In Bioconductor/ENAR2016: Tutorial: Introduction to High Throughput DNA Sequence Data Analysis Using R / Bioconductor

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suppressPackageStartupMessages({
    library(ENAR2016)
    library(ggplot2)
    library(airway)
    library(DESeq2)
    library(org.Hs.eg.db)
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    library(BSgenome.Hsapiens.UCSC.hg19)
    library(AnnotationHub)
})

Introduction

R

• r-project.org: "A free software environment for statistical computing and graphics"

Functions

rnorm(10)     # sample 10 random normal deviates

• zero or more arguments
• named or unnames rnorm(10) the same as rnorm(n=10)
• required or optional, e.g., rnorm(n=10, mean=0, sd=1)
• Help via ?rnorm

Vectors

• logical (c(TRUE, FALSE)), integer (1:5), numeric (rnorm(10)), character (c("alpha", "beat")), complex
• statistical concepts: missing values (NA), factors (factor(c("Female", "Male"))), formulas (y ~ x)
• vectorized

x <- rnorm(1000)
y <- x + rnorm(1000)
plot(y ~ x)

Objects: classes and methods

• coordinated manipulation of related data
• simple classes, e.g., data.frame(), matrix()
• more complicated classes, e.g., lm()
• manipulation via methods

df <- data.frame(Y = y, X = x)
fit <- lm(Y ~ X, df)
plot(Y ~ X, df)
abline(fit, col="red", lwd=2)
anova(fit)

Packages

• base & recommended
  • e.g., stats, graphics, Matrix
• contributed
  • e.g., ggplot2, dplyr, ade
  • discovery: CRAN landing pages and task views
  • installation: install.packages() (or biocLite(), below)

library(ggplot2)
ggplot(df, aes(x=X, y=Y)) + geom_point() + geom_smooth(color="blue") +
    geom_smooth(method="lm", color="red")

Help!

• help.start()
• ?rnorm
• class(fit); methods(plot); methods(class=class(fit))
• ?"plot<tab>...

Bioconductor

• bioconductor.org: "Analysis and comprehension of high-throughput genomic data"

Biological domains

• Sequence analysis -- RNA-seq, ChIP-seq, SNPs, structural variants, ...
• Microarrays -- expression, methylation, copy number, ...
• Flow cytometry; proteomics; ...

Principles

• Interoperability

  • Formal approach to data -- 'S4' object system
  • Example: DNA sequences represented as a DNAStringSet(), rather than character vector.

• Reproducibility

  • Hallmark of scientific endeavor
  • Example: Package vignettes
  • Example: 'release' (bug fixes only; end user oriented) and 'devel' (new features, new packages; developer playground) versions

Installation and use

• Discovery: biocViews, package landing pages (e.g., DESeq2)
• Installation: biocLite("DESeq2"); also works for CRAN & github packages
• Help! -- support.bioconductor.org

Sequence Analysis

Six stages

• Experimental design
• Wet-lab preparation
• Sequencing
• Alignment / Reduction
• Statistical analysis
• Comprehension

Key packages: data access

• Biostrings: DNA sequences
• ShortRead: FastQ
• GenomicAlignments / Rsamtools: BAM alignments
• VariantAnnotation: VCF files
• rtracklayer: BED, GFF / GTF, BigWig, ...

Coordinated management: SummarizedExperiment

• Matrix of feature x sample assay(s)
• Column data descrbing samples
• Row ranges descrbing features

Case Study: RNA-Seq Differential Expression

This is derived from: RNA-Seq workflow: gene-level exploratory analysis and differential expression, by Michael Love, Simon Anders, Wolfgang Huber; modified by Martin Morgan, October 2015.

We walk through an end-to-end RNA-Seq differential expression workflow, using DESeq2 along with other Bioconductor packages.

The complete work flow starts from the FASTQ files, but we will start after reads have been aligned to a reference genome and reads overlapping known genes have been counted.

Our main focus is on differential gene expression analysis with DESeq2. Other Bioconductor packages are important in statistical inference of differential expression at the gene level, including Rsubread, edgeR, limma, BaySeq, and others.

