mapping2015

Transcript

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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