R-universe search: edger

bioc

edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 5 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

68.7 match 13.40 score 17k scripts 255 dependents

bioc

tidybulk:Brings transcriptomics to the tidyverse

This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion.

Maintained by Stefano Mangiola. Last updated 5 months ago.

assaydomain infrastructure rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics bioconductor bulk-transcriptional-analyses deseq2 differential-expression edger ensembl-ids entrez gene-symbols gsea mds-dimensions pca pipe redundancy tibble tidy tidy-data tidyverse transcripts tsne

13.5 match 168 stars 9.48 score 172 scripts 1 dependents

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regionReport:Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results

Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results.

Maintained by Leonardo Collado-Torres. Last updated 2 months ago.

differentialexpression sequencing rnaseq software visualization transcription coverage reportwriting differentialmethylation differentialpeakcalling immunooncology qualitycontrol bioconductor derfinder deseq2 edger regionreport rmarkdown

17.1 match 9 stars 7.22 score 46 scripts

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beer:Bayesian Enrichment Estimation in R

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

Maintained by Athena Chen. Last updated 5 months ago.

software statisticalmethod bayesian sequencing coverage jags cpp

17.2 match 10 stars 5.38 score 12 scripts

bioc

Glimma:Interactive visualizations for gene expression analysis

This package produces interactive visualizations for RNA-seq data analysis, utilizing output from limma, edgeR, or DESeq2. It produces interactive htmlwidgets versions of popular RNA-seq analysis plots to enhance the exploration of analysis results by overlaying interactive features. The plots can be viewed in a web browser or embedded in notebook documents.

Maintained by Shian Su. Last updated 1 months ago.

differentialexpression geneexpression microarray reportwriting rnaseq sequencing visualization differential-expression interactive-visualizations

8.5 match 32 stars 10.58 score 600 scripts 1 dependents

person-c

easybio:Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit

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

Maintained by Wei Cui. Last updated 13 days ago.

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

11.0 match 10 stars 6.62 score 35 scripts

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ppcseq:Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models

Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

Maintained by Stefano Mangiola. Last updated 5 months ago.

rnaseq differentialexpression geneexpression normalization clustering qualitycontrol sequencing transcription transcriptomics bayesian-inference deseq2 edger negative-binomial outlier stan cpp

11.0 match 7 stars 5.65 score 16 scripts

tinnlab

RCPA:Consensus Pathway Analysis

Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.

Maintained by Ha Nguyen. Last updated 4 months ago.

biobase deseq2 geoquery edger limma rcyjs fgsea browserviz summarizedexperiment annotationdbi rontotools

11.0 match 1 stars 5.50 score 70 scripts

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compcodeR:RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods

This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package.

Maintained by Charlotte Soneson. Last updated 3 months ago.

immunooncology rnaseq differentialexpression

7.2 match 11 stars 8.06 score 26 scripts

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vidger:Create rapid visualizations of RNAseq data in R

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

Maintained by Brandon Monier. Last updated 5 months ago.

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

6.5 match 19 stars 6.61 score 27 scripts

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satuRn:Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications

satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest.

Maintained by Jeroen Gilis. Last updated 5 months ago.

regression experimentaldesign differentialexpression geneexpression rnaseq sequencing software singlecell transcriptomics multiplecomparison visualization

6.1 match 21 stars 6.97 score 74 scripts 1 dependents

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systemPipeR:systemPipeR: Workflow Environment for Data Analysis and Report Generation

systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates.

Maintained by Thomas Girke. Last updated 5 months ago.

genetics infrastructure dataimport sequencing rnaseq riboseq chipseq methylseq snp geneexpression coverage genesetenrichment alignment qualitycontrol immunooncology reportwriting workflowstep workflowmanagement

3.6 match 53 stars 11.56 score 344 scripts 3 dependents

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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 6 days ago.

software longread visualization differentialmethylation dnamethylation epigenetics dataimport zlib cpp

5.7 match 26 stars 6.95 score 11 scripts

bioc

treeclimbR:An algorithm to find optimal signal levels in a tree

The arrangement of hypotheses in a hierarchical structure appears in many research fields and often indicates different resolutions at which data can be viewed. This raises the question of which resolution level the signal should best be interpreted on. treeclimbR provides a flexible method to select optimal resolution levels (potentially different levels in different parts of the tree), rather than cutting the tree at an arbitrary level. treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one.

