This guide provides an overview and example of how to calculate differential Allele-Specific Expression (ASE) as described in “Differential Allele-Specific Expression Uncovers Breast Cancer Genes Dysregulated by Cis Noncoding Mutation.”
In order to use the method, simply source “diffASEfunctions.R” in R. The functions to compute differential ASE depend on the R packages: “metap”, “limma”, and “biomaRt”
source("diffASEfunctions.R")The main data required to calculate differential ASE are allele counts at heterozygous sites in a tumor and matched normal sample. These can be found using the package “AllelicImbalance” in R with the function “getAlleleCounts” called on BAM files for each sample. Make sure to use the same set of heterozygous sites for both the tumor and matched normal files. Heterozygous sites can be found using “scanForHeterozygotes” from the same package.
Example allele counts should look as follows, with columns for Entrez gene id, sample name, genomic location, normal sample primary allele count, normal sample total read count, tumor sample primary allele count, tumor sample total read count:
| V1 | V2 | V3 | V4 | V5 | V6 | V7 |
|---|---|---|---|---|---|---|
| 1 | sample1 | chr19:58346952 | 17 | 25 | 19 | 26 |
| 1 | sample2 | chr19:58346952 | 12 | 19 | 21 | 25 |
Using just this ASE data we can compute baseline differential ASE:
computeASEbaseline(testASE)| sample | gene | pval | ASE |
|---|---|---|---|
| sample1 | 1 | 0.5789254 | 0.0507692 |
| sample2 | 1 | 0.0307450 | 0.2084211 |
Next, to compute the purity adjusted differentual ASE, we need a tumor purity for each sample. For TCGA data many of these have been pre-computed in the paper “Systematic pan-cancer analysis of tumour purity.” These should be stored in a table with the sample name in the first column and the purity in the second.
| V1 | V2 |
|---|---|
| sample1 | 0.8 |
| sample2 | 0.9 |
Given that purity data, we can compute diffASE-purity:
computeASEpurity(testASE, allPurity)| sample | gene | pval | ASE |
|---|---|---|---|
| sample1 | 1 | 0.5349559 | 0.0634615 |
| sample2 | 1 | 0.0227733 | 0.2315789 |
Finally, expression adjusted differential ASE can be computed if expression values for each gene in each sample are known. This requires two tables: one for cpm counts for the normal sample and one with cpm counts for the matched tumor sample. These should have the Ensembl name of the gene as the row name and a column for each sample.
| sample1 | sample2 | |
|---|---|---|
| ENSG00000121410 | 1.2 | 2.1 |
| sample1 | sample2 | |
|---|---|---|
| ENSG00000121410 | 2 | 1.9 |
With that tumor purity and expression data, we can comute diffASE-exp:
computeASEexp(testASE, allPurity, cpmTumor, cpmNormal)| sample | gene | pval | ASE |
|---|---|---|---|
| sample1 | 1 | 0.4967081 | 0.0761538 |
| sample2 | 1 | 0.0234966 | 0.2291540 |
For comparison to differential ASE, we can also compute tumor sample ASE to see that sometimes ASE looks significant without the context of the matched tumor sample:
computeTumorSampleASE(testASE)| sample | gene | pval | ASE |
|---|---|---|---|
| sample1 | 1 | 0.0186029 | 0.2307692 |
| sample2 | 1 | 0.0006739 | 0.3400000 |