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\begin{document}

\title{R / Bioconductor for Analysis and
  Comprehension of High-Throughput Sequence Data}
\author{Martin T. Morgan (\url{mtmorgan@fhcrc.org}) \\
  Fred Hutchinson Cancer Research Center \\
  Seattle, WA, USA}
\date{3 February 2014}

\maketitle

\section*{Introduction}

\begin{frame}{Overview} 
  \begin{enumerate}
  \item Introduction to \R{} and \Bioconductor{}
  \item Sequencing work flows
  \item Successful computational biology software
  \item Exemplars: algorithms into actions
  \item Challenges \& opportunities
  \end{enumerate}
\end{frame}

\begin{frame}{Introduction: \Bioconductor}
  Analysis and comprehension of high-throughput genomic data
  \begin{itemize}
  \item \url{http://bioconductor.org}
  \item $>11$ years old, $749$ packages
  \end{itemize}
  Themes
  \begin{itemize}
  \item Rigorous statistics
  \item Reproducible work flows
  \item Integrative analysis
  \end{itemize}
\end{frame}

\begin{frame}{Introduction: \Bioconductor}
  \begin{columns}
    \column{.5\textwidth}
    \begin{itemize}
    \item 1341 PubMed full-text citations in trailing 12 months
    \item 28,000 web visits / month; 75,000 unique IP downloads / year
    \item Annual conferences; courses; active mailing list; \ldots
    \end{itemize}
    \column{.5\textwidth}
    \includegraphics[width=\textwidth]{figures/usage-geography}
  \end{columns}
  \bigskip\par
  \textbf{\Bioconductor{} Conference}, July 30 - Aug 1, Boston, USA
\end{frame}

\begin{frame}{Introduction: What is \Bioconductor{} good for?}
  \begin{itemize}
  \item Microarrays: expression, copy number, SNPs, methylation, \ldots
  \item Sequencing: RNA-seq, ChIP-seq, called variants, \ldots 
    \begin{itemize}
    \item Especially \emph{after} assembly / alignment
    \end{itemize}
  \item Annotation: genes, pathways, gene models (exons, transcripts,
    etc.), \ldots
  \item Flow cytometry, proteomics, image analysis, high-throughput
    screens, \ldots
  \end{itemize}
\end{frame}

\section*{Sequencing work flows}

\begin{frame}[fragile]{Introduction: \R}
  \begin{columns}
    \column{.45\textwidth}
    \begin{itemize}
    \item \url{http://r-project.org}
    \item Open-source, statistical programming language; widely used in
      academia, finance, pharma, \ldots
    \item Core language and base packages
    \item Interactive sessions, scripts
    \item $>5000$ contributed packages
    \end{itemize}
    \column{.55\textwidth}
<<eval=FALSE, tidy=FALSE>>=
## Two 'vectors'
x <- rnorm(1000)
y <- x + rnorm(1000, sd=.5)
## Integrated container
df <- data.frame(X=x, Y=y)
## Visualize
plot(Y ~ X, df)
## Regression; 'object'
fit <- lm(Y ~ X, df)
## Methods on the object
abline(fit) # regression line
anova(fit)  # ANOVA table
@ 
  \end{columns}
\end{frame}

\begin{frame}[fragile]{Sequencing: Work flows}
  \begin{columns}
    \column{.5\textwidth}
    \begin{enumerate}
    \item Experimental design
    \item `Wet lab' sample prep
    \item Sequencing
        \begin{itemize}
        \item 100's of millions of reads
        \item 30-150 nucleotides
        \item Single and paired-end
        \item Bar codes, lanes \& flow cells
        \end{itemize}
    \item Alignment
    \item Analysis: DNA, RNA, epigenetics, integrative, microbiome,
      \ldots
    \end{enumerate}
    \column{.5\textwidth}
    \includegraphics[width=\textwidth,height=!]{figures/Solexa-bridge-pcr.jpg}
    \par Bentley et al., 2008, Nature 456:
    \href{http://www.ncbi.nlm.nih.gov/pubmed/18987734}{53-9}
  \end{columns}
\end{frame}

