Differential expression analysis for sequence count data

By Simon Anders, Wolfgang Huber. Genome Biology, 2010

3,166 citations recorded by the source corpus.

High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f2ce1688d39b5d38cbd994766bedf9e867df2533

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Citations in the corpus

Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. (2015, methodology): “After data acquisition and application to the integrated model network, p values for metabolites and genes were calculated with limma and DESeq R-packages for differential expressions (Anders and Huber, 2010).”
Differential expression in RNA-seq: a matter of depth. (2011, methodology): “Methods traditionally used for microarrays have paved the way to other approaches that take into account the discrete nature of the expression quantification and use different probability distributions to model data (Marioni et al. 2008; Robinson et al. 2010; Anders and Huber 2010; Sultan et al. 200”
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts (2016, methodology): “This is the underlying concept of commonly used methods such as DESeq [3] and trimmed mean ofM values (TMM) normalization [4].”
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019, methodology): “For example, DESeq [23] uses the negative binomial distribution for read count data and links variance and mean by local regression.”
Normalization, testing, and false discovery rate estimation for RNA-sequencing data. (2012, methodology): “Another method, DESeq by Anders and Huber (2010), also uses a negative binomial distribution and uses local regression to estimate the relationship between the variance and the mean.”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, methodology): “More refined normalization schemes have been developed for bulk RNA-seq data (Anders and Huber, 2010).”
svaseq: removing batch effects and other unwanted noise from sequencing data (2014, methodology): “In Step 5, the covariates may be included in a standard linear regression modeling analysis on an appropriately transformed scale or the covariates can be directly used in software that models counts with generalized linear models (GLMs) including edgeR (13) and DESeq (14).”
LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, methodology): “The Wilcoxon rank sum test based on GMPR normalized data and MaAsLin2 perform well under C0 (a binary covariate, Fig.”
N6-methyladenosine Modulates Messenger RNA Translation Efficiency (2015, positive): “analyzed by DESeq (Anders and Huber, 2010) to generate RPKM (reads per kilobase, per million reads).”