Maintained by Charlotte Soneson. Last updated 3 months ago.

statisticalmethod cellbasedassays

5.3 match 20 stars 7.00 score 45 scripts

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topconfects:Top Confident Effect Sizes

Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors.

Maintained by Paul Harrison. Last updated 3 months ago.

geneexpression differentialexpression transcriptomics rnaseq mrnamicroarray regression multiplecomparison

4.5 match 14 stars 7.38 score 18 scripts 2 dependents

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ERSSA:Empirical RNA-seq Sample Size Analysis

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

Maintained by Zixuan Shao. Last updated 5 months ago.

immunooncology geneexpression transcription differentialexpression rnaseq multiplecomparison qualitycontrol

7.0 match 4.48 score 1 scripts

sssydysss

TransProR:Analysis and Visualization of Multi-Omics Data

A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

Maintained by Dongyue Yu. Last updated 19 days ago.

4.1 match 174 stars 7.55 score 34 scripts

adrientaudiere

MiscMetabar:Miscellaneous Functions for Metabarcoding Analysis

Facilitate the description, transformation, exploration, and reproducibility of metabarcoding analyses. 'MiscMetabar' is mainly built on top of the 'phyloseq', 'dada2' and 'targets' R packages. It helps to build reproducible and robust bioinformatics pipelines in R. 'MiscMetabar' makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) <doi:10.21105/joss.06038>.

Maintained by Adrien Taudière. Last updated 25 days ago.

sequencing microbiome metagenomics clustering classification visualization amplicon amplicon-sequencing biodiversity-informatics ecology illumina metabarcoding ngs-analysis

4.7 match 17 stars 6.44 score 23 scripts

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tximport:Import and summarize transcript-level estimates for transcript- and gene-level analysis

Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.

Maintained by Michael Love. Last updated 5 months ago.

dataimport preprocessing rnaseq transcriptomics transcription geneexpression immunooncology bioconductor deseq2

2.2 match 137 stars 12.95 score 2.6k scripts 11 dependents

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debrowser:Interactive Differential Expresion Analysis Browser

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

Maintained by Alper Kucukural. Last updated 5 months ago.

sequencing chipseq rnaseq differentialexpression geneexpression clustering immunooncology

3.5 match 61 stars 7.80 score 65 scripts

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mitch:Multi-Contrast Gene Set Enrichment Analysis

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

Maintained by Mark Ziemann. Last updated 4 months ago.

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

3.6 match 16 stars 7.06 score 15 scripts

bioc

TCGAbiolinks:TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.

Maintained by Tiago Chedraoui Silva. Last updated 26 days ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network sequencing survival software bioc bioconductor gdc integrative-analysis tcga tcga-data tcgabiolinks

1.7 match 305 stars 14.45 score 1.6k scripts 6 dependents

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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 4 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

3.9 match 7 stars 6.05 score 3 scripts

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msmsTests:LC-MS/MS Differential Expression Tests

Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition.

Maintained by Josep Gregori i Font. Last updated 5 months ago.

immunooncology software massspectrometry proteomics

4.6 match 5.03 score 15 scripts 1 dependents

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EDASeq:Exploratory Data Analysis and Normalization for RNA-Seq

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

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology sequencing rnaseq preprocessing qualitycontrol differentialexpression

2.2 match 5 stars 10.24 score 594 scripts 9 dependents

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Damsel:Damsel: an end to end analysis of DamID

Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R.

Maintained by Caitlin Page. Last updated 5 months ago.

differentialmethylation peakdetection geneprediction genesetenrichment

3.7 match 5.34 score 20 scripts

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

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

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

geneexpression differentialexpression rnaseq transcriptomics sequencing coverage genesetenrichment go

3.6 match 2 stars 4.90 score 7 scripts

cb4ds

DGEobj.utils:Differential Gene Expression (DGE) Analysis Utility Toolkit

Provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE) analysis (Law (2015) <doi:10.12688/f1000research.9005.3>). The tools include both analysis work-flow and utility functions: mapping/unit conversion, count normalization, accounting for unknown covariates, and more. This is a complement/cohort to the 'DGEobj' package that provides a flexible container to manage and annotate Differential Gene Expression analysis results.

