Martin Morgan, Sonali Arora

February 3, 2015

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.

Record co-variates

Be aware of *batch effects*

- 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*packageHapMap samples from one facility, ordered by date of processing.

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, …

Axes of variation

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

Application-specific, e.g.,

- ChIP-seq: short, single-end reads are usually sufficient
- RNA-seq, known genes: single- or paired-end reads
- RNA-seq, transcripts or novel variants: paired-end reads
- 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 strategies

*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)
- GMAP/GSNAP (
*gmapR*) - subread (doi)
(
*Rsubread*) - Systematic evaluation (Engstrom et al., 2013, doi)

- 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

- 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()`

- HTSeq, htseq-count

Summarization

- Counts
*per se*, rather than a summary (RPKM, FRPKM, …), 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.

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; work flow today.

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 typical null hypothesis used in microarray or RNA-seq experiments, each gene may respond differently to the treatment (numerator variance) but the 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.

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, …

Correlation between genomic copy number and mRNA expression identified 38 mis-labeled samples in the TCGA ovarian cancer Affymetrix microarray dataset.

The lab is based on a modified version of the RNA-seq work flow developed by Michael Love, Simon Anders, Wolfgang Huber.