dexseq2012

Transcript

1. DEXSeq paper discussion
   L Collado-Torres
   December 10th, 2012
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2. 1 Background
   2 DEXSeq paper
   3 Results
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3. Background
   Gene Expression 1
   1Source: http://www.ncbi.nlm.nih.gov/projects/genome/probe/doc/ApplExpression.shtml
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4. Background
   High-Throughput Sequencing 2
   2Source: Metzker, Sequencing technologies — the next generation, 2010, Nat Rev Genet
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5. Background
   Alignment (Mapping) 3
   3Source: Trapnell et al, How to map billions of short reads onto genomes, 2009, Nat Biotech
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6. Background
   What can we find? 4
   4Source: Sorek and Cossart, Prokaryotic transcriptomics a new view on regulation, physiology and pathogenicity, 2010, Nat Rev Genet
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7. Background
   What can we find? 5
   5Source: Sorek and Cossart, Prokaryotic transcriptomics a new view on regulation, physiology and pathogenicity, 2010, Nat Rev Genet
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8. Background
   What can we find? 6
   6Source: Sorek and Cossart, Prokaryotic transcriptomics a new view on regulation, physiology and pathogenicity, 2010, Nat Rev Genet
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9. Background
   What can we find? 7
   7Source: Sorek and Cossart, Prokaryotic transcriptomics a new view on regulation, physiology and pathogenicity, 2010, Nat Rev Genet
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10. DEXSeq paper
    Main ideas
    Compare two or more conditions of interest to find the DE exons (DEX).
    Focus on DE: assume a transcript inventory
    Account for biological variation
    Use GLMs
    Fine tuning to make it fast, control for false positives, and when possible increase power
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11. DEXSeq paper
    Simplifying the exome: counting bins 8
    8Source: Anders, Reyes, Huber; Detecting differential usage of exons from RNA-seq data, 2012, Genome Research
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12. DEXSeq paper
    Model
    Using count data and assume it follows a negative binomial distribution
    Kijl ∼ NB (mean = sj µijl , dispersion = αil ) (1)
    counting bin l
    gene i
    sample j = 1, . . . , m
    size factor sj : needed because each sample is sequenced at a different depth
    αil is the dispersion parameter
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13. DEXSeq paper
    Poisson vs NB 10
    Poisson GLM
    Outcome Y ∼ Poisson(µ)
    Link function: log µ = x β
    Variance function Var(Y ) = Var(µ) = αµ where α = 1. α = 1 is the quasi-likelihood
    approach.
    Negative Binomial Model: Gamma-Poisson mixture construction
    Assume unobserved r.v. E where E ∼ Gamma(θ, 1/θ).
    Mean: θ · 1/θ = 1, Variance: θ · 1/θ2 = 1/θ.
    Assume that Y |E ∼ Poisson(µE)
    Then Y has a negative binomial distribution with mean µ and variance
    µ + µ2/θ = µ(1 + µ/θ) 9
    Variance of Y increases quadratically with the mean rather than linearly.
    9α = 1/θ in the DEXSeq paper
    10Source: 140.654 2012 slides by Roger Peng
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14. DEXSeq paper
    Main log-linear model
    log µijl = βG
    i
    + βE
    il
    + βC
    iρj
    + βEC
    iρj l
    (2)
    βG
    i
    : baseline expression strength of gene i
    βE
    il
    : log of the expected fraction of the reads mapped to gene i that overlap counting bin l
    βC
    iρj
    : log of the fold change in overall expression of gene i under condition ρj
    ρj experimental condition of sample j
    βEC
    iρj l
    : effect condition ρj has on the fraction of reads falling into bin l
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15. DEXSeq paper
    Variability: gene expression + exon usage
    Var. in gene expression: when the total number of transcripts for a gene i differs from the
    expected value under ρj
    Var. in exon usage: using different exons or counting bins
    log µijl = βG
    i
    + βE
    il
    + βS
    ij
    + βEC
    iρj l
    (3)
    Change βC
    iρj
    by βS
    ij
    . Absorbs var. in gene expression.
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16. DEXSeq paper
    Dispersion estimates 11
    11Source: Anders, Reyes, Huber; Detecting differential usage of exons from RNA-seq data, 2012, Genome Research 16 / 23
    

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17. DEXSeq paper
    Analysis of Deviance 12
    Deviance D(ˆ
    β) = 2 ∗ − 2 (ˆ
    β; y) where ∗ is the saturated likelihood
    Two spaces for β: small S (nested) and large L with H0 : β ∈ S and Ha : β ∈ L − S.
    Likelihood ratio
    LR =
    L (ˆ
    βS ; y)
    L (ˆ
    βL; y)
    Under H0, −2 log LR ∼ χ2
    |L|−|S|
    Note D(ˆ
    βS ) − D(ˆ
    βL) = −2[ (ˆ
    βS ; y) − (ˆ
    βL; y)] = −2 log LR
    12Source: 140.654 2012 slides by Roger Peng
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18. DEXSeq paper
    Testing for DEX: ANODEV
    Fit two models
    log µijl = βG
    i
    + βE
    il
    + βS
    ij
    (4)
    log µijl = βG
    i
    + βE
    il
    + βS
    ij
    + βEC
    iρj l
    δll (5)
    where
    δll =
    1 if l = l
    0 otherwise
    Then test using analysis of deviance (ANODEV)
    Control FDR by adjusting p-values using Benjamini-Hochberg’s method.
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19. Results
    Finding DEX: knockdown of pasilla on Drosophila melanogaster example 13
    13Source http://www-huber.embl.de/pub/DEXSeq/analysis/brooksetal/
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20. Results
    Detection power depends on mean 14
    14Source: reproduced with code from http://genome.cshlp.org/content/suppl/2012/08/20/gr.133744.111.DC1/Supp_II.html
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21. Results
    Without considering biological variation 15
    15Source http://www-huber.embl.de/pub/DEXSeq/analysis/brooksetal/
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22. Results
    Interesting comparison
    Mock comparison: check for DEX between replicates from a control condition
    Used an FDR of 10%
    DEXSeq: 8 genes (159 in the real control vs treatment comparison)
    Cuffdiff v 1.3.0: 639 genes (37 in real comp.)
    This trend continues with other data sets.
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23. Results
    Thanks!
    Main source: Anders, Reyes, Huber; Detecting differential usage of exons from RNA-seq
    data, 2012, Genome Research
    PMID: 22722343.
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