Maintained by Connie Brett. Last updated 2 months ago.

3.4 match 2 stars 5.26 score 30 scripts 1 dependents

bioc

diffcyt:Differential discovery in high-dimensional cytometry via high-resolution clustering

Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.

Maintained by Lukas M. Weber. Last updated 1 months ago.

immunooncology flowcytometry proteomics singlecell cellbasedassays cellbiology clustering featureextraction software

1.7 match 20 stars 9.98 score 225 scripts 5 dependents

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RUVSeq:Remove Unwanted Variation from RNA-Seq Data

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

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology differentialexpression preprocessing rnaseq software

1.7 match 13 stars 9.90 score 482 scripts 5 dependents

pmartr

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

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

Maintained by Lisa Bramer. Last updated 3 days ago.

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

2.0 match 40 stars 7.69 score 144 scripts

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mobileRNA:mobileRNA: Investigate the RNA mobilome & population-scale changes

Genomic analysis can be utilised to identify differences between RNA populations in two conditions, both in production and abundance. This includes the identification of RNAs produced by multiple genomes within a biological system. For example, RNA produced by pathogens within a host or mobile RNAs in plant graft systems. The mobileRNA package provides methods to pre-process, analyse and visualise the sRNA and mRNA populations based on the premise of mapping reads to all genotypes at the same time.

Maintained by Katie Jeynes-Cupper. Last updated 5 months ago.

visualization rnaseq sequencing smallrna genomeassembly clustering experimentaldesign qualitycontrol workflowstep alignment preprocessing bioinformatics plant-science

3.0 match 4 stars 5.00 score 2 scripts

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biobroom:Turn Bioconductor objects into tidy data frames

This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.

Maintained by John D. Storey. Last updated 5 months ago.

multiplecomparison differentialexpression regression geneexpression proteomics dataimport

1.8 match 49 stars 8.22 score 280 scripts 1 dependents

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

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

Maintained by Davide Risso. Last updated 5 months ago.

immunooncology dimensionreduction geneexpression rnaseq software transcriptomics sequencing singlecell

1.3 match 43 stars 10.53 score 190 scripts 6 dependents

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easyRNASeq:Count summarization and normalization for RNA-Seq data

Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package.

Maintained by Nicolas Delhomme. Last updated 5 months ago.

geneexpression rnaseq genetics preprocessing immunooncology

2.5 match 5.43 score 15 scripts 1 dependents

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iSEEde:iSEE extension for panels related to differential expression analysis

This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of differential expression results. This package does not perform differential expression. Instead, it provides methods to embed precomputed differential expression results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications.

Maintained by Kevin Rue-Albrecht. Last updated 4 months ago.

software infrastructure differentialexpression bioconductor hacktoberfest iseeu

2.2 match 1 stars 5.38 score 15 scripts

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metaSeq:Meta-analysis of RNA-Seq count data in multiple studies

The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method

Maintained by Koki Tsuyuzaki. Last updated 5 months ago.

rnaseq differentialexpression sequencing immunooncology

3.3 match 3.30 score 2 scripts

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SpliceWiz:interactive analysis and visualization of alternative splicing in R

The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization.

Maintained by Alex Chit Hei Wong. Last updated 3 days ago.

software transcriptomics rnaseq alternativesplicing coverage differentialsplicing differentialexpression gui sequencing cpp openmp

1.7 match 16 stars 6.41 score 8 scripts

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circRNAprofiler:circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs

R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis.

Maintained by Simona Aufiero. Last updated 5 months ago.

annotation structuralprediction functionalprediction geneprediction genomeassembly differentialexpression

1.8 match 10 stars 5.78 score 5 scripts

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PathoStat:PathoStat Statistical Microbiome Analysis Package

The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis.

Maintained by Solaiappan Manimaran. Last updated 5 months ago.

microbiome metagenomics graphandnetwork microarray patternlogic principalcomponent sequencing software visualization rnaseq immunooncology

1.7 match 8 stars 5.90 score 8 scripts

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gg4way:4way Plots of Differential Expression

4way plots enable a comparison of the logFC values from two contrasts of differential gene expression. The gg4way package creates 4way plots using the ggplot2 framework and supports popular Bioconductor objects. The package also provides information about the correlation between contrasts and significant genes of interest.

