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mapping2015

 mapping2015

Leonardo Collado-Torres

February 23, 2015
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  1. Does mapping simulated RNA-seq reads
    provide information?
    Leonardo Collado-Torres
    tweet: @fellgernon blog: tinyurl.com/FellBit

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  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

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

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  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

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  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

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  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

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  14. Correlation

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  15. Correlation: summarize by sample

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  16. Correlation: summarize by gene

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  17. R^2

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  18. Scaling by max then RMSD

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  19. Scaling by max then RMSD

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  22. ARIMA: predictor p-value

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  23. ARIMA: predictor p-value

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  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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