April 2021
Differential Gene Expression Analysis Workflow
Sources of Noise
Sources of Noise - Sampling Bias
Sources of Noise - Transcript Length
The length of the transcript affects the number of RNA fragments present in the library from that gene.
Sources of Noise - Sequencing Artefacts
Normalisation
Counting estimates the relative counts for each gene
Does this accurately represent the original population of RNAs?
The relationship between counts and RNA expression is not the same for all genes across all samples
Library Size
Differing sequencing depth
Gene properties
Length, GC content, sequence
Library composition
Highly expressed genes overrepresented at the cost of lowly expressed genes
“Composition Bias”
Normalisation - scaling
Library Size
Normalise each sample by total number of reads sequenced.
Can also use another statistic similar to total count eg. median, upper quartile
Does not account for composition bias
DESeq2 analysis workflow
Normalisation - Geometric mean scaling factor
- Used by DESeq2
- For each gene calculate the geometric mean across all samples
- For each gene in each sample, normalise by dividing by the geometric mean for that gene
- For each sample calculate the scaling factor as the median of the normalised counts
Differential Expression
Comparing feature abundance under different conditions
Assumes linearity of signal
When feature=gene, well-established pre- and post-analysis strategies exist
Mortazavi, A. et al (2008) Nature Methods
Differential Expression
Simple difference in means
Replication introduces variation
Differential Expression - Modelling population distributions
Normal (Gaussian) Distribution - t-test
Two parameters - \(mean\) and \(sd\) (\(sd^2 = variance\))
Suitable for microarray data but not for RNAseq data
Differential Expression - Modelling population distributions
Count data - Poisson distribution
One parameter - \(mean\) \((\lambda)\)
\(variance\) = \(mean\)
Differential Expression - Modelling population distributions
Use the Negative Binomial distribution
In the NB distribution \(mean\) not equal to \(variance\)
Two paramenters - \(mean\) and \(dispersion\)
\(dispersion\) describes how \(variance\) changes with \(mean\)
Anders, S. & Huber, W. (2010) Genome Biology
Differential Expression - estimating dispersion
Estimating the dispersion parameter can be difficult with a small number of samples
DESeq2 models the variance as the sum of technical and biological variance
Esimate dispersion for each gene
‘Share’ dispersion information between genes to obtain fitted estimate
Shrink gene-wise estimates towards the fitted estimates
Differential Expression - worrying dispersion plot examples
Bad dispersion plots from: https://github.com/hbctraining/DGE_workshop
Differential Expression - linear models
Calculate coefficients describing change in gene expression
Linear Model \(\rightarrow\) General Linear Model
Towards biological meaning - hierachical clustering
Towards biological meaning - Gene Ontology testing
Towards biological meaning - Gene Set Enrichment Analysis
http://software.broadinstitute.org/gsea
Towards biological meaning - Pathway Analysis
More Depth or More Reps?
Liu et al. (2014) Bioinformatics
Thank you