lcg2014

Transcript

1. Fast differential expression analysis
   annotation-agnostic across groups with
   biological replicates
   Leonardo Collado-Torres
   tweet: @fellgernon blog: bit.ly/FellBit
   

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2. @fellgernon
   #LCG2014
   Field overview
   Ultimate Goal
   What is the biological (genomic) cause, if any,
   of X disease?
   Currently
   What are the most likely genomic difference(s)
   between two+ groups?
   

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3. @fellgernon
   #LCG2014
   Tools
   •  Molecular biology: reverse transcriptase
   •  High-throughput sequencing
   •  $$ and
   – > Large number of biological replicates
   •  Computers
   •  Biostatistics
   Image: http://bit.ly/15MVhSU
   

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4. @fellgernon
   #LCG2014
   Mapping reads from mRNAs
   Trapnell  et  al.  Nat.  Biotech  2009  
   

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5. @fellgernon
   #LCG2014
   Mapping result: our initial data
   n  samples  à  
   3  billion  nt    
   Adapted from @leekgroup
   

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6. @fellgernon
   #LCG2014
   Split by chromosome and filter
   n  samples  à  
   ~760  million  nt    
   Rows with at least 1 sample with coverage > 5
   

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7. @fellgernon
   #LCG2014
   Test for base-level DE
   Adapted from @andrewejaffe
   

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8. @fellgernon
   #LCG2014
   F-statistic at each base-pair
   •  Null model
   •  Alternative Model
   

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9. @fellgernon
   #LCG2014
   Threshold on F-statistics
   F-­‐sta6s6c  corresponding  to  
   p-­‐value  <  10-­‐8    (F5,30
   )  
   Adapted from @andrewejaffe
   

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10. @fellgernon
    #LCG2014
    Q-values: qvalue::qvalue
    Permute model matrices and find null
    regions for all chromosomes.
    

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11. @fellgernon
    #LCG2014
    How can we make it fast?
    •  Avoid Input/Output as much as possible
    •  Work by chromosome
    •  Reduce memory
    –  Run Length Encoding (IRanges::Rle)
    0000111111222 = (0, 1, 2)
    (4, 6, 3)
    •  Use multiple cores (parallel::mclapply)
    –  Split data to use cores efficiently
    •  Calculate F-stats using Rcpp
    

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12. @fellgernon
    #LCG2014
    Public datasets
    •  derfinderExample:
    –  Blood CEU vs YRI non-related individuals
    •  derHippo:
    –  Brain hippocampus from cocaine addicts, alcohol
    addicts, and controls
    •  derSnyder:
    –  Michael Snyder time course (~1 year):
    2 x diseases, recovery & healthy periods
    •  derStem:
    –  5 stem cell types, 2 replicates per group
    

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13. @fellgernon
    #LCG2014
    derSnyder: cluster #1
    

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14. @fellgernon
    #LCG2014
    derHippo: cluster #4
    

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15. @fellgernon
    #LCG2014
    derHippo: vs original paper
    

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16. @fellgernon
    #LCG2014
    Time and memory needed: derSnyder
    •  Load & filter data: 10 cores with mclapply
    1hr 15min, 177 GB
    •  Make models: 20 min, 52 GB
    •  Analysis: 10 permutations, 4 cores each chr,
    total 59 mins
    –  chr1 41 min, 46 GB
    •  Merging: 30 min, 22 GB
    •  Report: 27 min, 17 GB
    •  Total wallclock time: 3 hr 46 min
    20 samples
    

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17. @fellgernon
    #LCG2014
    A richer data set: 69 samples
    •  Load raw data: each chr, total 1hr 28 min
    –  chr1 1hr 28 min, 18 GB
    –  Merge 1hr 7 min, 67 GB
    •  Filter data: each chr, total 12 min
    –  chr1 12 min, 10 GB
    –  Merge 1hr, 62 GB
    •  Make models: 1 hr 49 min, 234 GB
    •  Analysis: 0 permutations, 8 cores each chr, 52 min
    (1 hr 41 min)
    –  chr1 49 min, 258 GB, had to run twice
    •  Merging: 1 hr 6 min, 46 GB
    •  Report: 1hr 29 min, 45 GB
    •  Total wallclock time: 9 hr 3 min (9 hr 52 min)
    

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18. @fellgernon
    #LCG2014
    derfinder R package
    https://github.com/lcolladotor/derfinder
    https://github.com/lcolladotor/derfinderReport
    https://github.com/lcolladotor/derfinderExample
    

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19. @fellgernon
    #LCG2014
    Acknowledgements
    Leek Group
    Jeffrey Leek
    Alyssa Frazee
    Hopkins
    Sarven Sabunciyan
    Ben Langmead
    Lieber Institute (LIBD)
    Andrew Jaffe
    Harvard
    Rafael Irizarry
    Funding
    NIH
    LIBD
    CONACyT México
    

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