{\scriptsize\begin{verbatim}
@ERR127302.1703 HWI-EAS350_0441:1:1:1460:19184#0/1
CCTGAGTGAAGCTGATCTTGATCTACGAAGAGAGATAGATCTTGATCGTCGAGGAGATGCTGACCTTGACCT
+
HHGHHGHHHHHHHHDGG<GDGGE@GDGGD<?B8??ADAD<BE@EE8EGDGA3CB85*,77@>>CE?=896=:
@ERR127302.1704 HWI-EAS350_0441:1:1:1460:16861#0/1
GCGGTATGCTGGAAGGTGCTCGAATGGAGAGCGCCAGCGCCCCGGCGCTGAGCCGCAGCCTCAGGTCCGCCC
+
DE?DD>ED4>EEE>DE8EEEDE8B?EB<@3;BA79?,881B?@73;1?########################
@ERR127302.1705 HWI-EAS350_0441:1:1:1460:13054#0/1
AAAACACCCTGCAATCTTTCAGACAGGATGTTGACAATGCGTCTCTGGCACGTCTTGACCTTGAACGCAAAG
+
EEDEE>AD>BBGGB8E8EEEGBGGGGBGGGGG3G>E3*?BE??BBC8GB8??:??GGDGDDD>D>B<GDDC8
@ERR127302.1706 HWI-EAS350_0441:1:1:1460:14924#0/1
CACCCAGTGGGGTGGAGTCGGAGCCACTGGTCCTGCTGCTGGCTGCCTCTCTGCTCCACCTTGTGACCCAGG
+
HHHHHGEEGEEADDGDBG>GGD8EG,<6<?AGGADFEHHC@>D@<@G@>AB@B?8AA>CE@D8@B=?CC>AG
@ERR127302.1707 HWI-EAS350_0441:1:1:1461:6983#0/1
CGACGCTGACACCGGAACGGCAGCAGCAGCAGGACGATTAAGACAAGGAGGATGGCTCCACAGACGCTCATG
+
GEEGEGE@GGGGGGEGGGGGBB>G3?33?8*;;79?<9@?DD8@DDEE888;-BB?.A##############
@ERR127302.1708 HWI-EAS350_0441:1:1:1461:10827#0/1
AAAGAAGGTCCTTGCAATAGACTGCCTCTGCTTGAGAACTTATGATGTAATTATTGCATGCTGCTAATATAC
+
GGGGGDDEBFGGGGGBE,DAGDDGGGEEEG<EEFDECFFEEEDE@<>ACEBEFDEEFE<EDC@E<EECCBEB
@ERR127302.1709 HWI-EAS350_0441:1:1:1461:7837#0/1
CAGCCACAGAACCACGGCACGGAAGACATGAGGCAGCATGCTCACGAGAGAGGTGAGGGTCTCCCCTCCAGG
+
HHGHHHH>DH:@.7@49;88G8>G>DDG@D>D@G@GE>@DDBDDG<A82?######################
\end{verbatim}}

\begin{frame}[fragile]{Sequencing: The \Biocpkg{ShortRead} package}
<<ShortRead, eval=FALSE, tidy=FALSE>>=
## Use the 'ShortRead' package
library(ShortRead)
## Create an object to represent a sample from a file
sampler <- FastqSampler("ERR127302_1.fastq.gz")
## Apply a method to yield a random sample
fq <- yield(sampler)
## Access sequences of sampled reads using `sread()`
## Summarize nucleotide use by cycle
## 'abc' is a nucleotide x cycle matrix of counts
abc <- alphabetByCycle(sread(fq))
## Subset of interesting nucleotides
abc <- abc[c("A", "C", "G", "T", "N"),]
@     
\end{frame}

\begin{frame}[fragile]{Sequencing: The \Biocpkg{ShortRead} package}
  \begin{columns}
    \column{.5\textwidth}
<<ShortRead-vis, eval=FALSE, tidy=FALSE>>=
## Create a plot from a
## matrix
matplot(t(abc), type="l",
  lty=1, lwd=3, 
  xlab="Cycle",
  ylab="Count",
  cex.lab=2)
## Add a legend
legend("topright", 
  legend=rownames(abc),
  lty=1, lwd=3, col=1:5,
  cex=1.8)
@     
    \column{.5\textwidth}
    \includegraphics[width=\textwidth]{figures/abc}
  \end{columns}
\end{frame}

\begin{frame}{Sequencing: Essential packages and classes}
  \begin{itemize}
  \item \Biocpkg{Biostrings} and \Rclass{DNAStringSet}
  \item \Biocpkg{GenomicRanges} and \Rclass{GRanges}
  \item \Biocpkg{GenomicFeatures} and \Rclass{TranscriptDb}
  \item \Biocpkg{VariantAnnotation} and \Rclass{VCF}
  \item Input and output: \Biocpkg{rtracklayer} (WIG, BED, etc.),
    \Biocpkg{Rsamtools} (BAM), \Biocpkg{ShortRead} (FASTQ) file input
  \end{itemize}
\end{frame}

