R-universe search: needs:beachmat

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 12 days ago.

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

212 stars 14.74 score 1.6k scripts 19 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

41 stars 13.05 score 7.6k scripts 37 dependents

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 2 months ago.

software singlecell geneexpression transcriptomics classification clustering annotation bioconductor singler cpp

184 stars 12.83 score 2.1k scripts 2 dependents

bioc

scDblFinder:scDblFinder

The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq.

Maintained by Pierre-Luc Germain. Last updated 13 days ago.

preprocessing singlecell rnaseq atacseq doublets single-cell

184 stars 12.38 score 888 scripts 1 dependents

bioc

bsseq:Analyze, manage and store whole-genome methylation data

A collection of tools for analyzing and visualizing whole-genome methylation data from sequencing. This includes whole-genome bisulfite sequencing and Oxford nanopore data.

Maintained by Kasper Daniel Hansen. Last updated 4 months ago.

dnamethylation cpp

37 stars 12.26 score 676 scripts 15 dependents

bioc

glmGamPoi:Fit a Gamma-Poisson Generalized Linear Model

Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.

Maintained by Constantin Ahlmann-Eltze. Last updated 16 days ago.

regression rnaseq software singlecell gamma-poisson glm negative-binomial-regression on-disk openblas cpp

111 stars 12.16 score 1.0k scripts 4 dependents

bioc

BiocSingular:Singular Value Decomposition for Bioconductor Packages

Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework.

Maintained by Aaron Lun. Last updated 5 months ago.

software dimensionreduction principalcomponent bioconductor-package human-cell-atlas singular-value-decomposition cpp

7 stars 12.10 score 1.2k scripts 103 dependents

bioc

mia:Microbiome analysis

mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.

Maintained by Tuomas Borman. Last updated 21 hours ago.

microbiome software dataimport analysis bioconductor cpp

51 stars 11.52 score 316 scripts 5 dependents

bioc

PCAtools:PCAtools: Everything Principal Components Analysis

Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.

Maintained by Kevin Blighe. Last updated 5 months ago.

rnaseq atacseq geneexpression transcription singlecell principalcomponent cpp

348 stars 11.12 score 832 scripts 2 dependents

bioc

scater:Single-Cell Analysis Toolkit for Gene Expression Data in R

A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

Maintained by Alan OCallaghan. Last updated 26 days ago.

immunooncology singlecell rnaseq qualitycontrol preprocessing normalization visualization dimensionreduction transcriptomics geneexpression sequencing software dataimport datarepresentation infrastructure coverage

11.07 score 12k scripts 43 dependents

bioc

CATALYST:Cytometry dATa anALYSis Tools

CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.

Maintained by Helena L. Crowell. Last updated 4 months ago.

clustering dataimport differentialexpression experimentaldesign flowcytometry immunooncology massspectrometry normalization preprocessing singlecell software statisticalmethod visualization

67 stars 10.99 score 362 scripts 2 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

184 stars 10.74 score 686 scripts 1 dependents

bioc

miloR:Differential neighbourhood abundance testing on a graph

Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model.

Maintained by Mike Morgan. Last updated 5 months ago.

singlecell multiplecomparison functionalgenomics software openblas cpp openmp

362 stars 10.49 score 340 scripts 1 dependents

bioc

celda:CEllular Latent Dirichlet Allocation

Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.

Maintained by Joshua Campbell. Last updated 1 months ago.

singlecell geneexpression clustering sequencing bayesian immunooncology dataimport cpp openmp

147 stars 10.47 score 256 scripts 2 dependents

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 1 months ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

182 stars 10.17 score 252 scripts

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

10.16 score 1.7k scripts 83 dependents

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

83 stars 10.14 score 368 scripts 1 dependents

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 4 months ago.

immunooncology singlecell sequencing rnaseq geneexpression transcriptomics dataimport coverage zlib cpp

10.01 score 2.7k scripts 9 dependents

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

224 stars 9.92 score 424 scripts 1 dependents

bioc

clusterExperiment:Compare Clusterings for Single-Cell Sequencing

Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets.

Maintained by Elizabeth Purdom. Last updated 5 months ago.

clustering rnaseq sequencing software singlecell cpp

38 stars 9.62 score 192 scripts 1 dependents

bioc

scMerge:scMerge: Merging multiple batches of scRNA-seq data

Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data.

Maintained by Yingxin Lin. Last updated 5 months ago.

batcheffect geneexpression normalization rnaseq sequencing singlecell software transcriptomics bioinformatics single-cell

67 stars 9.52 score 137 scripts 1 dependents

bioc

SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf

A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

Maintained by Lambda Moses. Last updated 2 months ago.

datarepresentation transcriptomics spatial

49 stars 9.40 score 322 scripts 1 dependents

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 20 days ago.

sequencing rnaseq software geneexpression transcriptomics singlecell batcheffect normalization cpp

9.10 score 1.2k scripts 10 dependents

bioc

BatchQC:Batch Effects Quality Control Software

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

Maintained by Jessica Anderson. Last updated 15 days ago.

batcheffect graphandnetwork microarray normalization principalcomponent sequencing software visualization qualitycontrol rnaseq preprocessing differentialexpression immunooncology

7 stars 9.06 score 54 scripts

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 4 months ago.

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

68 stars 9.02 score 84 scripts

bioc

Voyager:From geospatial to spatial omics

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

Maintained by Lambda Moses. Last updated 3 months ago.

geneexpression spatial transcriptomics visualization bioconductor eda esda exploratory-data-analysis omics spatial-statistics spatial-transcriptomics

88 stars 8.71 score 173 scripts

bioc

miaViz:Microbiome Analysis Plotting and Visualization

The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages.

