Bioconductor cheat sheet

Also useful are Kasper Daniel Hansen's Bioconductor class notes.

Install

For details go to http://bioconductor.org/install/

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

help within R

Simple help:

?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

Help for advanced users:

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 >= 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 

Bioconductor support website: https://support.bioconductor.org

If you use RStudio, then you already get nicely rendered documentation using ? or help. If you are a command line person, then you can use this alias to pop up a help page in your web browser with rhelp functionName packageName.

alias rhelp="Rscript -e 'args <- commandArgs(TRUE); help(args[2], package=args[3], help_type=\"html\"); Sys.sleep(5)' --args"

debugging R

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")

Show package-specific methods for a class

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.

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))

Annotations

Edit (May 2017): the below needs to be updated to show off AnnotationHub.

For AnnotationHub examples, see:

https://www.bioconductor.org/help/workflows/annotation/Annotation_Resources

The following is how to work with the organism database packages, and biomart.

AnnotationDbi

# 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 <- mapIds(org.Hs.eg.db, keys=k, column="ENSEMBL", keytype="ENTREZID")

# generates warning for 1:many mappings
res <- select(org.Hs.eg.db, keys=k,
  columns=c("ENTREZID","ENSEMBL","SYMBOL"),
  keytype="ENTREZID")

biomaRt

# map from one annotation to another using biomart
library(biomaRt)
m <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
map <- getBM(mart = m,
  attributes = c("ensembl_gene_id", "entrezgene"),
  filters = "ensembl_gene_id", 
  values = some.ensembl.genes)

Genomic ranges

GenomicRanges

library(GenomicRanges)
z <- 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

Intra-range methods

Affects ranges independently

• | zooms in (positive) or out (negative) by multiples

Inter-range methods

Affects ranges as a group

Nearest methods

Given two sets of ranges, x and subject, for each range in x, returns...

A Hits object can be accessed with queryHits, subjectHits and mcols if a distance is associated.

set methods

If y is a GRangesList, then use punion, etc. All functions have default ignore.strand=FALSE, so are strand specific.

union(x,y) 
intersect(x,y)
setdiff(x,y)

Overlaps

x %over% y  # logical vector of which x overlaps any in y
fo <- findOverlaps(x,y) # returns a Hits object
queryHits(fo)   # which in x
subjectHits(fo) # which in y 

Seqnames and seqlevels

GenomicRanges and GenomeInfoDb

gr.sub <- gr[seqlevels(gr) == "chr1"]
seqlevelsStyle(x) <- "UCSC" # convert to 'chr1' style from "NCBI" style '1'

Sequences

Biostrings

see the Biostrings Quick Overview PDF

For naming, see cheat sheet for annotation

library(BSgenome.Hsapiens.UCSC.hg19)
dnastringset <- getSeq(Hsapiens, granges) # returns a DNAStringSet
# also Views() for Bioconductor >= 3.1

library(Biostrings)
dnastringset <- readDNAStringSet("transcripts.fa")

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

Sequencing data

Rsamtools scanBam returns lists of raw values from BAM files

library(Rsamtools)
which <- GRanges("chr1",IRanges(1000001,1001000))
what <- c("rname","strand","pos","qwidth","seq")
param <- ScanBamParam(which=which, what=what)
# for more BamFile functions/details see ?BamFile
# yieldSize for chunk-wise access
bamfile <- BamFile("/path/to/file.bam")
reads <- scanBam(bamfile, param=param)
res <- 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 <- scanFa(fastaFile, param=granges)

GenomicAlignments returns Bioconductor objects (GRanges-based)

library(GenomicAlignments)
ga <- readGAlignments(bamfile) # single-end
ga <- readGAlignmentPairs(bamfile) # paired-end

Transcript databases

GenomicFeatures

# get a transcript database, which stores exon, trancript, and gene information
library(GenomicFeatures)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene

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

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

# in Bioconductor >= 3.1, also makeTxDbFromGRanges

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

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

# then get the transcript sequence
txSeq <- extractTranscriptSeqs(Hsapiens, ebt)

Summarizing information across ranges and experiments

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).

