Differential Expression Talk

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Goals

Three overall:

Primarily, it's about:

  • Identify differentially expressed genes in two or more conditions (e.g. normal v cancer)

Generally, it's about:

  • Gain biological insight into which genes cause / respond to a condition

And with an eye towards future project: looking for more promising places to look:

  • Identify biomarkers for a condition

Three principal themes

  1. Data normalisation
  2. Data quality control
  3. Differential expression analysis

Data filtering

  • Due to random noise / sampling errors, genes with low read counts across all samples cannot be found to be differentially expressed
  • Removing these:
- reduces amount of data
- improves speed of analysis
- reduces number of genes to be counted in multiple test correction

Data normalisation

What affects read count? Read count not only affected by:

  • level of transcription

but also by:

  • Between genes
- length of gene
- GC content
  • Between libraries
- sequencing depth (library size)
- RNA composition

RNA composition

  • A few extremely highly expressed genes may contribute a very large part of the sequenced reads
  • Changes in the expression of these change the relative abundance of all other genes

Normalisation methods

Dillies.png

  • Total Count (TC)
- TC = reads mapping to gene / total reads in library
  • Other methods of normalising counts:
- Reads per Kilobase per Million mapped reads (RPKM)
- Upper Quartile (UQ)
- Median (Med)
- DESeq
- Trimmed Mean of M-values (TMM) (used by edgeR)
- Quantile (Q)

Normalisation Example

  • Consider two samples
  • Almost all genes have identical read counts in library 1 and library 2
  • A few genes are highly expressed in library 2
  • How should library 2 be normalised to make it comparable to library 1?
  • Correct normalisation factor would be 1 (no change)

Normalisation Example

Normcount1.png

Trimmed Mean of M-values (TMM)

Normcount2.png

Normalisation conclusion

  • Dillies et al. conclude that only TMM and DESeq can cope with large changes in highly expressed genes.
  • These lean on the assumption that:
- the majority of genes are not differentially expressed
- for those differentially expressed, there is an approxmiately balanced proportion of over- and under-expression.

Data quality control

  • Do (technical and biological) replicates cluster together?
  • we can see on an MDS plot:
- Shows the level of similarity of individual cases of a dataset
- Distances represent fold-changes
  • Dataset: 10 patients
- Cancerous samples
- Non-cancerous samples

Plotting the samples 1

Mda1.png

  • A Multidimensional scaling plot is in fact a PCA Principle Component plot with the first two dimension.
  • These are the dimensions internal to the data where most variation in values is seen.
  • The distances here represent fold-changes.
  • Ten patients
- Cancerous samples in red
- Non-cancerous samples in black

Plotting the samples 2

Mda2.png

  • Multiple samples from the same patient cluster together

Plotting the samples 3

Mda3.png

  • Cancerous samples cluster together
  • Non-cancerous samples cluster together
- though not a very tight separation between the two

Plotting the samples 4

Mda4.png

  • Removing two patients improves the separation
  • Two out of ten patients: maybe not justified.

Differential expression methods

  • For each gene, two measures of expression level will show up:
- between the two groups of samples
- within groups of samples
  • Might the difference within groups of samples be big enough to explain the difference between groups of samples?

Differential expression methods

Singg1.png

  • Cancer samples in red
- Mean logcount is 116
  • Non cancer samples in black
- Mean logcount is 132

Differential expression methods

  • Count based:
– most tools
  • Coverage based:
– Cuffdiff
  • Methods may be parametric or non-parametric
- non-parametric build up their own parameters from the data.
  • Some tools allow a variety of experimental designs

Differential expression methods

  • Parametric methods
– e.g. edgeR, DESeq
– assume a negative binomial distribution to account for biological variation
– have problems when the data don’t fit this distribution
  • Non-parametric methods
– e.g. SAMseq and NOISeq
– need to learn the distribution from the data
– may require more replicates

edgeR

  • assumes that normalised counts for each gene across biological replicates follows a negative binomial distribution with the dispersion representing the biological variation
  • calculates a dispersion factor for each gene
  • calculates a dispersion factor that fits the data as a whole

Diversity of low count

Bcv1.png

  • Genes with fewer counts can appear to be highly variable due to sampling errors

Two types of comparsions

Expdes.png

Grouped comparisons

Groupcomp.png

Matched-pair comparison

Matchcomp.png

edgeR output

Outp.png

P-values

Testing 100 genes for DE ...

Pval.png

Add FDR to P-values

Testing 100 genes for DE ...

Pvalfdr.png

MA Plot comparison

Twoma.png

Summary

  • Before differential expression analysis is done there are multiple initial steps
  • Data must be filtered, normalised and outliers removed
  • A variety of techniques to both normalise data and call differentially expressed genes are used
  • Understanding of the experimental design is important
  • Different techniques can give different results, especially for low numbers of replicates, noisy data and lowly expressed genes
  • No standard way of doing any of this, best practices are still evolving.

Further reading

  • Dillies et al "A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis” Brief Bioinform. 2013 Nov;14(6):671-83.
  • Soneson and Delorenzi "A comparison of methods for differential expression analysis of RNA-seq data.” BMC Bioinformatics. 2013 Mar 9;14:91.
  • Rapaport et al "Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.” Genome Biol. 2013;14(9):R95.
  • Huang et al "RNA-Seq analyses generate comprehensive transcriptomic landscape and reveal complex transcript paLerns in hepatocellular carcinoma.” PLoS One 2011 17;6(10):e26168.