Maintained by Tuomas Borman. Last updated 14 days ago.

microbiome software visualization bioconductor microbiome-analysis plotting

10 stars 8.67 score 81 scripts 1 dependents

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

29 stars 8.53 score 119 scripts 2 dependents

bioc

ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.

Maintained by Johannes Griss. Last updated 4 months ago.

genesetenrichment proteomics transcriptomics systemsbiology geneexpression reactome

22 stars 8.50 score 67 scripts 1 dependents

bioc

lefser:R implementation of the LEfSE method for microbiome biomarker discovery

lefser is the R implementation of the popular microbiome biomarker discovery too, LEfSe. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers from two-level classes (and optional sub-classes).

Maintained by Sehyun Oh. Last updated 1 months ago.

software sequencing differentialexpression microbiome statisticalmethod classification bioconductor-package r01ca230551

56 stars 8.44 score 56 scripts

bioc

dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.

Maintained by Gabriel Hoffman. Last updated 8 days ago.

rnaseq geneexpression differentialexpression batcheffect qualitycontrol regression genesetenrichment generegulation epigenetics functionalgenomics transcriptomics normalization singlecell preprocessing sequencing immunooncology software cpp

12 stars 8.14 score 128 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

55 stars 8.06 score 52 scripts

bioc

FLAMES:FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data

Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

Maintained by Changqing Wang. Last updated 2 days ago.

rnaseq singlecell transcriptomics dataimport differentialsplicing alternativesplicing geneexpression longread zlib curl bzip2 xz-utils cpp

33 stars 8.04 score 12 scripts

bioc

scDD:Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

Maintained by Keegan Korthauer. Last updated 5 months ago.

immunooncology bayesian clustering rnaseq singlecell multiplecomparison visualization differentialexpression

33 stars 7.92 score 50 scripts

bioc

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

Maintained by Matt Stone. Last updated 5 months ago.

software clustering transcriptomics geneexpression singlecell immunooncology dataimport openblas cpp openmp

126 stars 7.90 score 278 scripts 1 dependents

bioc

lemur:Latent Embedding Multivariate Regression

Fit a latent embedding multivariate regression (LEMUR) model to multi-condition single-cell data. The model provides a parametric description of single-cell data measured with treatment vs. control or more complex experimental designs. The parametric model is used to (1) align conditions, (2) predict log fold changes between conditions for all cells, and (3) identify cell neighborhoods with consistent log fold changes. For those neighborhoods, a pseudobulked differential expression test is conducted to assess which genes are significantly changed.

Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.

transcriptomics differentialexpression singlecell dimensionreduction regression openblas cpp

87 stars 7.69 score 81 scripts

bioc

imcRtools:Methods for imaging mass cytometry data analysis

This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected.

Maintained by Daniel Schulz. Last updated 5 months ago.

immunooncology singlecell spatial dataimport clustering imc single-cell

24 stars 7.58 score 126 scripts

bioc

MIRA:Methylation-Based Inference of Regulatory Activity

DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.

Maintained by John Lawson. Last updated 5 months ago.

immunooncology dnamethylation generegulation genomeannotation systemsbiology functionalgenomics chipseq methylseq sequencing epigenetics coverage

12 stars 7.56 score 7 scripts 1 dependents

bioc

MGnifyR:R interface to EBI MGnify metagenomics resource

Utility package to facilitate integration and analysis of EBI MGnify data in R. The package can be used to import microbial data for instance into TreeSummarizedExperiment (TreeSE). In TreeSE format, the data is directly compatible with miaverse framework.

Maintained by Tuomas Borman. Last updated 6 days ago.

infrastructure dataimport metagenomics microbiome microbiomedata

21 stars 7.48 score 32 scripts

bioc

MOSim:Multi-Omics Simulation (MOSim)

MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups.

Maintained by Sonia Tarazona. Last updated 2 days ago.

software timecourse experimentaldesign rnaseq cpp

9 stars 7.46 score 11 scripts

bioc

mbkmeans:Mini-batch K-means Clustering for Single-Cell RNA-seq

Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation.

Maintained by Davide Risso. Last updated 5 months ago.

clustering geneexpression rnaseq software transcriptomics sequencing singlecell human-cell-atlas cpp

10 stars 7.41 score 54 scripts 2 dependents

bioc

netSmooth:Network smoothing for scRNAseq

netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data.

Maintained by Jonathan Ronen. Last updated 5 months ago.

network graphandnetwork singlecell rnaseq geneexpression sequencing transcriptomics normalization preprocessing clustering dimensionreduction bioinformatics genomics single-cell

27 stars 7.41 score 4 scripts

bioc

methylSig:MethylSig: Differential Methylation Testing for WGBS and RRBS Data

MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation.

Maintained by Raymond G. Cavalcante. Last updated 5 months ago.

dnamethylation differentialmethylation epigenetics regression methylseq differential-methylation dna-methylation

18 stars 7.40 score 23 scripts

bioc

scry:Small-Count Analysis Methods for High-Dimensional Data

Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.

Maintained by Kelly Street. Last updated 5 months ago.

dimensionreduction geneexpression normalization principalcomponent rnaseq software sequencing singlecell transcriptomics

19 stars 7.34 score 116 scripts

bioc

TBSignatureProfiler:Profile RNA-Seq Data Using TB Pathway Signatures

Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility.