library(GenomicAlignments)
fls <- list.files(pattern="*.bam$")
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
ebg <- exonsBy(txdb, by="gene")
# see yieldSize argument for restricting memory
bf <- BamFileList(fls)
library(BiocParallel)
register(MulticoreParam(4))
# lots of options in the man page
# singleEnd, ignore.strand, inter.features, fragments, etc.
se <- summarizeOverlaps(ebg, bf)

# operations on SummarizedExperiment
assay(se) # the counts from summarizeOverlaps
colData(se)
rowRanges(se)

My preferred quantification method is Salmon, with --gcBias option enabled unless you know there is no GC dependence in the data, followed by tximport. Here is an example of usage:

coldata <- read.table("samples.txt")
rownames(coldata) <- coldata$id
files <- coldata$files; names(files) <- coldata$id
txi <- tximport(files, type="salmon", tx2gene=tx2gene)
dds <- DESeqDataSetFromTximport(txi, coldata, ~condition)

Another fast Bioconductor read counting method is featureCounts in Rsubread.

library(Rsubread)
res <- featureCounts(files, annot.ext="annotation.gtf",
  isGTFAnnotationFile=TRUE,
  GTF.featureType="exon",
  GTF.attrType="gene_id")
res$counts

RNA-seq gene-wise analysis

DESeq2

My preferred pipeline for DESeq2 users is to start with a lightweight transcript abundance quantifier such as Salmon and to use tximport, followed by DESeqDataSetFromTximport.

Here, coldata is a data.frame with group as a column.

library(DESeq2)
# from tximport
dds <- DESeqDataSetFromTximport(txi, coldata, ~ group)
# from SummarizedExperiment
dds <- DESeqDataSet(se, ~ group)
# from count matrix
dds <- DESeqDataSetFromMatrix(counts, coldata, ~ group)
# minimal filtering helps keep things fast 
# one can set 'n' to e.g. min(5, smallest group sample size)
keep <- rowSums(counts(dds) >= 10) >= n 
dds <- dds[keep,]
dds <- DESeq(dds)
res <- results(dds) # no shrinkage of LFC, or:
res <- lfcShrink(dds, coef = 2, type="apeglm") # shrink LFCs

edgeR

# this chunk from the Quick start in the edgeR User Guide
library(edgeR) 
y <- DGEList(counts=counts,group=group)
keep <- filterByExpr(y)
y <- y[keep,]
y <- calcNormFactors(y)
design <- model.matrix(~group)
y <- estimateDisp(y,design)
fit <- glmFit(y,design)
lrt <- glmLRT(fit)
topTags(lrt)
# or use the QL methods:
qlfit <- glmQLFit(y,design)
qlft <- glmQLFTest(qlfit)
topTags(qlft)

limma-voom

library(limma)
design <- model.matrix(~ group)
y <- DGEList(counts)
keep <- filterByExpr(y)
y <- y[keep,]
y <- calcNormFactors(y)
v <- voom(y,design)
fit <- lmFit(v,design)
fit <- eBayes(fit)
topTable(fit)

Many more RNA-seq packages

Expression set

library(Biobase)
data(sample.ExpressionSet)
e <- sample.ExpressionSet
exprs(e)
pData(e)
fData(e)

Get GEO dataset

library(GEOquery)
e <- getGEO("GSE9514")

Microarray analysis

library(affy)
library(limma)
phenoData <- read.AnnotatedDataFrame("sample-description.csv")
eset <- justRMA("/celfile-directory", phenoData=phenoData)
design <- model.matrix(~ Disease, pData(eset))
fit <- lmFit(eset, design)
efit <- eBayes(fit)
topTable(efit, coef=2)

iCOBRA performance metrics

library(iCOBRA)
cd <- COBRAData(pval=pval.df, padj=padj.df, score=score.df, truth=truth.df)
cp <- calculate_performance(cd, binary_truth = "status", cont_truth = "logFC")
cobraplot <- prepare_data_for_plot(cp)
plot_fdrtprcurve(cobraplot)
# interactive shiny app:
COBRAapp(cd)