Published on March 5, 2014
Applied Bioinformatics Journal Club Wednesday, March 5
Background • Comparison of commonly used DE software packages – – – – – – Cuffdiff edgeR DESeq PoisssonSeq baySeq limma • Two benchmark datasets – Sequencing Quality Control (SEQC) dataset • Includes qRT-PCR for 1,000 genes – Biological replicates from 3 cell lines as part of ENCODE project
Focus of paper: Comparison of elevant measures for DE detection • Normalization of count data • Sensitivity and specificity of DE detection • Genes expressed in one condition but no expression in the other condition • Sequencing depth and number of replicates
Theoretical background • Count matrix—number of reads assigned to gene i in sequencing experiment j • Length bias when measuring gene expression by RNA-seq – Reduces the ability to detect differential expression among shorter genes • Differential gene expression consists of 3 components: – Normalization of counts – Parameter estimation of the statistical model – Tests for differential expression
Normalization • Commonly used – RPKM – FPKM – Biases—proportional representation of each gene is dependent on expression levels of other genes • DESeq-scaling factor based normalization – median of ratio for each gene of its read count over its geometric mean across all samples • Cuffdiff—extension of DESeq normalization – Intra-condition library scaling – Second scaling between conditions – Also accounts for changes in isoform levels
Normalization • edgeR – Trimmed means of M values (TMM) – Weighted average of subset of genes (excluding genes of high average read counts and genes with large differences in expression) • baySeq – Sum gene counts to upper 25% quantile to normalize library size • PoissonSeq – Goodness of fit estimate to define a gene set that is least differentiated between 2 conditions, and then used to compute library normalization factors
Normalization • limma (2 normalization procedures) – Quantile normalization Sorts counts from each sample and sets the values to be equal to quantile mean from all samples – Voom: LOWESS regression to estimate mean variance relation and transforms read counts to log form for linear modeling
Statistical modeling of gene expression • edgeR and DESeq – Negative binomial distribution (estimation of dispersion factor) • edgeR – Estimation of dispersion factor as weighted combination of 2 components • Gene specific dispersion effect and common dispersion effect calculated for all genes
Statistical modeling of gene expression • DESeq – Variance estimate into a combination of Poisson estimate and a second term that models biological variability • Cuffdiff – Separate variance models for single isoform and multiple isoform genes • Single isoform—similar to DESeq • Multiple isoform– mixed model of negative binomial and beta distributions
Statistical modeling of gene expression • baySeq – Full Bayesian model of negative binomial distributions – Prior probability parameters are estimated by numerical sampling of the data • PoissonSeq – Models gene counts as a Poisson variable – Mean of distribution represented by log-linear relationship of library size, expression of gene, and correlation of gene with condition
Test for differential expression • edgeR and DESeq – Variation of Fisher exact test modified for negative binomial distribution – Returns exact P value from derived probabilities • Cuffdiff – Ratio of normalized counts between 2 conditions (follows normal distribution) – t-test to calculate P value
Test for differential expression • limma – Moderated t-statistic of modified standard error and degrees of freedom • baySeq – Estimates 2 models for every gene • No differential expression • Differential expression – Posterior likelihood of DE given the data is used to identify differentially expressed genes (no P value)
Test for differential expression • PoissonSeq – Test for significance of correlation term – Evaluated by score statistics which follow a Chisquared distribution (used to derive P values) • Multiple hypothesis corrections – Benjamini-Hochberg – PoissonSeq—permutation based FDR
Results • Normalization and log expression correlation • Differential expression analysis • Evaluation of type I errors • Evaluation of genes expressed in one condition • Impact of sequencing depth and replication on DE detection
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