Maintained by Benjamin I Laufer. Last updated 5 months ago.

software visualization differentialexpression geneexpression transcription rnaseq singlecell sequencing

2.2 match 4.30 score 3 scripts

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ribosomeProfilingQC:Ribosome Profiling Quality Control

Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis.

Maintained by Jianhong Ou. Last updated 1 months ago.

riboseq sequencing generegulation qualitycontrol visualization coverage

1.9 match 4.88 score 17 scripts

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icetea:Integrating Cap Enrichment with Transcript Expression Analysis

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

Maintained by Vivek Bhardwaj. Last updated 5 months ago.

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

1.8 match 2 stars 5.08 score 7 scripts

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ideal:Interactive Differential Expression AnaLysis

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

Maintained by Federico Marini. Last updated 3 months ago.

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

1.3 match 29 stars 6.78 score 5 scripts

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AWFisher:An R package for fast computing for adaptively weighted fisher's method

Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern.

Maintained by Zhiguang Huo. Last updated 5 months ago.

statisticalmethod software

1.8 match 5 stars 4.70 score 4 scripts

bioc

HTSFilter:Filter replicated high-throughput transcriptome sequencing data

This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions.

Maintained by Andrea Rau. Last updated 5 months ago.

sequencing rnaseq preprocessing differentialexpression geneexpression normalization immunooncology

1.3 match 6.24 score 58 scripts 1 dependents

bioc

ScreenR:Package to Perform High Throughput Biological Screening

ScreenR is a package suitable to perform hit identification in loss of function High Throughput Biological Screenings performed using barcoded shRNA-based libraries. ScreenR combines the computing power of software such as edgeR with the simplicity of use of the Tidyverse metapackage. ScreenR executes a pipeline able to find candidate hits from barcode counts, and integrates a wide range of visualization modes for each step of the analysis.

Maintained by Emanuel Michele Soda. Last updated 5 months ago.

software assaydomain geneexpression high-throughput-screening

2.5 match 1 stars 3.11 score 13 scripts

bioc

PRONE:The PROteomics Normalization Evaluator

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

Maintained by Lis Arend. Last updated 16 days ago.

proteomics preprocessing normalization differentialexpression visualization data-analysis evaluation

1.7 match 2 stars 4.38 score 9 scripts

bioc

coseq:Co-Expression Analysis of Sequencing Data

Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided.

Maintained by Andrea Rau. Last updated 5 months ago.

geneexpression rnaseq sequencing software immunooncology

1.2 match 4.98 score 16 scripts

bioc

saseR:Scalable Aberrant Splicing and Expression Retrieval

saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process BAM files to ASpli counts \item \code{\link{convertASpli}} - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code{\link{calculateOffsets}} - Create an offsets assays for aberrant expression or splicing analysis \item \code{\link{saseRfindEncodingDim}} - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code{\link{saseRfit}} - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing } For information upon how to use these functions, check out our vignette at \url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd} and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \url{https://doi.org/10.1101/2023.06.29.547014}.

Maintained by Alexandre Segers. Last updated 5 months ago.

differentialexpression differentialsplicing regression geneexpression alternativesplicing rnaseq sequencing software

1.1 match 1 stars 4.40 score 1 scripts

bioc

DEGreport:Report of DEG analysis

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

Maintained by Lorena Pantano. Last updated 5 months ago.

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

0.5 match 24 stars 9.42 score 354 scripts 1 dependents

bioc

Rvisdiff:Interactive Graphs for Differential Expression

Creates a muti-graph web page which allows the interactive exploration of differential expression results. The graphical web interface presents results as a table which is integrated with five interactive graphs: MA-plot, volcano plot, box plot, lines plot and cluster heatmap. Graphical aspect and information represented in the graphs can be customized by means of user controls. Final graphics can be exported as PNG format.

