Differential expression analysis for sequence count data
Open accessAbstract
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.
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Regularization by combining information across similar genes is commonly applied in learning error models for bulk RNA-seq in differential expression analysis, but has not been previously applied in this context for single-cell normalization.
“This approach is commonly applied in learning error models for bulk RNA-seq in the context of differential expression analysis [22–25], but to our knowledge has not been previously applied in this context for single-cell normalization.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [23]
DESeq uses the negative binomial distribution for read count data and links variance and mean by local regression.
“For example, DESeq [23] uses the negative binomial distribution for read count data and links variance and mean by local regression.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [23]
Differentially regulated H3K27ac or STAT3 sites were identified from three (H3K27ac) or two (STAT3) biological replicates using DESeq.
“Differentially regulated H3K27ac or STAT3 sites were identified from three (H3K27ac) or two (STAT3) biological replicates using DESeq65 in the default parameters through HOMER66 (http://homer.salk.edu/homer/).”
From Synergistic gene expression during the acute phase response is characterized by transcription factor assisted loading (2017) · cites this paper as [65]
The capability to demultiplex and identify doublets using natural genetic variation reduces per-sample and per-cell library preparation cost, does not require synthetic barcodes or split-pool strategies, and captures biological variability among individual samples while limiting unwanted technical variability.
“The capability to demultiplex and identify doublets using natural genetic variation reduces the per-sample and per-cell library preparation cost of single-cell RNA-sequencing, does not require synthetic barcodes or split-pool strategies36–40, and captures biological variability among individual samples while limiting unwanted technical variability.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [36]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredNormalization methods TMM, DESeq, PoissonSeq, and UpperQuartile take heterogeneous transcript distributions into account by ignoring highly variable and/or highly expressed features.
“Normalization methods that take this into account are TMM [47], DESeq [48], PoissonSeq [49] and UpperQuartile [45], which ignore highly variable and/or highly expressed features.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [48]
Some differential expression tools can only perform pairwise comparisons, while others (edgeR, limma-voom, DESeq, DESeq2, maSigPro) can perform multiple comparisons, include covariates, or analyze time-series data.
“While some of the differential expression tools can only perform a pair-wise comparison, others such as edgeR [57], limma-voom [55], DESeq [48], DESeq2 [58], and maSigPro [213] can perform multiple comparisons, include different covariates or analyze time-series data.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [48]
Statistical significance of differential expression was based on negative binomial distribution, with P-values adjusted for multiple testing using Benjamini-Hochberg procedure and cutoff 0.1.
“Calculation of statistical significance of gene expression being different (higher) in the apical part of the sponge was based on analysis of the negative binomial distribution of the expression levels 50 .We have used the DESeq 'padj', P-value adjusted for multiple testing with the Benjamini-Hochberg procedure with cutoff r0.1 for calling the expression significantly different.”
From Developmental gene expression provides clues to relationships between sponge and eumetazoan body plans (2014) · cites this paper as [50]
Reproducibility of the results was assessed by Principal Component Analysis and sample-to-sample distances.
“Reproducibility of the results was assessed by Principal Component Analysis and sample-to-sample distances 50 (see supplementary materials for details).”
From Developmental gene expression provides clues to relationships between sponge and eumetazoan body plans (2014) · cites this paper as [50]
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- Authors
- Anders, S., & Huber, W.
- Affiliations
- European Molecular Biology Laboratory (DE)
- Corresponding author
- Anders, Simon
- Venue
- Genome Biology
- Published
- 2010-10
- Volume
- 11
- Issue
- 10
- Pages
- R106-R106
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC3218662
- OpenAlex
- W2152239989
- Cited by
- 15,581
- References
- 49
- Citations by year
- 2026: 444 · 2025: 728 · 2024: 1002 · 2023: 1038 · 2022: 1340 · 2021: 1537 · 2020: 1423 · 2019: 1308 · 2018: 1235 · 2017: 1294 · 2016: 1122 · 2015: 1103 · 2014: 982 · 2013: 604 · 2012: 296
- FWCI
- 131.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Commission
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- differential gene expression, count data, negative binomial distribution, local regression, variance estimation, error model, RNA-seq, ChIP-seq, DESeq, statistical power
- MeSH terms
- Animals, Drosophila, Models, Genetic, Saccharomyces cerevisiae, Stem Cells, Binomial Distribution, Linear Models, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Tissue Culture Techniques, Chromatin Immunoprecipitation, High-Throughput Nucleotide Sequencing
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