The data are from an RNA-Seq experiment of airway smooth muscle cells treated with dexamethasone, a synthetic glucocorticoid steroid with anti-inflammatory effects. Glucocorticoids are used, for example, in asthma patients to prevent or reduce inflammation of the airways.

In the experiment, four primary human airway smooth muscle cell lines were treated with 1 micromolar dexamethasone for 18 hours. For each of the four cell lines, we have a treated and an untreated sample. The reference for the experiment is:

Himes BE, Jiang X, Wagner P, Hu R, Wang Q, Klanderman B, Whitaker RM, Duan Q, Lasky-Su J, Nikolos C, Jester W, Johnson M, Panettieri R Jr, Tantisira KG, Weiss ST, Lu Q. "RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells." PLoS One. 2014 Jun 13;9(6):e99625. PMID: 24926665. GEO: GSE52778.

Preparation

Counts

• A feature x sample matrix of raw count data, typically counts of reads overlapping features
• Derived from sequence reads
• Via alignment and summarization (via Rsubread, SummarizedExperiment summarizeOverlaps(), etc.)
• Directly from sequence data via kallisto or sailfish, using tximport (in Bioc-devel, soon in release!)

SummarizedExperiment

library(airway)
data("airway")
se <- airway

Assay data

• Features x samples
• Raw counts

• Row-wise tests

  r head(assay(se))

Experimental design

• Covariate (cell) plus treatment (dex)

• R's formula interface allows for complicated designs, contrasts, etc

  r colData(se) design = ~ cell + dex se$dex <- relevel(se$dex, "untrt") # 'untrt' as reference level

Features (genes)

• Genomic coordinates of gene models

  r rowRanges(se)

Analysis Pipeline

Create DESeqDataSet object

library(DESeq2)
dds <- DESeqDataSet(se, design = ~ cell + dex)

Run the pipeline

dds <- DESeq(dds)

• Estimate size factors ('library size')
• Estimate dispersion coeficient of negative binomial model
• Fit GLM using negative binomial error
• Wald test

Summarize results

res <- results(dds)
head(res)
mcols(res)                     # what does each column mean?
head(res[order(res$padj),])    # 'top table' by p-adj

Some Considerations

Experimental design

Keep it simple

• Classical experimental designs
• Time series
• Without missing values, where possible
• Intended analysis must be feasbile -- can the available samples and hypothesis of interest be combined to formulate a testable statistical hypothesis?

Replicate

• Extent of replication determines nuance of biological question.
• No replication (1 sample per treatment): qualitative description with limited statistical options.
• 3-5 replicates per treatment: designed experimental manipulation with cell lines or other well-defined entities; 2-fold (?) change in average expression between groups.
• 10-50 replicates per treatment: population studies, e.g., cancer cell lines.
• 1000's of replicates: prospective studies, e.g., SNP discovery
• One resource: RNASeqPower

Avoid confounding experimental factors with other factors

• Common problems: samples from one treatment all on the same flow cell; samples from treatment 1 processed first, treatment 2 processed second, etc.

Be aware of batch effects

• Known
  • Phenotypic covariates, e.g., age, gender
  • Experimental covariates, e.g., lab or date of processing
  • Incorporate into linear model, at least approximately
• Unknown
  • Or just unexpected / undetected
  • Characterize using, e.g., sva
• Surrogate variable analysis
  • Leek et al., 2010, Nature Reviews Genetics 11 733-739, Leek & Story PLoS Genet 3(9): e161.
  • Scientific finding: pervasive batch effects
  • Statistical insights: surrogate variable analysis: identify and build surrogate variables; remove known batch effects
  • Benefits: reduce dependence, stabilize error rate estimates, and improve reproducibility
  • combat software / sva Bioconductor package

HapMap samples from one facility, ordered by date of processing.

Wet-lab

Confounding factors

• Record or avoid

Artifacts of your particular protocols

• Sequence contaminants
• Enrichment bias, e.g., non-uniform transcript representation.
• PCR artifacts -- adapter contaminants, sequence-specific amplification bias, ...

