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    <div class="quarto-title-meta-heading">Author</div>
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             <p>Michael Love </p>
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</header>

<section id="install" class="level2">
<h2 class="anchored" data-anchor-id="install">Install</h2>
<p>For details go to http://bioconductor.org/install/</p>
<pre><code>if (!requireNamespace("BiocManager"))
    install.packages("BiocManager")
BiocManager::install()
BiocManager::install(c("package1","package2")
BiocManager::valid() # are packages up to date?

# what Bioc version is release right now?
http://bioconductor.org/bioc-version
# what Bioc versions are release/devel?
http://bioconductor.org/js/versions.js</code></pre>
</section>
<section id="help-within-r" class="level2">
<h2 class="anchored" data-anchor-id="help-within-r">help within R</h2>
<p>Simple help:</p>
<pre><code>?functionName
?"eSet-class" # classes need the '-class' on the end
help(package="foo",help_type="html") # launch web browser help
vignette("topic")
browseVignettes(package="package") # show vignettes for the package</code></pre>
<p>Help for advanced users:</p>
<pre><code>functionName # prints source code
getMethod(method,"class")  # prints source code for method
selectMethod(method, "class") # will climb the inheritance to find method
showMethods(classes="class") # show all methods for class
methods(class="GRanges") # this will work in R &gt;= 3.2
?"functionName,class-method" # method help for S4 objects, e.g.:
?"plotMA,data.frame-method" # from library(geneplotter)
?"method.class" # method help for S3 objects e.g.:
?"plot.lm"
sessionInfo() # necessary info for getting help
packageVersion("foo") # what version of package </code></pre>
<p>Bioconductor support website: https://support.bioconductor.org</p>
<p>If you use RStudio, then you already get nicely rendered documentation using <code>?</code> or <code>help</code>. If you are a command line person, then you can use this alias to pop up a help page in your web browser with <code>rhelp functionName packageName</code>.</p>
<pre><code>alias rhelp="Rscript -e 'args &lt;- commandArgs(TRUE); help(args[2], package=args[3], help_type=\"html\"); Sys.sleep(5)' --args"</code></pre>
</section>
<section id="debugging-r" class="level2">
<h2 class="anchored" data-anchor-id="debugging-r">debugging R</h2>
<pre><code>traceback() # what steps lead to an error
# debug a function
debug(myFunction) # step line-by-line through the code in a function
undebug(myFunction) # stop debugging
debugonce(myFunction) # same as above, but doesn't need undebug()
# also useful if you are writing code is to put
# the function browser() inside a function at a critical point
# this plus devtools::load_all() can be useful for programming
# to jump in function on error:
options(error=recover)
# turn that behavior off:
options(error=NULL)
# debug, e.g. estimateSizeFactors from DESeq2...
# debugging an S4 method is more difficult; this gives you a peek inside:
trace(estimateSizeFactors, browser, exit=browser, signature="DESeqDataSet")</code></pre>
</section>
<section id="show-package-specific-methods-for-a-class" class="level2">
<h2 class="anchored" data-anchor-id="show-package-specific-methods-for-a-class">Show package-specific methods for a class</h2>
<p>These two long strings of R code do approximately the same thing: obtain the methods that operate on an object of a given class, which are defined in a specific package.</p>
<pre><code>intersect(sapply(strsplit(as.character(methods(class="DESeqDataSet")), ","), `[`, 1), ls("package:DESeq2"))
sub("Function: (.*) \\(package .*\\)","\\1",grep("Function",showMethods(classes="DESeqDataSet", where=getNamespace("DESeq2"), printTo=FALSE), value=TRUE))</code></pre>
</section>
<section id="annotations" class="level2">
<h2 class="anchored" data-anchor-id="annotations">Annotations</h2>
<p>For AnnotationHub examples, see:</p>
<p>https://www.bioconductor.org/help/workflows/annotation/Annotation_Resources</p>
<p>The following is how to work with the organism database packages, and biomart.</p>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.html">AnnotationDbi</a></p>
<pre><code># using one of the annotation packges
library(AnnotationDbi)
library(org.Hs.eg.db) # or, e.g. Homo.sapiens
columns(org.Hs.eg.db)
keytypes(org.Hs.eg.db)
head(keys(org.Hs.eg.db, keytype="ENTREZID"))
# returns a named character vector, see ?mapIds for multiVals options
res &lt;- mapIds(org.Hs.eg.db, keys=k, column="ENSEMBL", keytype="ENTREZID")

