of the Tumor Immune contexture from RNA-seq data

This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data.

Maintained by Federico Marini. Last updated 3 months ago.

geneexpression software transcription transcriptomics sequencing microarray visualization annotation immunooncology featureextraction classification statisticalmethod experimenthubsoftware flowcytometry

14.0 match 4.65 score 3 scripts 1 dependents

bioc

Oscope:Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq

Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group.

Maintained by Ning Leng. Last updated 5 months ago.

immunooncology statisticalmethod rnaseq sequencing geneexpression

13.2 match 4.92 score 14 scripts 1 dependents

scmethods

scregclust:Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data

Implementation of the scregclust algorithm described in Larsson, Held, et al. (2024) <doi:10.1038/s41467-024-53954-3> which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program.

Maintained by Felix Held. Last updated 2 months ago.

clustering regulatory-programs scrna-seq-analysis cpp openmp

10.0 match 9 stars 6.45 score 21 scripts

bioc

scBFA:A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.

Maintained by Ruoxin Li. Last updated 5 months ago.

singlecell transcriptomics dimensionreduction geneexpression atacseq batcheffect kegg qualitycontrol

14.9 match 4.30 score 4 scripts

bioc

sSeq:Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size

The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution.

Maintained by Danni Yu. Last updated 5 months ago.

immunooncology rnaseq

12.9 match 4.98 score 4 scripts 2 dependents

bioc

AUCell:AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures)

AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed.

Maintained by Gert Hulselmans. Last updated 5 months ago.

singlecell genesetenrichment transcriptomics transcription geneexpression workflowstep normalization

7.5 match 8.59 score 860 scripts 4 dependents

bioc

POWSC:Simulation, power evaluation, and sample size recommendation for single cell RNA-seq

Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.

Maintained by Kenong Su. Last updated 5 months ago.

differentialexpression immunooncology singlecell software

16.0 match 4.00 score 7 scripts

bioc

SiPSiC:Calculate Pathway Scores for Each Cell in scRNA-Seq Data

Infer biological pathway activity of cells from single-cell RNA-sequencing data by calculating a pathway score for each cell (pathway genes are specified by the user). It is recommended to have the data in Transcripts-Per-Million (TPM) or Counts-Per-Million (CPM) units for best results. Scores may change when adding cells to or removing cells off the data. SiPSiC stands for Single Pathway analysis in Single Cells.

Maintained by Daniel Davis. Last updated 5 months ago.

software differentialexpression genesetenrichment biomedicalinformatics cellbiology transcriptomics rnaseq singlecell transcription sequencing immunooncology dataimport

12.3 match 6 stars 5.18 score 3 scripts

bioc

cardelino:Clone Identification from Single Cell Data

Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used.

Maintained by Davis McCarthy. Last updated 5 months ago.

singlecell rnaseq visualization transcriptomics geneexpression sequencing software exomeseq clonal-clustering gibbs-sampling scrna-seq single-cell somatic-mutations

9.0 match 61 stars 7.05 score 62 scripts

bioc

srnadiff:Finding differentially expressed unannotated genomic regions from RNA-seq data

srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values.

Maintained by Zytnicki Matthias. Last updated 2 months ago.

immunooncology geneexpression coverage smallrna epigenetics statisticalmethod preprocessing differentialexpression cpp

17.2 match 3.70 score 3 scripts

bioc

DEsingle:DEsingle for detecting three types of differential expression in single-cell RNA-seq data

DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions.

Maintained by Zhun Miao. Last updated 5 months ago.

differentialexpression geneexpression singlecell immunooncology rnaseq transcriptomics sequencing preprocessing software

8.6 match 31 stars 7.42 score 28 scripts

bioc

scAnnotatR:Pretrained learning models for cell type prediction on single cell RNA-sequencing data

The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.

Maintained by Johannes Griss. Last updated 5 months ago.

singlecell transcriptomics geneexpression supportvectormachine classification software

9.4 match 15 stars 6.73 score 20 scripts

bioc

adverSCarial:adverSCarial, generate and analyze the vulnerability of scRNA-seq classifier to adversarial attacks

adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, single gene attack and max change attack. The single-gene attack involves making a small modification to the input to alter the classification. The max-change attack involves making a large modification to the input without changing its classification. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks.

Maintained by Ghislain FIEVET. Last updated 5 months ago.

software singlecell transcriptomics classification

11.2 match 5.42 score 19 scripts

yuelyu21

SCIntRuler:Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets

The accumulation of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. SCIntRuler, a novel R package, addresses these challenges by guiding the integration of multiple scRNA-seq datasets.

Maintained by Yue Lyu. Last updated 5 months ago.

sequencing geneticvariability singlecell cpp

12.4 match 2 stars 4.85 score 3 scripts

bioc

tweeDEseq:RNA-seq data analysis using the Poisson-Tweedie family of distributions

Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde.

Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.

immunooncology statisticalmethod differentialexpression sequencing rnaseq dnaseq

12.1 match 4.91 score 45 scripts 1 dependents

bioc

NewWave:Negative binomial model for scRNA-seq

A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.

Maintained by Federico Agostinis. Last updated 5 months ago.

software geneexpression transcriptomics singlecell batcheffect sequencing coverage regression batch-effects dimensionality-reduction negative-binomial scrna-seq

11.0 match 4 stars 5.33 score 27 scripts

bioc

ReportingTools:Tools for making reports in various formats

The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools.

Maintained by Jason A. Hackney. Last updated 5 months ago.

immunooncology software visualization microarray rnaseq go datarepresentation genesetenrichment

9.4 match 6.23 score 93 scripts 1 dependents

bioc

SCnorm:Normalization of single cell RNA-seq data

This package implements SCnorm — a method to normalize single-cell RNA-seq data.

Maintained by Rhonda Bacher. Last updated 5 months ago.

normalization rnaseq singlecell immunooncology

6.9 match 47 stars 8.46 score 76 scripts

bioc

scTGIF:Cell type annotation for unannotated single-cell RNA-Seq data

scTGIF connects the cells and the related gene functions without cell type label.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

dimensionreduction qualitycontrol singlecell software geneexpression

14.5 match 4.00 score 2 scripts

bioc

EnrichmentBrowser:Seamless navigation through combined results of set-based and network-based enrichment analysis

The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways.

Maintained by Ludwig Geistlinger. Last updated 5 months ago.

immunooncology microarray rnaseq geneexpression differentialexpression pathways graphandnetwork network genesetenrichment networkenrichment visualization reportwriting

6.2 match 20 stars 9.37 score 164 scripts 3 dependents

bioc

gage:Generally Applicable Gene-set Enrichment for Pathway Analysis

GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.

Maintained by Weijun Luo. Last updated 5 months ago.

pathways go differentialexpression microarray onechannel twochannel rnaseq genetics multiplecomparison genesetenrichment geneexpression systemsbiology sequencing

6.6 match 5 stars 8.71 score 784 scripts 1 dependents

bioc

CAEN:Category encoding method for selecting feature genes for the classification of single-cell RNA-seq

With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples.

Maintained by Zhou Yan. Last updated 5 months ago.

differentialexpression sequencing classification rnaseq atacseq singlecell geneexpression ripseq

12.5 match 4.60 score 2 scripts

bioc

NOISeq:Exploratory analysis and differential expression for RNA-seq data

Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions.

Maintained by Sonia Tarazona. Last updated 5 months ago.

immunooncology rnaseq differentialexpression visualization sequencing

8.5 match 6.70 score 207 scripts 4 dependents

lazappi

clustree:Visualise Clusterings at Different Resolutions

Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.

Maintained by Luke Zappia. Last updated 1 years ago.

clustering clustering-trees visualisation visualization

5.0 match 219 stars 11.40 score 1.9k scripts 5 dependents

bioc

epigenomix:Epigenetic and gene transcription data normalization and integration with mixture models

A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types.

Maintained by Hans-Ulrich Klein. Last updated 5 months ago.

chipseq geneexpression differentialexpression classification

17.1 match 3.30 score 1 scripts

bioc

phantasusLite:Loading and annotation RNA-seq counts matrices

PhantasusLite – a lightweight package with helper functions of general interest extracted from phantasus package. In parituclar it simplifies working with public RNA-seq datasets from GEO by providing access to the remote HSDS repository with the precomputed gene counts from ARCHS4 and DEE2 projects.

Maintained by Alexey Sergushichev. Last updated 5 months ago.

geneexpression transcriptomics rnaseq

9.3 match 8 stars 6.08 score 8 scripts 1 dependents

bioc

profileplyr:Visualization and annotation of read signal over genomic ranges with profileplyr

Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal.

Maintained by Tom Carroll. Last updated 5 months ago.

chipseq dataimport sequencing chiponchip coverage

14.0 match 4.03 score 54 scripts

bioc

batchelor:Single-Cell Batch Correction Methods

Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches.

Maintained by Aaron Lun. Last updated 1 days ago.

sequencing rnaseq software geneexpression transcriptomics singlecell batcheffect normalization cpp

6.1 match 9.10 score 1.2k scripts 10 dependents

stephenslab

fastTopics:Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.

Maintained by Peter Carbonetto. Last updated 15 days ago.

openblas cpp

6.6 match 79 stars 8.38 score 678 scripts 1 dependents

sblanck

metaRNASeq:Meta-Analysis of RNA-Seq Data

Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.

Maintained by samuel Blanck. Last updated 3 years ago.

highthroughputsequencing rnaseq differentialexpression

14.2 match 1 stars 3.82 score 22 scripts 2 dependents