Maintained by Aubrey R. Odom. Last updated 4 months ago.

geneexpression differentialexpression bioconductor-package biomarkers gene-signatures tuberculosis

12 stars 7.25 score 23 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

41 stars 7.02 score 43 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

7.02 score 248 scripts 5 dependents

bioc

COCOA:Coordinate Covariation Analysis

COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.

Maintained by John Lawson. Last updated 5 months ago.

epigenetics dnamethylation atacseq dnaseseq methylseq methylationarray principalcomponent genomicvariation generegulation genomeannotation systemsbiology functionalgenomics chipseq sequencing immunooncology dna-methylation pca

10 stars 7.02 score 21 scripts

bioc

NanoMethViz:Visualise methylation data from Oxford Nanopore sequencing

NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.

Maintained by Shian Su. Last updated 23 days ago.

software longread visualization differentialmethylation dnamethylation epigenetics dataimport zlib cpp

26 stars 6.95 score 11 scripts

bioc

COTAN:COexpression Tables ANalysis

Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes.

Maintained by Galfrè Silvia Giulia. Last updated 21 days ago.

systemsbiology transcriptomics geneexpression singlecell

16 stars 6.85 score 96 scripts

bioc

BioTIP:BioTIP: An R package for characterization of Biological Tipping-Point

Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory.

Maintained by Yuxi (Jennifer) Sun. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription software

18 stars 6.84 score 37 scripts

bioc

epiregulon:Gene regulatory network inference from single cell epigenomic data

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 23 days ago.

singlecell generegulation networkinference network geneexpression transcription genetarget cpp

14 stars 6.67 score 17 scripts

bioc

CiteFuse:CiteFuse: multi-modal analysis of CITE-seq data

CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.

Maintained by Yingxin Lin. Last updated 5 months ago.

singlecell geneexpression bioinformatics single-cell cpp

27 stars 6.59 score 18 scripts

bioc

tricycle:tricycle: Transferable Representation and Inference of cell cycle

The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference.

Maintained by Shijie Zheng. Last updated 5 months ago.

singlecell software transcriptomics rnaseq transcription biologicalquestion dimensionreduction immunooncology

25 stars 6.54 score 46 scripts

bioc

condiments:Differential Topology, Progression and Differentiation

This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.

Maintained by Hector Roux de Bezieux. Last updated 4 months ago.

rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

26 stars 6.52 score 17 scripts

bioc

ChAMP:Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC

The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations.

Maintained by Yuan Tian. Last updated 5 months ago.

microarray methylationarray normalization twochannel copynumber dnamethylation

6.50 score 278 scripts

bioc

dmrseq:Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing

This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions.

Maintained by Keegan Korthauer. Last updated 5 months ago.

immunooncology dnamethylation epigenetics multiplecomparison software sequencing differentialmethylation wholegenome regression functionalgenomics

6.39 score 59 scripts 1 dependents

bioc

distinct:distinct: a method for differential analyses via hierarchical permutation tests

distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group.

Maintained by Simone Tiberi. Last updated 5 months ago.

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

11 stars 6.35 score 34 scripts 1 dependents

bioc

CellMixS:Evaluate Cellspecific Mixing

CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level.

Maintained by Almut Lütge. Last updated 5 months ago.

singlecell transcriptomics geneexpression batcheffect

7 stars 6.35 score 64 scripts

bioc

iSEEtree:Interactive visualisation for microbiome data

iSEEtree is an extension of iSEE for the TreeSummarizedExperiment data container. It provides interactive panel designs to explore hierarchical datasets, such as the microbiome and cell lines.

Maintained by Giulio Benedetti. Last updated 14 days ago.

software visualization microbiome gui shinyapps dataimport shiny-apps visualisation

3 stars 6.28 score 5 scripts

bioc

signifinder:Collection and implementation of public transcriptional cancer signatures

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.

Maintained by Stefania Pirrotta. Last updated 3 months ago.

geneexpression genetarget immunooncology biomedicalinformatics rnaseq microarray reportwriting visualization singlecell spatial genesignaling

7 stars 6.28 score 15 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

12 stars 6.09 score 34 scripts

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 21 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

7 stars 6.05 score 3 scripts

bioc

CRISPRball:Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering

A Shiny application for visualization, exploration, comparison, and filtering of CRISPR screens analyzed with MAGeCK RRA or MLE. Features include interactive plots with on-click labeling, full customization of plot aesthetics, data upload and/or download, and much more. Quickly and easily explore your CRISPR screen results and generate publication-quality figures in seconds.

Maintained by Jared Andrews. Last updated 3 months ago.

software shinyapps crispr qualitycontrol visualization gui crispr-screen data-visualization interactive-visualizations mageck plotly screening shiny

9 stars 6.03 score 24 scripts

bioc

Dino:Normalization of Single-Cell mRNA Sequencing Data

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.

Maintained by Jared Brown. Last updated 5 months ago.

software normalization rnaseq singlecell sequencing geneexpression transcriptomics regression cellbasedassays

9 stars 6.02 score 13 scripts

bioc

biscuiteer:Convenience Functions for Biscuit

A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc.

Maintained by Jacob Morrison. Last updated 5 months ago.

dataimport methylseq dnamethylation

6 stars 5.98 score 16 scripts

bioc

dar:Differential Abundance Analysis by Consensus

Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.

Maintained by Francesc Catala-Moll. Last updated 18 days ago.

software sequencing microbiome metagenomics multiplecomparison normalization bioconductor biomarker-discovery differential-abundance-analysis feature-selection microbiology phyloseq

2 stars 5.98 score 8 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

3 stars 5.98 score 7 scripts

bioc

consensusOV:Gene expression-based subtype classification for high-grade serous ovarian cancer

This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.

Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.

classification clustering differentialexpression geneexpression microarray transcriptomics cancer-data cancer-genomics cancer-research expression-database ovarian-cancer

3 stars 5.98 score 15 scripts 1 dependents

bioc

StabMap:Stabilised mosaic single cell data integration using unshared features

StabMap performs single cell mosaic data integration by first building a mosaic data topology, and for each reference dataset, traverses the topology to project and predict data onto a common embedding. Mosaic data should be provided in a list format, with all relevant features included in the data matrices within each list object. The output of stabMap is a joint low-dimensional embedding taking into account all available relevant features. Expression imputation can also be performed using the StabMap embedding and any of the original data matrices for given reference and query cell lists.

Maintained by Shila Ghazanfar. Last updated 5 months ago.

singlecell dimensionreduction software

5.95 score 60 scripts

bioc

transformGamPoi:Variance Stabilizing Transformation for Gamma-Poisson Models

Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).

Maintained by Constantin Ahlmann-Eltze. Last updated 5 months ago.

singlecell normalization preprocessing regression cpp

21 stars 5.95 score 21 scripts

bioc

SingleCellSignalR:Cell Signalling Using Single Cell RNAseq Data Analysis

Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions.

Maintained by Jacques Colinge Developer. Last updated 5 months ago.

singlecell network clustering rnaseq classification

5.87 score 35 scripts 1 dependents

bioc

SpaNorm:Spatially-aware normalisation for spatial transcriptomics data

This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.

Maintained by Dharmesh D. Bhuva. Last updated 5 months ago.

software geneexpression transcriptomics spatial cellbiology

9 stars 5.86 score 3 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 2 months ago.

openblas cpp

2 stars 5.86 score 12 scripts 1 dependents

bioc

escape:Easy single cell analysis platform for enrichment

A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations across individual cells.

Maintained by Nick Borcherding. Last updated 12 days ago.

software singlecell classification annotation genesetenrichment sequencing genesignaling pathways

5.84 score 138 scripts

bioc

ChromSCape:Analysis of single-cell epigenomics datasets with a Shiny App

ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis.

Maintained by Pacome Prompsy. Last updated 5 months ago.

shinyapps software singlecell chipseq atacseq methylseq classification clustering epigenetics principalcomponent annotation batcheffect multiplecomparison normalization pathways preprocessing qualitycontrol reportwriting visualization genesetenrichment differentialpeakcalling epigenomics shiny single-cell cpp

14 stars 5.83 score 16 scripts

bioc

SpatialExperimentIO:Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object

Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object.

Maintained by Yixing E. Dong. Last updated 2 months ago.

datarepresentation dataimport infrastructure transcriptomics singlecell spatial geneexpression

9 stars 5.81 score 16 scripts

bioc

dandelionR:Single-cell Immune Repertoire Trajectory Analysis in R

dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package.

Maintained by Kelvin Tuong. Last updated 30 days ago.

software immunooncology singlecell

8 stars 5.81 score 7 scripts

bioc

EGSEA:Ensemble of Gene Set Enrichment Analyses

This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set.

Maintained by Monther Alhamdoosh. Last updated 5 months ago.

immunooncology differentialexpression go geneexpression genesetenrichment genetics microarray multiplecomparison onechannel pathways rnaseq sequencing software systemsbiology twochannel metabolomics proteomics kegg graphandnetwork genesignaling genetarget networkenrichment network classification

5.81 score 64 scripts

bioc

scFeatures:scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.

Maintained by Yue Cao. Last updated 5 months ago.

cellbasedassays singlecell spatial software transcriptomics

11 stars 5.69 score 15 scripts

xiaozhangryy

CAESAR.Suite:CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation

Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

Maintained by Xiao Zhang. Last updated 8 days ago.

openblas cpp

1 stars 5.67 score 2 scripts

bioc

lute:Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments

Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms.

Maintained by Sean K Maden. Last updated 5 months ago.

rnaseq sequencing singlecell coverage transcriptomics normalization

3 stars 5.65 score 3 scripts

bioc

scrapper:Bindings to C++ Libraries for Single-Cell Analysis

Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. It is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows.

Maintained by Aaron Lun. Last updated 16 days ago.

normalization rnaseq software geneexpression transcriptomics singlecell batcheffect qualitycontrol differentialexpression featureextraction principalcomponent clustering openblas cpp

5.57 score 32 scripts

bioc

chevreulPlot:Plots used in the chevreulPlot package

Tools for plotting SingleCellExperiment objects in the chevreulPlot package. Includes functions for analysis and visualization of single-cell data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 1 months ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.56 score 2 scripts 1 dependents

bioc

scDotPlot:Cluster a Single-cell RNA-seq Dot Plot

Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames.

Maintained by Benjamin I Laufer. Last updated 13 days ago.

software visualization differentialexpression geneexpression transcription rnaseq singlecell sequencing clustering

7 stars 5.45 score 2 scripts

bioc

chevreulProcess:Tools for managing SingleCellExperiment objects as projects

Tools analyzing SingleCellExperiment objects as projects. for input into the Chevreul app downstream. Includes functions for analysis of single cell RNA sequencing data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 2 months ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.38 score 2 scripts 2 dependents

igordot

clustermole:Unbiased Single-Cell Transcriptomic Data Cell Type Identification

Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.