\section*{Principles}

\begin{frame}{Principles: Some key points}
  \begin{itemize}
  \item \R{} is a high-level programming language, so lots can be
    accomplished with just a little code
  \item Packages such as \Biocpkg{ShortRead} provide a great way to
    benefit from the expertise of others (and to contribute your own
    expertise back to the community!)
    \begin{itemize}
    \item The path from `user' to `developer' is not that long, and
      has been taken by many!
    \end{itemize}
  \item Objects and methods such as \Rclass{data.frame},
    \Rclass{ShortReadQ} and \Rcode{alphabetByCycle()}) help to manage
    complicated data
    \begin{itemize}
    \item Reducing possibility for clerical and other mistakes
    \item Facilitating inter-operability between different parts of an
      analysis
    \end{itemize}
  \item Scripts make work flows reproducible
  \item Visualizing data is an important part of exploratory analysis
  \end{itemize}
\end{frame}

\begin{frame}{Principles: Successful computational biology software}
  \begin{enumerate}
  \item Extensive: software, annotation, integration
      \begin{itemize}
      \item 750 inter-operable \Bioconductor{} packages
      \end{itemize}
  \item Statistical: volume, technology, experimental design
      \begin{itemize}
      \item \R{} a `natural' for statistical analysis
      \end{itemize}
  \item Reproducible: long-term, multi-participant science
      \begin{itemize}
      \item Objects, scripts, vignettes, packages, \ldots encourage
        reproducible research
      \end{itemize}
  \item Leading edge: novel, technology-driven
      \begin{itemize}
      \item Packages and user community closely track leading edge
        science
      \end{itemize}
  \item Accessible: affordable, transparent, usable
      \begin{itemize}
      \item \Bioconductor{} is free and open, with extensive
        documentation and an active and supportive user community
      \end{itemize}
  \end{enumerate}
  Case study: differential expression of known genes; see also
  \href{http://bioconductor.org/help/course-materials/2013/EMBOBGI/reproducible-research.pdf}{reproducible
    research} lecture.
\end{frame}

\section*{Exemplars}

\begin{frame}{Exemplars: Algorithms to action}
  \begin{enumerate}
  \item Batch effects
  \item Methylation
  \item RNA-seq Differential Representation
  \item Visualization
  \end{enumerate}
\end{frame}

\begin{frame}{Exemplar: Differential Representation}
  Haglund et al., 2012
  \href{http://www.ncbi.nlm.nih.gov/pubmed/23024189}{J Clin Endocrin
    Metab}
  \bigskip\par
  \begin{columns}
    \column{.5\textwidth}
    \begin{itemize}
    \item Scientific finding: identify genes whose expression is
      regulated by estrogen receptors in parathyroid adenoma cells
    \item Statistical challenges: between-sample normalization;
      appropriate statistical model; efficient estimation; \ldots
    \end{itemize}
    \column{.5\textwidth}
    \includegraphics[width=\textwidth]{figures/DESeq2_parathyroid-plotMApadjchange.png}
  \end{columns}
  \bigskip\par\Bioconductor{} support: \Biocpkg{DESeq2}, \Biocpkg{edgeR}, many
  statistical `lessons learned' from microarrays; extensive
  integration with down-stream tools
\end{frame}

\begin{frame}{Exemplar: Batch Effects}
  Leek et al., 2010, Nature Reviews Genetics 11,
  \href{http://www.nature.com/nrg/journal/v11/n10/abs/nrg2825.html}{733-739},
  Leek \& Story
  \href{http://dx.doi.org/10.1371/journal.pgen.0030161}{PLoS Genet
    3(9): e161}
  \begin{columns}
    \column{.5\textwidth} 
    \begin{itemize}
    \item Scientific finding: pervasive batch effects
    \item Statistical insights: surrogate variable analysis: identify
      and build surrogate variables; remove known batch effects
    \item Benefits: reduce dependence, stabilize error rate estimates,
      and improve reproducibility
    \end{itemize}
    \Bioconductor{} support: \Biocpkg{sva}
    \column{.5\textwidth}
    \only<1>{
      \includegraphics[width=\textwidth]{figures/nrg2825-f2.jpg}
      \par{\small HapMap samples from one facility, ordered by
        date of processing.  From }
    }
    \only<2>{
      \begin{enumerate}
      \item Remove signal due to variable(s) of interest
      \item Identify subset of genes driving orthogonal signatures
        of EH
      \item Build a surrogate variable based on full EH signature of
        that subset
      \item Include significant surrogate variables as covariates
      \end{enumerate}
      EH: expression heterogeneity
    }
  \end{columns}
\end{frame}

