RNASeq DE methods review Applied Bioinformatics Journal Club

100 %
0 %
Information about RNASeq DE methods review Applied Bioinformatics Journal Club

Published on March 5, 2014

Author: kstatebioinformatics

Source: slideshare.net

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



Add a comment

Related presentations

Related pages

Applied Bioinformatics Journal Club Pacbio RNA-Seq

1.Journal Club A single-molecule long-read survey of ... Share Applied Bioinformatics Journal Club Pacbio ... RNASeq DE methods review Applied ...
Read more

Introduction to RNA-seq and RNA-seq Data Analysis (UEB-UAT ...

... NORMALIZATION METHODS DIFFERENTIAL EXPRESSION TESTING 5 Complements . ... RNASeq DE methods review Applied Bioinformatics Journal Club ... Rnaseq ...
Read more

Applied Bioinformatics | LinkedIn

View 699 Applied Bioinformatics ... in machine learning applied to bioinformatics and ... DE methods review Applied Bioinformatics Journal Club.
Read more

Applied Bioinformatics - Documents

Applied Bioinformatics. Week 3. Theory I. Similarity Dot plot. 3.2 On sequence alignment Sequence alignment is the most important task in bioinformatics!.
Read more

Latest Regression techniques for Microarray data

... so I can help create an overview of the contemporary methods. ... lab's next journal club that ... that can be applied to bioinformatics ...
Read more

CiteULike: Applied bioinformatics for the identification ...

Applied bioinformatics for the identification of regulatory elements. by: Wyeth W. Wasserman, ... Bioinformatics's tags for this article. new; Citations ...
Read more

CiteULike: Applied bioinformatics for the identification ...

Applied bioinformatics for the identification of regulatory elements. by: Wyeth W. Wasserman, ... cis-regulatory prediction review; Citations (CiTO)
Read more

Bioinformatics Core Staff — Kentucky Biomedical Research ...

KBRIN-Sponsored Bioinformatics Core; Bioinformatics Core Staff; ... KBRIN Bioinformatics Journal Club; ... sequencing data using RNASeq-MATS. Methods in ...
Read more