Maintained by Igor Dolgalev. Last updated 1 years ago.

cell-type cell-type-annotation cell-type-classification cell-type-identification cell-type-matching gene-expression-signatures scrna-seq single-cell

13 stars 5.37 score 36 scripts

bioc

visiumStitched:Enable downstream analysis of Visium capture areas stitched together with Fiji

This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexogonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects.

Maintained by Nicholas J. Eagles. Last updated 4 months ago.

software spatial transcriptomics transcription geneexpression visualization dataimport 10xgenomics bioconductor spatial-transcriptomics spatialexperiment spatiallibd visium

1 stars 5.36 score 4 scripts

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

4 stars 5.33 score 27 scripts

bioc

SCArray:Large-scale single-cell omics data manipulation with GDS files

Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.

Maintained by Xiuwen Zheng. Last updated 9 days ago.

infrastructure datarepresentation dataimport singlecell rnaseq cpp

1 stars 5.32 score 9 scripts 1 dependents

bioc

scCB2:CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq singlecell sequencing geneexpression transcriptomics preprocessing clustering

10 stars 5.30 score 5 scripts

bioc

scviR:experimental inferface from R to scvi-tools

This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.

Maintained by Vincent Carey. Last updated 5 months ago.

infrastructure singlecell dataimport bioconductor cite-seq scverse

6 stars 5.30 score 11 scripts

bioc

DeMixT:Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms

DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.

Maintained by Ruonan Li. Last updated 5 months ago.

software statisticalmethod classification geneexpression sequencing microarray tissuemicroarray coverage cpp openmp

5.27 score 25 scripts

bioc

methylCC:Estimate the cell composition of whole blood in DNA methylation samples

A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing).

Maintained by Stephanie C. Hicks. Last updated 5 months ago.

microarray sequencing dnamethylation methylationarray methylseq wholegenome

19 stars 5.18 score 8 scripts

bioc

msImpute:Imputation of label-free mass spectrometry peptides

MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP).

Maintained by Soroor Hediyeh-zadeh. Last updated 5 months ago.

massspectrometry proteomics software imputation-algorithm label-free-proteomics low-rank-approximation

14 stars 5.15 score 7 scripts

bioc

SplineDV:Differential Variability (DV) analysis for single-cell RNA sequencing data. (e.g. Identify Differentially Variable Genes across two experimental conditions)

A spline based scRNA-seq method for identifying differentially variable (DV) genes across two experimental conditions. Spline-DV constructs a 3D spline from 3 key gene statistics: mean expression, coefficient of variance, and dropout rate. This is done for both conditions. The 3D spline provides the “expected” behavior of genes in each condition. The distance of the observed mean, CV and dropout rate of each gene from the expected 3D spline is used to measure variability. As the final step, the spline-DV method compares the variabilities of each condition to identify differentially variable (DV) genes.

Maintained by Shreyan Gupta. Last updated 2 months ago.

software singlecell sequencing differentialexpression rnaseq geneexpression transcriptomics featureextraction

2 stars 5.08 score 3 scripts

bioc

chevreulShiny:Tools for managing SingleCellExperiment objects as projects

Tools for managing SingleCellExperiment objects as projects. Includes functions for analysis and visualization of single-cell data. Also included is a shiny app for visualization of pre-processed scRNA data. Supported by NIH grants R01CA137124 and R01EY026661 to David Cobrinik.

Maintained by Kevin Stachelek. Last updated 30 days ago.

coverage rnaseq sequencing visualization geneexpression transcription singlecell transcriptomics normalization preprocessing qualitycontrol dimensionreduction dataimport

5.08 score

bioc

epiregulon.extra:Companion package to epiregulon with additional plotting, differential and graph functions

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 14 days ago.

generegulation network geneexpression transcription chiponchip differentialexpression genetarget normalization graphandnetwork

4.95 score 10 scripts

bioc

decontX:Decontamination of single cell genomics data

This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset.

Maintained by Joshua Campbell. Last updated 1 months ago.

singlecell bayesian cpp

4.94 score 29 scripts

andrewdhawan

sigQC:Quality Control Metrics for Gene Signatures

Provides gene signature quality control metrics in publication ready plots. Namely, enables the visualization of properties such as expression, variability, correlation, and comparison of methods of standardisation and scoring metrics.

Maintained by Andrew Dhawan. Last updated 8 months ago.

4 stars 4.89 score 13 scripts

diegommcc

SpatialDDLS:Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.

Maintained by Diego Mañanes. Last updated 5 months ago.

deconvolution deep-learning neural-network spatial-transcriptomics

5 stars 4.88 score 1 scripts

bioc

beachmat.hdf5:beachmat bindings for HDF5-backed matrices

Extends beachmat to support initialization of tatami matrices from HDF5-backed arrays. This allows C++ code in downstream packages to directly call the HDF5 C/C++ library to access array data, without the need for block processing via DelayedArray. Some utilities are also provided for direct creation of an in-memory tatami matrix from a HDF5 file.

Maintained by Aaron Lun. Last updated 5 months ago.

datarepresentation dataimport infrastructure zlib cpp

4.88 score 6 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 6 months ago.

sequencing geneticvariability singlecell cpp

2 stars 4.85 score 3 scripts

bioc

CelliD:Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis

CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.

Maintained by Akira Cortal. Last updated 5 months ago.

rnaseq singlecell dimensionreduction clustering genesetenrichment geneexpression atacseq openblas cpp openmp

4.85 score 70 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

4.78 score 1 scripts

bioc

airpart:Differential cell-type-specific allelic imbalance

Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets.

