Journal club slides to discuss "Differential analysis of gene regulation at transcript resolution with RNA-seq" (2014).
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The document discusses the differential analysis of gene regulation using RNA-seq, specifically highlighting the limitations of cuffdiff2 compared to ballgown, which demonstrates greater sensitivity in identifying truly differentially expressed transcripts. The conservative bias of cuffdiff2 is attributed to its normalization for transcript length, affecting its accuracy in statistical significance. Ballgown allows for modeling continuous covariates and has been validated against biological samples, showing improved performance in detecting transcript-SNP pairs.
Journal club slides to discuss "Differential analysis of gene regulation at transcript resolution with RNA-seq" (2014).
- 1. Differential analysis of gene regulation at transcript resolution with RNA-seq (2014)! ! Alyssa C Frazee, Geo Pertea, Andrew E Jaffe, Ben Langmead, Steven L Salzberg, Jeffrey T Leek Preprint available at http://biorxiv.org/content/biorxiv/early/ 2014/03/30/003665.full.pdf
- 2. Cuffdiff2 is over conservative by comparison! Simulated data:! 274 transcripts differentially expressed! 0 were called by Cuffdiff2! 80 were called by Ballgown! ! “78 of the top 100 transcripts called differentially expressed were truly differentially expressed for Ballgown versus 63 for Cuffdiff2, a 23% increase in truly differentially expressed genes (Figure 2d).”! !
- 3. Cuffdiff2 is over conservative by comparison! ! ! “We further investigated the source of the conservative bias of Cuffdiff2 and found that when we sampled reads with equal probability from each transcript, ignoring transcript length, Cuffdiff2 produced accurate measures of statistical significance (Supplmentary Figure 1). This result suggests that the conservative bias may be due to transcript length normalization in the Cuffdiff2 software.”!
- 4. A recent evaluation using biological samples in which expression has been confirmed with qRT-PCR agrees! “Cuffdiff has reduced sensitivity and specificity as measured by ROC analysis as well as the significant number of false positives in the null model test. We postulate that this is related to its normalization procedure, which attempts to account for both alternative isoform expression and length of transcripts.”! ! http://www.biomedcentral.com/ content/pdf/gb-2013-14-9-r95.pdf! !
- 5. Ballgown can model continuous covariants! Example: RNA quality or RNA Integrity Number (RIN) as a continuous covariant
- 6. Ballgown can be used with other standard DE tools! Example: eQTL with MatrixEQTL for 464 samples! ! filters:! Transcripts with FPKM > 0.1! SNPs with minor allele frequency < 5%! Cis eQTLs within 1000kb “57% and 78% of transcript-SNP pairs significant at FDR of 1% appeared in the list of significant transcript eQTL identified in the original analysis of the EUR and YRI populations individually. 14% of eQTL pairs were identified for transcripts that did not overlap Ensembl annotated transcripts (Figure 4).
- 7. Ballgown pipeline runtimes for the Geuvadis datasets
- 8. FPKM based (e.g. Cufflinks) vs Average Coverage based (e.g. DESeq and edgeR)! FPKM (length normalized) vs average coverage (a count-based measure of expression though not the raw counts that DESeq and edgeR take as input)! Geuvadis! Simulated! Simulated! Simulated (avg.cov.)! Simulated (FPKM)! Similar