wfleabase.org/docs/tileMEseq0905.pdf Notes and statistics on base level expression May 2009Don Gilbert Biology Dept., Indiana University

wfleabase.org/docs/tileMEseq0905.pdf 2007: Tile expression DrosMel tiled by Affymetrix, finds new genes (blue) and known (orange).

wfleabase.org/docs/tileMEseq0905.pdf Precision improves ’06-’09 Measuring expression over gene structures, Nimblegen (08) has higher precision than Affy (06/07) RNA-Seq (09) has higher precision than Nimblegen.

wfleabase.org/docs/tileMEseq0905.pdf … microarray statistics for base level expression?

wfleabase.org/docs/tileMEseq0905.pdf Gene or Base expression? Base-level expression (tiles, rna-seq) calculate like gene differential expression (DE) Per tile, per RNA-seq contig or per base: treatment - control Combine for tiles over gene Independent (technically) observations, but biologically related Increase DF, Power with longer gene How to combine? As independent replicates: gene > (tiles, technical, bio replicates)? As nested block: gene > tiles > replicates ? As gene average: gene = mean(tiles) > replicates ? Compare with gene-level stats …

wfleabase.org/docs/tileMEseq0905.pdf Gene or Base expression? Base level tests find expression better than gene average Base level sensitivity= 42%, Gene level sensitivity= 38% Both have specificity= 37% Sensitivity = 1 - false rejection; Specificity = 1 - false discovery

wfleabase.org/docs/tileMEseq0905.pdf Gene or Base expression? DE is consistent over gene span though expression Ave changes; gene-level measure can miss this. Expression over gene span, treatment(red) vs control(green) with 3 replicates

wfleabase.org/docs/tileMEseq0905.pdf … gene structures & expression

wfleabase.org/docs/tileMEseq0905.pdf Sequence normalizing? Idea is to remove sequence (GC) effects on probe hyb. score TileScope ; Royce TE, Rozowsky JS, and Gerstein, MB. (2007). Assessing the need for sequence-based normalization in tiling microarray experiments. Bioinformatics, 23,

wfleabase.org/docs/tileMEseq0905.pdf Sequence normalizing? Sequence-normalizing also removes Exon/Intron signal ! Don’t use it (TileScope’s quantilenorm).. or other sequence adjustments of expression, unless gene structure signals are included.

wfleabase.org/docs/tileMEseq0905.pdf Intron-Exon Detection Nimblegen and Solexa tile/base expression detects gene structure, on average, fairly well.

wfleabase.org/docs/tileMEseq0905.pdf Intron-Exon Update Newest RNA-Seq finds intron/exon very well (Stranded RNA- Seq, modEncode Gingeras lab, March 2009 )

wfleabase.org/docs/tileMEseq0905.pdf Differential expression Gene end (3’) has more expression, but constant differential over gene span, on average. Green is treatment, red control. Line style shows 3 replicates of Daphnia tiled expression. Example genes introns exons

wfleabase.org/docs/tileMEseq0905.pdf Diff. Expr. distributions Introns show a null DE distribution, genes and TAR regions are wider. Use introns as baseline for statistics? GenesIntronsTARs Pred Sex Metal

wfleabase.org/docs/tileMEseq0905.pdf … multiple testing corrections

wfleabase.org/docs/tileMEseq0905.pdf Multiple statistic tests Problem: perform 20,000 tests and p-values hit laws of chance. Pr = 0.05 can happen 1,000 times by chance (false discovery, FDR). DrosMel Affy line t-tests: 2,284,383 / 5,395,023 = 0.42 Sig Bonferroni: conservative = 0.03 Sig Benjamini & Hochberg: p.adjust(p,’BH’) = 0.35 Sig qvalue(p) : distribution based = 0.41 Sig Storey, JD and R Tibshirani, Statistical significance for genomewide studies. PNAS 100: SAM permutation qvalue However, p.adjust meant for 100’s of tests, not Millions Drosmel modEncode case: 1900 pairwise Affy cell line (62 cells) DE comparisons x 14,000 genes = 26,600,000 t-tests

wfleabase.org/docs/tileMEseq0905.pdf Multiple DE tests : Daphnia Much different corrections for experiments on same genes Daphnia DE: 3 expt.s (trt - con), genes, 3 replicates Predate, Metal genes have low expression, important to detect SexPredateMetals P< %P28310 %BH1900 %Qvalue2100 max P|Q1e-21e-4

wfleabase.org/docs/tileMEseq0905.pdf Multiple statistic tests “Statisticians have turned p-value corrections into an industry, but they are really more of a band-aid than a solution”* What about false rejection (FRR; type II error)? Balance errors, false rejection maybe more important Solution #1: test fewer, directed hypotheses Solution #2: measure error rate on knowns, eg. prediction of “known” genes Solution #3: known null hypothesis, eg. introns *

wfleabase.org/docs/tileMEseq0905.pdf 1900 pairwise Affy cell line DE comparisons x 14,000 genes = 26,600,000 t-tests

wfleabase.org/docs/tileMEseq0905.pdf Hypotheses of interest are fewer: ~100s cells x 14,000 genes ~ 2 Million tests

wfleabase.org/docs/tileMEseq0905.pdf Summary 1.Base-level expression (tiles, rna-seq) measures gene expression better Balances sensitivity (false rejection) with specificity (false discovery) 2.Base-level expression measures gene structures well On average, and precision is improving for individual genes. 3.Multiple test corrections are needed but problematic False discovery corrections for millions of tests leads to false rejections. Determine empirical error rates where possible

wfleabase.org/docs/tileMEseq0905.pdf End note Summary pages wfleabase.org/genome-summaries/tile-expression/ insects.eugenes.org/species/data/dmel5/modencode/ Genome expression maps insects.eugenes.org:8091/gbrowse/cgi-bin/gbrowse/drosmelme/ expression in 52 cell lines (affy) and more precise solexa & nimblegen for a few cell lines insects.eugenes.org:8091/gbrowse/cgi-bin/gbrowse/daphnia_pulex8/ expression among 4 treatment groups (sex, metal stress, biotic predator); nimblegen

wfleabase.org/docs/tileMEseq0905.pdf Differential expression Gene models miss much expression Known sex genes capture DE, but unknown regions capture environmental stress expression, in Daphnia.