RNASeq DE methods review Applied Bioinformatics Journal Club

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Transcript of RNASeq DE methods review Applied Bioinformatics Journal Club

  • 1.Applied Bioinformatics Journal Club Wednesday, March 5

2. 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 3. 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 4. Theoretical background Count matrixnumber 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 5. Normalization Commonly used RPKM FPKM Biasesproportional 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 Cuffdiffextension of DESeq normalization Intra-condition library scaling Second scaling between conditions Also accounts for changes in isoform levels 6. 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 7. 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 8. 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 9. 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 isoformsimilar to DESeq Multiple isoform mixed model of negative binomial and beta distributions 10. 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 11. 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 12. 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) 13. 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 PoissonSeqpermutation based FDR 14. 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 15. 5 16. 5