\begin{frame}{Exemplar: Methylation}
  Hansen et al., 2011, Nature Genetics 43, 
  \href{http://www.nature.com/ng/journal/v43/n8/full/ng.865.html}{768-775}
  \begin{itemize}
  \item Scientific finding: stochastic methylation variation of
    cancer-specific de-methylated regions (DMR), distinguishing cancer from
    normal tissue, in several cancers.
  \item Statistical challenges: smoothing, non-specific filtering, $t$
    statistics, find DMRs
  \end{itemize}
  \bigskip\par
  \includegraphics[width=\textwidth]{figures/bsseq_analysis-1.png}
  \medskip\par \Bioconductor{} support: whole-genome (\Biocpkg{bsseq})
  or reduced representation (\Biocpkg{MethylSeekR}) bisulfite
  sequencing; Illumina 450k arrays (\Biocpkg{minfi})
\end{frame}

\begin{frame}{Exemplar: Visualization}
  \begin{columns}
    \column{.5\textwidth}
    \Biocpkg{Gviz}\par
    \only<1-2>{
    \begin{itemize}
    \item Track-like visualizations
    \item Data panels
    \item Fully integrated with \Bioconductor{} sequence
      representations
    \end{itemize}
    }
    \Biocpkg{ggbio}\par
    \only<3>{
    \begin{itemize}
    \item Comprehensive visualizations
    \item \Rfunction{autoplot} file and data types
    \item Fully integrated with \Bioconductor{} sequence
      representations
    \end{itemize}
    }
    \Biocpkg{epivizr}\par
    \only<4>{
      \begin{itemize}
      \item Genome browser with socket communication to \R{}
      \item Fully integrated with \Bioconductor{} sequence
        representations
      \end{itemize}
    }
    \column{.5\textwidth}
    \only<1>{\includegraphics[width=\textwidth]{figures/Gviz-vignette-1.png}}
    \only<2>{\includegraphics[width=\textwidth]{figures/Gviz-vignette-2.png}}
    \only<3>{\includegraphics[width=\textwidth]{figures/ggbio-vignette-1.png}}
    \only<4>{\includegraphics[width=\textwidth]{figures/epivisr.png}}
  \end{columns}
\end{frame}

\section*{Challenges \& Opportunities}

\begin{frame}{Challenges \& Opportunities}
  \begin{itemize}
  \item Big data -- transparent management within \R, facile use of
    established resources
  \item Developer and user training
  \end{itemize}
  Resources
  \begin{itemize}
  \item \url{http://r-project.org}, \emph{An Introduction to \R}
    manual; Dalgaard, \emph{Introductory Statistics with \R};
    \href{http://rfordummies.com/}{\R{} for Dummies}
  \item \url{http://bioconductor.org/}
  \item \url{http://rstudio.org}
  \item 
    \href{http://stackoverflow.com/questions/tagged/r}{StackOverflow},
    \Bioconductor{}
    \href{http://bioconductor.org/help/mailing-list/mailform/}{mailing
      list}
  \end{itemize}
\end{frame}

\section*{Acknowledgements}

\begin{frame}{Acknowledgements}
  \begin{itemize}
  \item \Bioconductor{} team: Marc Carlson, Valerie Obenchain, Herv\'e
    Pag\`es, Paul Shannon, Dan Tenenbaum
  \item Technical advisory council: Vincent Carey, Wolfgang Huber,
    Robert Gentleman, Rafael Irizzary, Sean Davis, Kasper Hansen
  \item Scientific advisory board: Simon Tavar\'e, Vivian Bonazzi,
    Vincent Carey, Wolfgang Huber, Robert Gentleman, Rafael Irizzary,
    Paul Flicek, Simon Urbanek.
  \item NIH / NHGRI U41HG0004059
  \item The \Bioconductor{} community
  \item \ldots and the organizers of this course!
  \end{itemize}
\end{frame}

\end{document}