# generates warning for 1:many mappings
res &lt;- select(org.Hs.eg.db, keys=k,
  columns=c("ENTREZID","ENSEMBL","SYMBOL"),
  keytype="ENTREZID")</code></pre>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/biomaRt.html">biomaRt</a></p>
<pre><code># map from one annotation to another using biomart
library(biomaRt)
m &lt;- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
map &lt;- getBM(mart = m,
  attributes = c("ensembl_gene_id", "entrezgene"),
  filters = "ensembl_gene_id", 
  values = some.ensembl.genes)</code></pre>
</section>
<section id="genomic-ranges" class="level2">
<h2 class="anchored" data-anchor-id="genomic-ranges">Genomic ranges</h2>
<p><a href="http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html">GenomicRanges</a></p>
<pre><code>library(GenomicRanges)
z &lt;- GRanges("chr1",IRanges(1000001,1001000),strand="+")
start(z)
end(z)
width(z)
strand(z)
mcols(z) # the 'metadata columns', any information stored alongside each range
ranges(z) # gives the IRanges
seqnames(z) # the chromosomes for each ranges
seqlevels(z) # the possible chromosomes
seqlengths(z) # the lengths for each chromosome</code></pre>
<section id="intra-range-methods" class="level3">
<h3 class="anchored" data-anchor-id="intra-range-methods">Intra-range methods</h3>
<p>Affects ranges independently</p>
<table class="table">
<thead>
<tr class="header">
<th>function</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>shift</td>
<td>moves left/right</td>
</tr>
<tr class="even">
<td>narrow</td>
<td>narrows by relative position within range</td>
</tr>
<tr class="odd">
<td>resize</td>
<td>resizes to width, fixing start for +, end for -</td>
</tr>
<tr class="even">
<td>flank</td>
<td>returns flanking ranges to the left +, or right -</td>
</tr>
<tr class="odd">
<td>promoters</td>
<td>similar to flank</td>
</tr>
<tr class="even">
<td>restrict</td>
<td>restricts ranges to a start and end position</td>
</tr>
<tr class="odd">
<td>trim</td>
<td>trims out of bound ranges</td>
</tr>
<tr class="even">
<td>+/-</td>
<td>expands/contracts by adding/subtracting fixed amount</td>
</tr>
<tr class="odd">
<td>*</td>
<td>zooms in (positive) or out (negative) by multiples</td>
</tr>
</tbody>
</table>
</section>
<section id="inter-range-methods" class="level3">
<h3 class="anchored" data-anchor-id="inter-range-methods">Inter-range methods</h3>
<p>Affects ranges as a group</p>
<table class="table">
<thead>
<tr class="header">
<th>function</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>range</td>
<td>one range, leftmost start to rightmost end</td>
</tr>
<tr class="even">
<td>reduce</td>
<td>cover all positions with only one range</td>
</tr>
<tr class="odd">
<td>gaps</td>
<td>uncovered positions within range</td>
</tr>
<tr class="even">
<td>disjoin</td>
<td>breaks into discrete ranges based on original starts/ends</td>
</tr>
</tbody>
</table>
</section>
<section id="nearest-methods" class="level3">
<h3 class="anchored" data-anchor-id="nearest-methods">Nearest methods</h3>
<p>Given two sets of ranges, <code>x</code> and <code>subject</code>, for each range in <code>x</code>, returns…</p>
<table class="table">
<colgroup>
<col style="width: 50%">
<col style="width: 50%">
</colgroup>
<thead>
<tr class="header">
<th>function</th>
<th>description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>nearest</td>
<td>index of the nearest neighbor range in subject</td>
</tr>
<tr class="even">
<td>precede</td>
<td>index of the range in subject that is directly preceded by the range in x</td>
</tr>
<tr class="odd">
<td>follow</td>
<td>index of the range in subject that is directly followed by the range in x</td>
</tr>
<tr class="even">