Maintained by Wancen Mu. Last updated 5 months ago.

singlecell rnaseq atacseq chipseq sequencing generegulation geneexpression transcription transcriptomevariant cellbiology functionalgenomics differentialexpression graphandnetwork regression clustering qualitycontrol

2 stars 4.78 score 2 scripts

bioc

spoon:Address the Mean-variance Relationship in Spatial Transcriptomics Data

This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools.

Maintained by Kinnary Shah. Last updated 14 days ago.

spatial singlecell transcriptomics geneexpression preprocessing

4.76 score 19 scripts

bioc

scmeth:Functions to conduct quality control analysis in methylation data

Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report.

Maintained by Divy Kangeyan. Last updated 5 months ago.

dnamethylation qualitycontrol preprocessing singlecell immunooncology bioconductor-package methylation single-cell-methylation

4.70 score 5 scripts

bioc

DelayedTensor:R package for sparse and out-of-core arithmetic and decomposition of Tensor

DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum.

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

software infrastructure datarepresentation dimensionreduction

4 stars 4.68 score 3 scripts

bioc

beachmat.tiledb:beachmat bindings for TileDB-backed matrices

Extends beachmat to initialize tatami matrices from TileDB-backed arrays. This allows C++ code in downstream packages to directly call the TileDB C/C++ library to access array data, without the need for block processing via DelayedArray. Developers only need to import this package to automatically extend the capabilities of beachmat::initializeCpp to TileDBArray instances.

Maintained by Aaron Lun. Last updated 4 months ago.

datarepresentation dataimport infrastructure cpp

4.65 score 4 scripts

bioc

mumosa:Multi-Modal Single-Cell Analysis Methods

Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities.

Maintained by Aaron Lun. Last updated 5 months ago.

immunooncology singlecell rnaseq

4.41 score 13 scripts

bioc

Spaniel:Spatial Transcriptomics Analysis

Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue.

Maintained by Rachel Queen. Last updated 5 months ago.

singlecell rnaseq qualitycontrol preprocessing normalization visualization transcriptomics geneexpression sequencing software dataimport datarepresentation infrastructure coverage clustering

4.34 score 22 scripts

yunuuuu

BPCellsArray:Using BPCells as a DelayedArray Backend

Implements a DelayedArray backend for reading and writing arrays in the BPCells storage layout. The resulting BPCells*Arrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances.

Maintained by Yun Peng. Last updated 8 months ago.

software dataimport datarepresentation infrastructure single-cell

7 stars 4.32 score

bioc

cytofQC:Labels normalized cells for CyTOF data and assigns probabilities for each label

cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells.

Maintained by Jill Lundell. Last updated 5 months ago.

software singlecell annotation

2 stars 4.30 score 3 scripts

bioc

RegionalST:Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data

This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application.

Maintained by Ziyi Li. Last updated 4 months ago.

spatial transcriptomics reactome kegg

4.30 score 8 scripts

bioc

SOMNiBUS:Smooth modeling of bisulfite sequencing

This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture.

Maintained by Kathleen Klein. Last updated 3 months ago.

dnamethylation regression epigenetics differentialmethylation sequencing functionalprediction

1 stars 4.30 score 3 scripts

bioc

spillR:Spillover Compensation in Mass Cytometry Data

Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. We implement our method using expectation-maximization to fit the mixture model.

Maintained by Marco Guazzini. Last updated 5 months ago.

flowcytometry immunooncology massspectrometry preprocessing singlecell software statisticalmethod visualization regression

4.30 score 3 scripts

bioc

regionalpcs:Summarizing Regional Methylation with Regional Principal Components Analysis

Functions to summarize DNA methylation data using regional principal components. Regional principal components are computed using principal components analysis within genomic regions to summarize the variability in methylation levels across CpGs. The number of principal components is chosen using either the Marcenko-Pasteur or Gavish-Donoho method to identify relevant signal in the data.

Maintained by Tiffany Eulalio. Last updated 5 months ago.

dnamethylation differentialmethylation statisticalmethod software methylationarray

2 stars 4.30 score 4 scripts

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 1 months ago.

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

5 stars 4.18 score 6 scripts

bioc

MPAC:Multi-omic Pathway Analysis of Cells

Multi-omic Pathway Analysis of Cells (MPAC), integrates multi-omic data for understanding cellular mechanisms. It predicts novel patient groups with distinct pathway profiles as well as identifying key pathway proteins with potential clinical associations. From CNA and RNA-seq data, it determines genes’ DNA and RNA states (i.e., repressed, normal, or activated), which serve as the input for PARADIGM to calculate Inferred Pathway Levels (IPLs). It also permutes DNA and RNA states to create a background distribution to filter IPLs as a way to remove events observed by chance. It provides multiple methods for downstream analysis and visualization.

Maintained by Peng Liu. Last updated 19 days ago.

software technology sequencing rnaseq survival clustering immunooncology

4.18 score 1 scripts

bioc

simPIC:simPIC: flexible simulation of paired-insertion counts for single-cell ATAC-sequencing data

simPIC is a package for simulating single-cell ATAC-seq count data. It provides a user-friendly, well documented interface for data simulation. Functions are provided for parameter estimation, realistic scATAC-seq data simulation, and comparing real and simulated datasets.