Sequencing

Axes of variation

• Single- versus paired-end
• Length: 50-200nt
• Number of reads per sample

Application-specific, e.g.,

• RNA-seq, known genes: single- or paired-end reads
• RNA-seq, transcripts or novel variants: paired-end reads
• ChIP-seq: short, single-end reads are usually sufficient
• Copy number: single- or paired-end reads
• Structural variants: paired-end reads
• Variants: depth via longer, paired-end reads
• Microbiome: long paired-end reads (overlapping ends)

Alignment / Reduction

Alignment

• de novo
  • No reference genome; considerable sequencing and computational resources
• Genome
  • Established reference genome
  • Splice-aware aligners
  • Novel transcript discovery
• Transcriptome
  • Established reference genome; reliable gene model
  • Simple aligners
  • Known gene / transcript expression

Splice-aware aligners (and Bioconductor wrappers)

• Bowtie2 (Rbowtie)
• STAR (doi)
• subread (Rsubread)
• Systematic evaluation (Engstrom et al., 2013, doi)

Reduction to 'count tables'

• Use known gene model to count aligned reads overlapping regions of interest / gene models
• Gene model can be public (e.g., UCSC, NCBI, ENSEMBL) or ad hoc (gff file)
• GenomicAlignments::summarizeOverlaps()
• Rsubread::featureCount()
• HTSeq, htseq-count

(Bowtie2 / tophat / Cufflinks / Cuffdiff / etc)

• tophat uses Bowtie2 to perform basic single- and paired-end alignments, then uses algorithms to place difficult-to-align reads near to their well-aligned mates.
• Cufflinks (doi) takes tophat output and estimate existing and novel transcript abundance. How Cufflinks Works
• Cuffdiff assesses statistical significance of estimated abundances between experimental groups
• RSEM includes de novo assembly and quantification

(kallisto / sailfish)

• 'Next generation' differential expression tools; transcriptome alignment
• E.g., kallisto takes a radically different approach: from FASTQ to count table without BAM files.
• Very fast, almost as accurate.
• Hints on how it works; arXiv