<td>distanceToNearest</td>
<td>distances to its nearest neighbor in subject (Hits object)</td>
</tr>
<tr class="odd">
<td>distance</td>
<td>distances to nearest neighbor (integer vector)</td>
</tr>
</tbody>
</table>
<p>A Hits object can be accessed with <code>queryHits</code>, <code>subjectHits</code> and <code>mcols</code> if a distance is associated.</p>
</section>
<section id="set-methods" class="level3">
<h3 class="anchored" data-anchor-id="set-methods">set methods</h3>
<p>If <code>y</code> is a GRangesList, then use <code>punion</code>, etc. All functions have default <code>ignore.strand=FALSE</code>, so are strand specific.</p>
<pre><code>union(x,y) 
intersect(x,y)
setdiff(x,y)</code></pre>
</section>
<section id="overlaps" class="level3">
<h3 class="anchored" data-anchor-id="overlaps">Overlaps</h3>
<pre><code>x %over% y  # logical vector of which x overlaps any in y
fo &lt;- findOverlaps(x,y) # returns a Hits object
queryHits(fo)   # which in x
subjectHits(fo) # which in y </code></pre>
</section>
<section id="seqnames-and-seqlevels" class="level3">
<h3 class="anchored" data-anchor-id="seqnames-and-seqlevels">Seqnames and seqlevels</h3>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/GenomicRanges.html">GenomicRanges</a> and <a href="http://www.bioconductor.org/packages/release/bioc/html/GenomeInfoDb.html">GenomeInfoDb</a></p>
<pre><code>gr.sub &lt;- gr[seqlevels(gr) == "chr1"]
seqlevelsStyle(x) &lt;- "UCSC" # convert to 'chr1' style from "NCBI" style '1'</code></pre>
</section>
</section>
<section id="sequences" class="level2">
<h2 class="anchored" data-anchor-id="sequences">Sequences</h2>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/Biostrings.html">Biostrings</a></p>
<p>see the <a href="http://www.bioconductor.org/packages/release/bioc/vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf">Biostrings Quick Overview PDF</a></p>
<p>For naming, see <a href="http://genomicsclass.github.io/book/pages/annoCheat.html">cheat sheet for annotation</a></p>
<pre><code>library(BSgenome.Hsapiens.UCSC.hg19)
dnastringset &lt;- getSeq(Hsapiens, granges) # returns a DNAStringSet
# also Views() for Bioconductor &gt;= 3.1</code></pre>
<pre><code>library(Biostrings)
dnastringset &lt;- readDNAStringSet("transcripts.fa")</code></pre>
<pre><code>substr(dnastringset, 1, 10) # to character string
subseq(dnastringset, 1, 10) # returns DNAStringSet
Views(dnastringset, 1, 10) # lightweight views into object
complement(dnastringset)
reverseComplement(dnastringset)
matchPattern("ACGTT", dnastring) # also countPattern, also works on Hsapiens/genome
vmatchPattern("ACGTT", dnastringset) # also vcountPattern
letterFrequecy(dnastringset, "CG") # how many C's or G's
# also letterFrequencyInSlidingView
alphabetFrequency(dnastringset, as.prob=TRUE)
# also oligonucleotideFrequency, dinucleotideFrequency, trinucleotideFrequency
# transcribe/translate for imitating biological processes</code></pre>
</section>
<section id="sequencing-data" class="level2">
<h2 class="anchored" data-anchor-id="sequencing-data">Sequencing data</h2>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/Rsamtools.html">Rsamtools</a> <code>scanBam</code> returns lists of raw values from BAM files</p>
<pre><code>library(Rsamtools)
which &lt;- GRanges("chr1",IRanges(1000001,1001000))
what &lt;- c("rname","strand","pos","qwidth","seq")
param &lt;- ScanBamParam(which=which, what=what)
# for more BamFile functions/details see ?BamFile
# yieldSize for chunk-wise access
bamfile &lt;- BamFile("/path/to/file.bam")
reads &lt;- scanBam(bamfile, param=param)
res &lt;- countBam(bamfile, param=param) 
# for more sophisticated counting modes
# see summarizeOverlaps() below

