Analyze RNA-Seq expression in vicinity of integration events.
Evaluate the expression of case versus controls, of which there are typically many.
- In
L_Expressionstage, obtain expression data from RNA-Seq BAMs directly (RPKM format). - An alternate workflow is illustrated in stage
M_RSEM_Expressionwhich uses pre-processed RSEM expression data, with no need for sequence (RNA-Seq) data.
Either L_Expression or M_RSEM_Expression stage is required for BPS expression plots, but not necessary for structure plots.
- For each case, create BED file which indicates regions of interest where RPKM data will be evaluated. Regions of interest are all exons belonging to genes within some distance - here, 1Mbp - of integration event.
- Analyze RNA-Seq data for both cases (sample with integration event in given region)
and controls (all other samples with same cancer type as case) to obtain RPKM, a normalized count of
RNA-Seq reads mapped to regions of interest. Note that this step reads RNA-Seq BED files.
- Raw RNA-Seq reads are counted for each exon using
bedtools coverage. Per-exon RPKM is evaluated as (10^9 * R)/(N * L), where R is the number of raw reads mapped to an exon, N is the count of total mapped reads, and L is exon length.
- Raw RNA-Seq reads are counted for each exon using
- Evaluate expression of case exons vs. control. Combine exon expression to obtain per-gene p-value
which quantifies degree to which gene expression differs in case vs. controls.
- Specifically, we report the probability that expression of case is drawn from the same distribution as expression of controls.
- See
$BPS_CORE/src/analysis/ExonExpressionAnalyzer.R - Algorithm details available here.
The third step can work with other expression data, e.g. RSEM data as illustrated in M_RSEM_Expression.