Maintained by Sagrika Chugh. Last updated 5 months ago.

singlecell atacseq software sequencing immunooncology dataimport

4.18 score 3 scripts

bioc

DESpace:DESpace: a framework to discover spatially variable genes and differential spatial patterns across conditions

Intuitive framework for identifying spatially variable genes (SVGs) and differential spatial variable pattern (DSP) between conditions via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. For multi-sample, multi-condition datasets, we again fit a NB model via edgeR, incorporating spatial clusters, conditions and their interactions as covariates. DSP genes-representing differences in spatial gene expression patterns across experimental conditions-are identified by testing the interaction between spatial clusters and conditions.

Maintained by Peiying Cai. Last updated 5 days ago.

spatial singlecell rnaseq transcriptomics geneexpression sequencing differentialexpression statisticalmethod visualization

4 stars 4.02 score 13 scripts

bioc

octad:Open Cancer TherApeutic Discovery (OCTAD)

OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds.

Maintained by E. Chekalin. Last updated 5 months ago.

classification geneexpression pharmacogenetics pharmacogenomics software genesetenrichment

4.00 score 4 scripts

bioc

VAExprs:Generating Samples of Gene Expression Data with Variational Autoencoders

A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones.

Maintained by Dongmin Jung. Last updated 5 months ago.

software geneexpression singlecell openjdk

4.00 score 4 scripts

hanjunwei-lab

SMDIC:Identification of Somatic Mutation-Driven Immune Cells

A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..

Maintained by Junwei Han. Last updated 6 months ago.

2 stars 4.00 score 5 scripts

bioc

scTreeViz:R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations

scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters.

Maintained by Jayaram Kancherla. Last updated 5 months ago.

visualization infrastructure gui singlecell

4.00 score 3 scripts

hanjunwei-lab

SubtypeDrug:Prioritization of Candidate Cancer Subtype Specific Drugs

A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.

Maintained by Junwei Han. Last updated 1 years ago.

2 stars 4.00 score 3 scripts

juananvg

GSEMA:Gene Set Enrichment Meta-Analysis

Performing the different steps of gene set enrichment meta-analysis. It provides different functions that allow the application of meta-analysis based on the combination of effect sizes from different pathways in different studies to obtain significant pathways that are common to all of them.

Maintained by Juan Antonio Villatoro-García. Last updated 6 months ago.

statisticalmethod genesetenrichment pathways

3.90 score 3 scripts

mpallocc

autoGO:Auto-GO: Reproducible, Robust and High Quality Ontology Enrichment Visualizations

Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.

Maintained by Fabio Ticconi. Last updated 1 months ago.

2 stars 3.90 score

bioc

BASiCStan:Stan implementation of BASiCS

Provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries.

Maintained by Alan OCallaghan. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology single-cell-rna-seq cpp

3.78 score 1 scripts

bioc

borealis:Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution

Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses.

Maintained by Garrett Jenkinson. Last updated 5 months ago.

sequencing coverage dnamethylation differentialmethylation

3.73 score 27 scripts

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

4 stars 3.60 score 4 scripts

bioc

SCArray.sat:Large-scale single-cell RNA-seq data analysis using GDS files and Seurat

Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets.

Maintained by Xiuwen Zheng. Last updated 9 days ago.

datarepresentation dataimport singlecell rnaseq

1 stars 3.48 score 3 scripts

thecailab

SCRIP:An Accurate Simulator for Single-Cell RNA Sequencing Data

We provide a comprehensive scheme that is capable of simulating Single Cell RNA Sequencing data for various parameters of Biological Coefficient of Variation, busting kinetics, differential expression (DE), cell or sample groups, cell trajectory, batch effect and other experimental designs. 'SCRIP' proposed and compared two frameworks with Gamma-Poisson and Beta-Gamma-Poisson models for simulating Single Cell RNA Sequencing data. Other reference is available in Zappia et al. (2017) <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0>.

Maintained by Fei Qin. Last updated 2 years ago.

2 stars 3.41 score 13 scripts

dsokolo

scMappR:Single Cell Mapper

The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>.

Maintained by Dustin Sokolowski. Last updated 2 years ago.

4 stars 3.30 score 9 scripts

bioc

oppar:Outlier profile and pathway analysis in R

The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis.

Maintained by Soroor Hediyeh zadeh. Last updated 5 months ago.

pathways genesetenrichment systemsbiology geneexpression software

3.30 score 3 scripts

shufeyangyi2015310117

SC.MEB:Spatial Clustering with Hidden Markov Random Field using Empirical Bayes

Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Maintained by Yi Yang. Last updated 3 years ago.

openblas cpp

3.04 score 11 scripts

ellisdoro

jrSiCKLSNMF:Clustering Single-Cell Multimodal Omics Data with Joint Graph Regularized Single-Cell Kullback-Leibler Sparse Non-Negative Matrix Factorization

Methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") on quality controlled multi-assay single-cell omics count data, specifically dual-assay scRNA-seq and scATAC-seq data. 'jrSiCKLSNMF' extracts meaningful latent factors that are shared across omics views. These factors enable accurate cell-type clustering, and facilitate visualizations. Also includes methods for mini- batch updates and other adaptations for larger datasets.

Maintained by Dorothy Ellis. Last updated 10 months ago.

openblas cpp openmp

3.00 score 6 scripts

sridhara-omics

scPipeline:A Wrapper for 'Seurat' and Related R Packages for End-to-End Single Cell Analysis

Reports markers list, differentially expressed genes, associated pathways, cell-type annotations, does batch correction and other related single cell analyses all wrapped within 'Seurat'.

Maintained by Viswanadham Sridhara. Last updated 26 days ago.

2.70 score

bhaibeka

SIGN:Similarity Identification in Gene Expression

Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.