# quickly check chromosome names
seqinfo(BamFile("/path/to/file.bam"))

# DNAStringSet is defined in the Biostrings package
# see the Biostrings Quick Overview PDF
dnastringset &lt;- scanFa(fastaFile, param=granges)</code></pre>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/GenomicAlignments.html">GenomicAlignments</a> returns Bioconductor objects (GRanges-based)</p>
<pre><code>library(GenomicAlignments)
ga &lt;- readGAlignments(bamfile) # single-end
ga &lt;- readGAlignmentPairs(bamfile) # paired-end</code></pre>
</section>
<section id="transcript-databases" class="level2">
<h2 class="anchored" data-anchor-id="transcript-databases">Transcript databases</h2>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/GenomicFeatures.html">GenomicFeatures</a></p>
<pre><code># get a transcript database, which stores exon, trancript, and gene information
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb &lt;- TxDb.Hsapiens.UCSC.hg19.knownGene

# or build a txdb from GTF file (e.g. downloadable from Ensembl FTP site)
txdb &lt;- makeTranscriptDbFromGFF("file.GTF", format="gtf")

# or build a txdb from Biomart (however, not as easy to reproduce later)
txdb &lt;- makeTranscriptDbFromBiomart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")

# in Bioconductor &gt;= 3.1, also makeTxDbFromGRanges

# saving and loading
saveDb(txdb, file="txdb.sqlite")
loadDb("txdb.sqlite")

# extracting information from txdb
g &lt;- genes(txdb) # GRanges, just start to end, no exon/intron information
tx &lt;- transcripts(txdb) # GRanges, similar to genes()
e &lt;- exons(txdb) # GRanges for each exon
ebg &lt;- exonsBy(txdb, by="gene") # exons grouped in a GRangesList by gene
ebt &lt;- exonsBy(txdb, by="tx") # similar but by transcript

# then get the transcript sequence
txSeq &lt;- extractTranscriptSeqs(Hsapiens, ebt)</code></pre>
</section>
<section id="summarizing-information-across-ranges-and-experiments" class="level2">
<h2 class="anchored" data-anchor-id="summarizing-information-across-ranges-and-experiments">Summarizing information across ranges and experiments</h2>
<p>The SummarizedExperiment is a storage class for high-dimensional information tied to the same GRanges or GRangesList across experiments (e.g., read counts in exons for each gene).</p>
<pre><code>library(GenomicAlignments)
fls &lt;- list.files(pattern="*.bam$")
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb &lt;- TxDb.Hsapiens.UCSC.hg19.knownGene
ebg &lt;- exonsBy(txdb, by="gene")
# see yieldSize argument for restricting memory
bf &lt;- BamFileList(fls)
library(BiocParallel)
register(MulticoreParam(4))
# lots of options in the man page
# singleEnd, ignore.strand, inter.features, fragments, etc.
se &lt;- summarizeOverlaps(ebg, bf)