• Integration with gene-level analyses -- Soneson et al.

  ```r

  in Bioc-devel

  library(tximportData) dir <- system.file("extdata", package="tximportData") samples <- read.table(file.path(dir,"samples.txt"), header=TRUE) files <- file.path(dir,"salmon", samples$run, "quant.sf") names(files) <- paste0("sample",1:6)

  tx2gene links transcript IDs to gene IDs for summarization

  tx2gene <- read.csv(file.path(dir, "tx2gene.csv"))

  txi <- tximport(files, type="salmon", tx2gene=tx2gene) ```

tx2gene links transcript IDs to gene IDs for summarization

tx2gene <- read.csv(file.path(dir, "tx2gene.csv"))

txi <- tximport(files, type="salmon", tx2gene=tx2gene)

Statistical Considerations

Unique statistical aspects

• Large data, few samples
• Comparison of each gene, across samples; univariate measures
• Each gene is analyzed by the same experimental design, under the same null hypothesis

Summarization

• Counts per se, rather than a summary (RPKM, FPKM, ...), are relevant for analysis

  • For a given gene, larger counts imply more information; RPKM etc., treat all estimates as equally informative.
  • Comparison is across samples at each region of interest; all samples have the same region of interest, so modulo library size there is no need to correct for, e.g., gene length or mapability.

Normalization

• Libraries differ in size (total counted reads per sample) for un-interesting reasons; we need to account for differences in library size in statistical analysis.
• Total number of counted reads per sample is not a good estimate of library size. It is un-necessarily influenced by regions with large counts, and can introduce bias and correlation across genes. Instead, use a robust measure of library size that takes account of skew in the distribution of counts (simplest: trimmed geometric mean; more advanced / appropriate encountered in the lab).
• Library size (total number of counted reads) differs between samples, and should be included as a statistical offset in analysis of differential expression, rather than 'dividing by' the library size early in an analysis.

• Detail

  • DESeq2 estimateSizeFactors(), Anders and Huber, 2010
  • For each gene: geometric mean of all samples.
  • For each sample: median ratio of the sample gene over the geometric mean of all samples
  • Functions other than the median can be used; control genes can be used instead

Appropriate error model

• Count data is not distributed normally or as a Poisson process, but rather as negative binomial.
• Result of a combination Poisson (`shot' noise, i.e., within-sample technical and sampling variation in read counts) with variation between biological samples.
• A negative binomial model requires estimation of an additional parameter ('dispersion'), which is estimated poorly in small samples.
• Basic strategy is to moderate per-gene estimates with more robust local estimates derived from genes with similar expression values (a little more on borrowing information is provided below).

Pre-filtering

• Naively, a statistical test (e.g., t-test) could be applied to each row of a counts table. However, we have relatively few samples (10's) and very many comparisons (10,000's) so a naive approach is likely to be very underpowered, resulting in a very high false discovery rate
• A simple approach is perform fewer tests by removing regions that could not possibly result in statistical significance, regardless of hypothesis under consideration.
• Example: a region with 0 counts in all samples could not possibly be significant regradless of hypothesis, so exclude from further analysis.
• Basic approaches: 'K over A'-style filter -- require a minimum of A (normalized) read counts in at least K samples. Variance filter, e.g., IQR (inter-quartile range) provides a robust estimate of variability; can be used to rank and discard least-varying regions.
• More nuanced approaches: edgeR vignette.

Borrowing information

• Why does low statistical power elevate false discovery rate?
• One way of developing intuition is to recognize a t-test (for example) as a ratio of variances. The numerator is treatment-specific, but the denominator is a measure of overall variability.
• Variances are measured with uncertainty; over- or under-estimating the denominator variance has an asymmetric effect on a t-statistic or similar ratio, with an underestimate inflating the statistic more dramatically than an overestimate deflates the statistic. Hence elevated false discovery rate.
• Under the null hypothesis used in microarray or RNA-seq experiments, the expected overall variability of a gene is the same, at least for genes with similar average expression
• The strategy is to estimate the denominator variance as the between-group variance for the gene, moderated by the average between-group variance across all genes.
• This strategy exploits the fact that the same experimental design has been applied to all genes assayed, and is effective at moderating false discovery rate.

Comprehension: placing differentially expressed regions in context

• Gene names associated with genomic ranges
• Gene set enrichment and similar analysis
• Proximity to regulatory marks
• Integrate with other analyses, e.g., methylation, copy number, variants, ...

'Annotation' packages

• 'org': map between gene identifiers; also GO.db, KEGGREST

  r library(org.Hs.eg.db) ttbl <- head(res[order(res$padj),]) # 'top table' by p-adj (ensid <- rownames(ttbl)) mapIds(org.Hs.eg.db, ensid, "SYMBOL", "ENSEMBL") select(org.Hs.eg.db, ensid, c("SYMBOL", "GENENAME"), "ENSEMBL") columns(org.Hs.eg.db) - 'TxDb'

  ```r

  gene models, organized by entrez identifier

  library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene

  from Ensembl to Entrez identifiers

  egid <- mapIds(org.Hs.eg.db, ensid, "ENTREZID", "ENSEMBL") transcriptsBy(txdb, "gene")[egid] ``` - 'BSgenome': BSgenome.Hsapiens.UCSC.hg19

  r library(BSgenome.Hsapiens.UCSC.hg19) genome <- BSgenome.Hsapiens.UCSC.hg19 gn <- genes(txdb)[egid] up1000 <- flank(gn, width=1000) getSeq(genome, up1000)

• AnnotationHub

  • Many consortium-scale resources
  • Cached on disk
  • Imported in appropriate format

  r library(AnnotationHub) hub = AnnotationHub() query(hub, c("ensembl", "gtf", "release-83")) hub["AH50417"] hub[["AH50417"]]

Challenges & Solutions

Interoperability: Standard Representations

Genomic Ranges: GenomicRanges

• Standard representation of genomic coordiantes
• Describes annotations (exons, genes, transcripts, regulatory elements, ...) and data (peaks, reads, ...)

