apeglm - https://bioconductor.org/packages/release/bioc/html/apeglm.html
ashr - https://github.com/stephens999/ashr, https://cran.r-project.org/web/packages/ashr/index.html
consensusDE - https://bioconductor.org/packages/release/bioc/html/consensusDE.html
DESeq2 - https://bioconductor.org/packages/release/bioc/html/DESeq2.html
edgeR - https://bioconductor.org/packages/release/bioc/html/edgeR.html
limma - https://kasperdanielhansen.github.io/genbioconductor/html/limma.html https://bioconductor.org/packages/release/bioc/html/limma.html
MetaCycle - https://cran.r-project.org/web/packages/MetaCycle/index.html, https://github.com/gangwug/MetaCycle
RUVSeq - https://bioconductor.org/packages/release/bioc/html/RUVSeq.html
SARTools - https://github.com/PF2-pasteur-fr/SARTools
tximport - https://github.com/mikelove/tximport
Comments
Differential expression analysis is a widely used technique in genomics and transcriptomics research that allows researchers to identify genes that are differentially expressed between two or more conditions or groups. There are several tools available for performing differential expression analysis, including:
DESeq2: This R package is widely used for analyzing RNA-Seq data. It uses a negative binomial distribution to model the count data and provides methods for estimating variance and testing for differential expression.
edgeR: Another R package that is commonly used for differential expression analysis of RNA-Seq data. It uses a generalized linear model to model the count data and provides methods for estimating dispersion and testing for differential expression.
limma: A popular R package that is used for differential expression analysis of microarray and RNA-Seq data. It uses a linear model to model the gene expression data and provides methods for estimating variance and testing for differential expression.
NOISeq: This is an R package that is used for differential expression analysis of RNA-Seq data. It uses a non-parametric method based on the relative expression values of the genes and provides a user-friendly interface for performing the analysis.
Cuffdiff: This tool is a part of the Cufflinks suite and is used for differential expression analysis of RNA-Seq data. It uses a Bayesian framework to model the gene expression data and provides methods for estimating variance and testing for differential expression.
DEGSeq: This R package is used for differential expression analysis of RNA-Seq data. It uses a method based on the negative binomial distribution to model the count data and provides methods for estimating variance and testing for differential expression.
These are just a few examples of the many tools available for differential expression analysis. The choice of tool depends on the specific research question, the type of data, and the user's familiarity with the tool.