# operations on SummarizedExperiment
assay(se) # the counts from summarizeOverlaps
colData(se)
rowRanges(se)</code></pre>
<p>My preferred quantification method is <a href="https://combine-lab.github.io/salmon/">Salmon</a>, with <code>--gcBias</code> option enabled unless you know there is no GC dependence in the data, followed by <a href="http://bioconductor.org/pacakges/tximport">tximport</a>. Here is an example of usage:</p>
<pre><code>coldata &lt;- read.table("samples.txt")
rownames(coldata) &lt;- coldata$id
files &lt;- coldata$files; names(files) &lt;- coldata$id
txi &lt;- tximport(files, type="salmon", tx2gene=tx2gene)
dds &lt;- DESeqDataSetFromTximport(txi, coldata, ~condition)</code></pre>
<p>Another fast Bioconductor read counting method is featureCounts in <a href="http://www.bioconductor.org/packages/release/bioc/html/Rsubread.html">Rsubread</a>.</p>
<pre><code>library(Rsubread)
res &lt;- featureCounts(files, annot.ext="annotation.gtf",
  isGTFAnnotationFile=TRUE,
  GTF.featureType="exon",
  GTF.attrType="gene_id")
res$counts</code></pre>
</section>
<section id="rna-seq-gene-wise-analysis" class="level2">
<h2 class="anchored" data-anchor-id="rna-seq-gene-wise-analysis">RNA-seq gene-wise analysis</h2>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html">DESeq2</a></p>
<p>My preferred pipeline for DESeq2 users is to start with a lightweight transcript abundance quantifier such as <a href="https://combine-lab.github.io/salmon/">Salmon</a> and to use <a href="http://bioconductor.org/packages/tximport">tximport</a>, followed by <code>DESeqDataSetFromTximport</code>.</p>
<p>Here, <code>coldata</code> is a <em>data.frame</em> with <code>group</code> as a column.</p>
<pre><code>library(DESeq2)
# from tximport
dds &lt;- DESeqDataSetFromTximport(txi, coldata, ~ group)
# from SummarizedExperiment
dds &lt;- DESeqDataSet(se, ~ group)
# from count matrix
dds &lt;- DESeqDataSetFromMatrix(counts, coldata, ~ group)
# minimal filtering helps keep things fast 
# one can set 'n' to e.g. min(5, smallest group sample size)
keep &lt;- rowSums(counts(dds) &gt;= 10) &gt;= n 
dds &lt;- dds[keep,]
dds &lt;- DESeq(dds)
res &lt;- results(dds) # no shrinkage of LFC, or:
res &lt;- lfcShrink(dds, coef = 2, type="apeglm") # shrink LFCs</code></pre>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/edgeR.html">edgeR</a></p>
<pre><code># this chunk from the Quick start in the edgeR User Guide
library(edgeR) 
y &lt;- DGEList(counts=counts,group=group)
keep &lt;- filterByExpr(y)
y &lt;- y[keep,]
y &lt;- calcNormFactors(y)
design &lt;- model.matrix(~group)
y &lt;- estimateDisp(y,design)
fit &lt;- glmFit(y,design)
lrt &lt;- glmLRT(fit)
topTags(lrt)
# or use the QL methods:
qlfit &lt;- glmQLFit(y,design)
qlft &lt;- glmQLFTest(qlfit)
topTags(qlft)</code></pre>
<p><a href="http://www.bioconductor.org/packages/release/bioc/html/limma.html">limma-voom</a></p>
<pre><code>library(limma)
design &lt;- model.matrix(~ group)
y &lt;- DGEList(counts)
keep &lt;- filterByExpr(y)
y &lt;- y[keep,]
y &lt;- calcNormFactors(y)
v &lt;- voom(y,design)
fit &lt;- lmFit(v,design)
fit &lt;- eBayes(fit)
topTable(fit)</code></pre>
<p><a href="http://www.bioconductor.org/packages/release/BiocViews.html#___RNASeq">Many more RNA-seq packages</a></p>
</section>
<section id="expression-set" class="level2">
<h2 class="anchored" data-anchor-id="expression-set">Expression set</h2>
<pre><code>library(Biobase)
data(sample.ExpressionSet)
e &lt;- sample.ExpressionSet
exprs(e)
pData(e)
fData(e)</code></pre>
</section>
<section id="get-geo-dataset" class="level2">
<h2 class="anchored" data-anchor-id="get-geo-dataset">Get GEO dataset</h2>
<pre><code>library(GEOquery)
e &lt;- getGEO("GSE9514")</code></pre>
</section>
<section id="microarray-analysis" class="level2">
<h2 class="anchored" data-anchor-id="microarray-analysis">Microarray analysis</h2>
<pre><code>library(affy)
library(limma)
phenoData &lt;- read.AnnotatedDataFrame("sample-description.csv")
eset &lt;- justRMA("/celfile-directory", phenoData=phenoData)
design &lt;- model.matrix(~ Disease, pData(eset))
fit &lt;- lmFit(eset, design)
efit &lt;- eBayes(fit)
topTable(efit, coef=2)</code></pre>
</section>
<section id="icobra-performance-metrics" class="level2">
<h2 class="anchored" data-anchor-id="icobra-performance-metrics">iCOBRA performance metrics</h2>
<pre><code>library(iCOBRA)
cd &lt;- COBRAData(pval=pval.df, padj=padj.df, score=score.df, truth=truth.df)
cp &lt;- calculate_performance(cd, binary_truth = "status", cont_truth = "logFC")
cobraplot &lt;- prepare_data_for_plot(cp)
plot_fdrtprcurve(cobraplot)
# interactive shiny app:
COBRAapp(cd)</code></pre>
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