Maintained by David Barrios. Last updated 5 months ago.

software visualization rnaseq datarepresentation differentialexpression

1.1 match 4.18 score 2 scripts

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

0.5 match 23 stars 8.05 score 67 scripts

bioc

DESpace:DESpace: a framework to discover spatially variable genes

Intuitive framework for identifying spatially variable genes (SVGs) 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. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the best of our knowledge, it is the only SV approach that allows: - performing a SV test on each individual spatial cluster, hence identifying the key regions of the tissue affected by spatial variability; - jointly fitting multiple samples, targeting genes with consistent spatial patterns across replicates.

Maintained by Peiying Cai. Last updated 5 months ago.

spatial singlecell rnaseq transcriptomics geneexpression sequencing differentialexpression statisticalmethod visualization

0.8 match 4 stars 5.02 score 13 scripts

bioc

multiHiCcompare:Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available

multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner.

Maintained by Mikhail Dozmorov. Last updated 5 months ago.

software hic sequencing normalization

0.5 match 9 stars 7.30 score 37 scripts 2 dependents

bioc

diffHic:Differential Analysis of Hi-C Data

Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.

Maintained by Aaron Lun. Last updated 3 months ago.

multiplecomparison preprocessing sequencing coverage alignment normalization clustering hic curl bzip2 xz-utils zlib cpp

0.5 match 5.58 score 38 scripts

bioc

GDCRNATools:GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC

This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.

Maintained by Ruidong Li. Last updated 5 months ago.

immunooncology geneexpression differentialexpression generegulation genetarget networkinference survival visualization genesetenrichment networkenrichment network rnaseq go kegg

0.5 match 5.64 score 44 scripts

bioc

diffUTR:diffUTR: Streamlining differential exon and 3' UTR usage

The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases.

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

geneexpression

0.5 match 6 stars 5.38 score 9 scripts

bioc

mastR:Markers Automated Screening Tool in R

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

Maintained by Jinjin Chen. Last updated 5 months ago.

software geneexpression transcriptomics differentialexpression visualization

0.5 match 4 stars 5.08 score 3 scripts

bioc

dinoR:Differential NOMe-seq analysis

dinoR tests for significant differences in NOMe-seq footprints between two conditions, using genomic regions of interest (ROI) centered around a landmark, for example a transcription factor (TF) motif. This package takes NOMe-seq data (GCH methylation/protection) in the form of a Ranged Summarized Experiment as input. dinoR can be used to group sequencing fragments into 3 or 5 categories representing characteristic footprints (TF bound, nculeosome bound, open chromatin), plot the percentage of fragments in each category in a heatmap, or averaged across different ROI groups, for example, containing a common TF motif. It is designed to compare footprints between two sample groups, using edgeR's quasi-likelihood methods on the total fragment counts per ROI, sample, and footprint category.

Maintained by Michaela Schwaiger. Last updated 5 months ago.

nucleosomepositioning epigenetics methylseq differentialmethylation coverage transcription sequencing software

0.5 match 4.18 score 7 scripts

bioc

consensusDE:RNA-seq analysis using multiple algorithms

This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation.

Maintained by Ashley J. Waardenberg. Last updated 5 months ago.

transcriptomics multiplecomparison clustering sequencing software

0.5 match 4.00 score 10 scripts

ggloor

aIc:Testing for Compositional Pathologies in Datasets

A set of tests for compositional pathologies. Tests for coherence of correlations with aIc.coherent() as suggested by (Erb et al. (2020) <doi:10.1016/j.acags.2020.100026>), compositional dominance of distance with aIc.dominant(), compositional perturbation invariance with aIc.perturb() as suggested by (Aitchison (1992) <doi:10.1007/BF00891269>) and singularity of the covariation matrix with aIc.singular(). Currently tests five data transformations: prop, clr, TMM, TMMwsp, and RLE from the R packages 'ALDEx2', 'edgeR' and 'DESeq2' (Fernandes et al (2014) <doi:10.1186/2049-2618-2-15>, Anders et al. (2013)<doi:10.1038/nprot.2013.099>).

Maintained by Greg Gloor. Last updated 1 years ago.

0.5 match 2 stars 4.04 score 11 scripts

bioc

tRanslatome:Comparison between multiple levels of gene expression

Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots.

Maintained by Toma Tebaldi. Last updated 5 months ago.

cellbiology generegulation regulation geneexpression differentialexpression microarray highthroughputsequencing qualitycontrol go multiplecomparisons bioinformatics

0.5 match 3.30 score 2 scripts