Analyzing Genomics Data in R with Bioconductor

Slide deck for Stephanie Hicks from the 2018 DC R Conference (https://dc.rstats.ai)

Transcript

1. ANALYZING
   GENOMICS DATA IN R
   WITH
   BIOCONDUCTOR
   Stephanie Hicks
   Assistant Professor, Biostatistics
   Johns Hopkins Bloomberg School of Public Health
   #rstatsdc Conference
   November 8, 2018
   

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2. ABOUT ME
   Teaching: Data Science
   Research: Genomics
   • R/Bioconductor developer
   Other fun things about me:
   • Co-founded R-Ladies Baltimore
   • Creating a children’s book
   featuring women statisticians
   and data scientists
   

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3. COMPREHENSIVE R ARCHIVE NETWORK (CRAN)
   

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4. #rstatsdc
   #rladies
   #rstats
   

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5. https://www.r-project.org/other-projects.html
   

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6. https://www.r-project.org/other-projects.html
   

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7. CRAN, MEET YOUR COUSIN
   BIOCONDUCTOR!
   

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8. • Open-source, open development software project
   • Began in 2001
   • Big priorities: reproducible research and high-quality
   documentation
   • Vignettes
   • Diverse community support
   • Workflows (super helpful for n00bs)
   • Teaching resources
   and open development
   

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10. EXPLORE BIOCONDUCTOR
    STATISTICS WITH BIOCPKGTOOLS
    Functions to access metadata around Bioc packages and
    usage in a tidy data format.
    library(BiocPkgTools)
    pkgs <- biocDownloadStats()
    head(pkgs)
    ## # A tibble: 6 x 6
    ## Package Year Month Nb_of_distinct_IPs Nb_of_downloads repo
    ##
    ## 1 ABarray 2018 Jan 117 150 Software
    ## 2 ABarray 2018 Feb 97 125 Software
    ## 3 ABarray 2018 Mar 102 121 Software
    ## 4 ABarray 2018 Apr 229 359 Software
    ## 5 ABarray 2018 May 99 134 Software
    ## 6 ABarray 2018 Jun 133 209 Software
    

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11. HOW MANY PACKAGES IN
    BIOCONDUCTOR?
    pkgs %>%
    filter(Year==2018) %>%
    select(Package, repo) %>%
    distinct() %>%
    group_by(repo) %>%
    summarize(total_packages=n())
    ## # A tibble: 3 x 2
    ## repo total_packages
    ##
    ## 1 AnnotationData 1124
    ## 2 ExperimentData 400
    ## 3 Software 1733
    • Annotation packages = streamlines tedious bookkeeping
    • Experiment data packages = contains processed data; useful for teaching
    

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12. BIOCONDUCTOR SOFTWARE
    PACKAGES OVER TIME
    ●
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    600
    900
    1200
    1500
    1800
    2010 2012 2014 2016 2018
    Year
    Package count
    Number of Bioconductor Software Packages
    

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13. STANDARD BIOC DATA
    STRUCTURE
    GenomicRanges (GRanges)
    Lee et al. (2018), bioRixv
    

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14. CREATE GRANGES OBJECT
    library(GenomicRanges)
    gr <- GRanges(seqnames = "chr1", strand = c("+", "-"),
    ranges = IRanges(start = c(102012,520211),
    end=c(120303, 526211)),
    gene_id = c(1001,2151),
    score = c(10, 25))
    gr
    ## GRanges object with 2 ranges and 2 metadata columns:
    ## seqnames ranges strand | gene_id score
    ## |
    ## [1] chr1 102012-120303 + | 1001 10
    ## [2] chr1 520211-526211 - | 2151 25
    ## -------
    ## seqinfo: 1 sequence from an unspecified genome
    

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15. THINGS YOU CAN DO WITH
    GRANGES OBJECTS
    width(gr)
    ## [1] 18292 6001
    gr[gr$score > 15, ]
    ## GRanges object with 1 range and 2 metadata columns:
    ## seqnames ranges strand | gene_id score
    ## |
    ## [1] chr1 520211-526211 - | 2151 25
    ## -------
    ## seqinfo: 1 sequence from an unspecified genome
    

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16. GENOMIC VERBS/ACTIONS +
    TIDY DATA = PLYRANGES
    • Goal: Write human readable analysis
    workflows
    • Idea: Define an API (i.e. extend dplyr) that
    maps relational genomic algebra to “verbs”
    that act on ”tidy” genomic data
    • Another great idea: Borrow dplyr’s
    syntax and design principles
    • And another great idea: Compose verbs
    together with pipe operator from magrittr
    Stuart Lee
    Di Cook
    Michael Lawrence
    

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17. library(plyranges)
    gr %>%
    filter(score > 15)
    ## GRanges object with 1 range and 2 metadata columns:
    ## seqnames ranges strand | gene_id score
    ## |
    ## [1] chr1 520211-526211 - | 2151 25
    ## -------
    ## seqinfo: 1 sequence from an unspecified genome
    gr %>%
    filter(score > 15) %>%
    width()
    ## [1] 6001
    

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19. Feel free to send comments/questions:
    Twitter: @stephaniehicks
    Email: shicks19@jhu.edu
    #rstatsdc
    #rladies
    Thank you!
    

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