mapping2015

Transcript

1. Does mapping simulated RNA-seq reads provide information? Leonardo Collado-Torres tweet:

   @fellgernon blog: tinyurl.com/FellBit

2. Previously • Choose 10 genes with FPKM > 20 •

   cufflinks: estimate isoform FPKM from 7 Geuvadis samples • polyester: simulate with uniform & rnaf models • Map with TopHat • View coverage https://github.com/alyssafrazee/polyester_code/blob/master/polyester_manuscript.Rmd

3. https://github.com/alyssafrazee/polyester_code/blob/master/polyester_manuscript.Rmd

4. Goals • Reproduce • Similar behavior in other genes/samples? •

   Do simulated reads predict observed data? Get a measure by gene • Even more than by chance?
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6. Original

7. Reproduced

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12. Measures by gene: using single bp exon • Correlation (R^2)

    • RMSD: scaling by max first • ARIMA: forecast models – Auto ARIMA on obs – Fit again using simulated data as predictor – P-value for predictor and estimated coef • Chance: – Neg. binomial size 1 and 6 – Compare replicates of simulated data

13. • d: default params, 2 reps (2x) • r: using

    rnaf bias, 2x • b1: neg. binomial with size = 1, 2x • b6: neg. binom. size = 6, 2x • d1-d2: using d1 as “obs” – Same for r1-r2, b1a-b1b, b6a-b6b

14. Correlation

15. Correlation: summarize by sample

16. Correlation: summarize by gene

17. R^2

18. Scaling by max then RMSD

19. Scaling by max then RMSD

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22. ARIMA: predictor p-value

23. ARIMA: predictor p-value

24. ARIMA: predictor coefficient

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27. Code • https://github.com/alyssafrazee/polyest er_code/blob/master/polyester_manuscri pt.Rmd • https://github.com/lcolladotor/mapBias (private for now)

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