Maintained by Benjamin Haibe-Kains. Last updated 6 years ago.

geneexpression classification clustering survival

2.70 score 3 scripts

igordot

phenomenalist:Analysis Toolkit for PhenoCycler (CODEX) Data in R

A collection of tools for cleaning, clustering, and plotting PhenoCycler (CODEX) data.

Maintained by Igor Dolgalev. Last updated 1 years ago.

codex phenocycler

3 stars 2.18 score 1 scripts

hanjunwei-lab

DRviaSPCN:Drug Repurposing in Cancer via a Subpathway Crosstalk Network

A systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results.

Maintained by Junwei Han. Last updated 3 months ago.

2.00 score 5 scripts

thermostats

ssdGSA:Single Sample Directional Gene Set Analysis

A method that inherits the standard gene set variation analysis (GSVA) method and also provides the option to use summary statistics from any analysis (disease vs healthy, lesional side vs nonlesional side, etc..) input to define the direction of gene sets used for directional gene set score calculation for a given disease. Note to use this package, GSVA(>= 1.52.1) is needed to pre-installed. Hanzelmann, S., Castelo, R., and Guinney, J. (2013) <doi:10.1186/1471-2105-14-7>.

Maintained by Xingpeng Li. Last updated 8 months ago.

2.00 score 3 scripts

blaserlab

blaseRdata:Supporting Data for the blaseRtools Package

What the package does (one paragraph).

Maintained by Brad Blaser. Last updated 1 years ago.

1.70 score 6 scripts

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

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

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

scDblFinder:scDblFinder

bsseq:Analyze, manage and store whole-genome methylation data

glmGamPoi:Fit a Gamma-Poisson Generalized Linear Model

BiocSingular:Singular Value Decomposition for Bioconductor Packages

mia:Microbiome analysis

PCAtools:PCAtools: Everything Principal Components Analysis

scater:Single-Cell Analysis Toolkit for Gene Expression Data in R

CATALYST:Cytometry dATa anALYSis Tools

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

miloR:Differential neighbourhood abundance testing on a graph

celda:CEllular Latent Dirichlet Allocation

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

scuttle:Single-Cell RNA-Seq Analysis Utilities

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

DropletUtils:Utilities for Handling Single-Cell Droplet Data

splatter:Simple Simulation of Single-cell RNA Sequencing Data

clusterExperiment:Compare Clusterings for Single-Cell Sequencing

scMerge:scMerge: Merging multiple batches of scRNA-seq data

SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf

batchelor:Single-Cell Batch Correction Methods

BatchQC:Batch Effects Quality Control Software

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

Voyager:From geospatial to spatial omics

miaViz:Microbiome Analysis Plotting and Visualization

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

ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

lefser:R implementation of the LEfSE method for microbiome biomarker discovery

dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

velociraptor:Toolkit for Single-Cell Velocity

FLAMES:FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data

scDD:Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

lemur:Latent Embedding Multivariate Regression

imcRtools:Methods for imaging mass cytometry data analysis

MIRA:Methylation-Based Inference of Regulatory Activity

MGnifyR:R interface to EBI MGnify metagenomics resource

MOSim:Multi-Omics Simulation (MOSim)

mbkmeans:Mini-batch K-means Clustering for Single-Cell RNA-seq

netSmooth:Network smoothing for scRNAseq

methylSig:MethylSig: Differential Methylation Testing for WGBS and RRBS Data

scry:Small-Count Analysis Methods for High-Dimensional Data

TBSignatureProfiler:Profile RNA-Seq Data Using TB Pathway Signatures

pipeComp:pipeComp pipeline benchmarking framework

DSS:Dispersion shrinkage for sequencing data

COCOA:Coordinate Covariation Analysis

NanoMethViz:Visualise methylation data from Oxford Nanopore sequencing

COTAN:COexpression Tables ANalysis

BioTIP:BioTIP: An R package for characterization of Biological Tipping-Point

epiregulon:Gene regulatory network inference from single cell epigenomic data

CiteFuse:CiteFuse: multi-modal analysis of CITE-seq data

tricycle:tricycle: Transferable Representation and Inference of cell cycle

condiments:Differential Topology, Progression and Differentiation

ChAMP:Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC

dmrseq:Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing

distinct:distinct: a method for differential analyses via hierarchical permutation tests

CellMixS:Evaluate Cellspecific Mixing

iSEEtree:Interactive visualisation for microbiome data

signifinder:Collection and implementation of public transcriptional cancer signatures

peco:A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data

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

CRISPRball:Shiny Application for Interactive CRISPR Screen Visualization, Exploration, Comparison, and Filtering

Dino:Normalization of Single-Cell mRNA Sequencing Data

biscuiteer:Convenience Functions for Biscuit

dar:Differential Abundance Analysis by Consensus

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

consensusOV:Gene expression-based subtype classification for high-grade serous ovarian cancer

StabMap:Stabilised mosaic single cell data integration using unshared features

transformGamPoi:Variance Stabilizing Transformation for Gamma-Poisson Models

SingleCellSignalR:Cell Signalling Using Single Cell RNAseq Data Analysis

SpaNorm:Spatially-aware normalisation for spatial transcriptomics data

ProFAST:Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction

escape:Easy single cell analysis platform for enrichment

ChromSCape:Analysis of single-cell epigenomics datasets with a Shiny App

SpatialExperimentIO:Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object

dandelionR:Single-cell Immune Repertoire Trajectory Analysis in R

EGSEA:Ensemble of Gene Set Enrichment Analyses

scFeatures:scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

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