• A ton of functionality, especially findOverlaps()

  r gr <- GRanges("chr1", IRanges(c(1000, 2000, 3000), width=100), strand=c("+", "-", "*"), score=c(10, 20, 30)) gr

Data / metadata integration: SummarizedExperiment

• Simple matrix, with more complicated row (feature) and column (sample) annotations.

• Very easy to coordinate data manipulation -- avoid those embarassing 'off-by-one' and other errors!

  r library(airway) data(airway) airway

Scalability

Efficient R programming

• R works best when functions are vectorized, i.e., operating on vectors rather than iterating.
  • n.b., *apply() are forms of iteration, not vectorization

• R works best when memory is pre-allocated

• Worst: no pre-allocation, no vectorization; scales quadratically with number of elements

  r result <- integer() for (i in 1:10) result[i] = sqrt(i)

• Better: pre-allocate and fill; scales linearly with number of elements, but at the interpretted level

  ```r result <- integer(10) for (i in 1:10) result[[i]] = sqrt(i)

  same as the much more compact, expressive, and robust...

  result <- sapply(1:10, sqrt) ```

• Best: vectorize; scales linearly, but at the compiled level so 100x faster

  r result <- sqrt(1:10)

Restriction and 'chunk' iteration

• e.g., ScanBamParam(), VcfParam()
• e.g., BamFile(..., yieldSize=1000000)

• See also GenomicFiles::reduceByYield()

  r library(GenomicAlignments) file <- system.file("extdata", "ex1.bam", package="Rsamtools") aln <- readGAlignments(file) # all data length(aln) bamfile <- open(BamFile(file, yieldSize=1500)) # chunks length(readGAlignments(file)) # better: yieldSize=1e6 length(readGAlignments(file)) length(readGAlignments(file)) length(readGAlignments(file))

Parallel evaluation: BiocParallel

• After writing efficient R code
• Very common paradigm: lapply-like

• Different 'back-ends', e.g., cores on a computer; computers in a cluster; instances in a cloud

  r library(BiocParallel) system.time({ res <- lapply(1:8, function(i) { Sys.sleep(1); i }) }) system.time({ res <- bplapply(1:8, function(i) { Sys.sleep(1); i }) })

• In Bioc-devel

  r X <- list(1, "2", 3) res <- bptry(bplapply(X, sqrt)) X <- list(1, 2, 3) res <- bplapply(X, sqrt, BPREDO=res)

Reproducibility: From Script to Package

Scripts

• ...Mature into re-usable functions
• 'Fit in your head'
• Poetry

Vignettes

• Like this document!
• Classic -- LaTeX-based

• Light weight / accessible: R-flavored markdown

  ---
  title: "A title"
  author: "An Author"
  vignette: >
    % \VignetteEngine{knitr::rmarkdown}
  ---
  
  # Heading
  
  ## Sub-heading
  
  Some text.
  
  ```r
  x <- rnorm(1000)
  plot(x)
  ```
  

• Check out the knitr package!

Packages

• Standard on-disk representation

  /MyPackage
  /MyPackage/DESCRIPTION
  /MyPackage/NAMESPACE
  /MyPackage/R/fun_one.R
  /MyPackage/R/fun_another.R
  /MyPackage/vignettes
  

• Very easy to create

• Very easy to share, e.g., github

Acknowledgments

Individuals

• Current and recent team members: Valerie Obenchain, Herve Pages, Dan Tenenbaum, Jim Hester, Jim Java, Brian Long, Sonali Arora, Nate Hayden, Paul Shannon
• Technical Advisory Board: Vince Carey, Wolfgang Huber, Sean Davis, Michael Lawrence, Levi Waldron, Raphael Irizzary, Robert Gentleman
• Scientific Advisory Board: Simon Tavare, Robert Gentleman, Vince Carey, Raphael Irizzary, Wolfgang Huber, Simon Urbanek.

Annual conference: https://bioconductor.org/BioC2016

• Stanford, June 24 - 26
• Morning scientific lectures
• Afternoon workshops
• Social and other activities
• Space filling up!

sessionInfo()

Bioconductor/ENAR2016 documentation built on May 6, 2019, 7:49 a.m.