There was great heterogeneity within both series in terms of sample-wise amounts of deviating exon usage.
Sample-wise amounts of exons with aberrant splicing patterns were summarized from the exons exceeding these threshold values.
Sample-wise amounts of aberrant exon skipping and inclusion were calculated as the number of exons exceeding these threshold values (Additional file 1: Table S1).
This provided the basis for calculations of sample-wise amounts of aberrant exon skipping and inclusion within the datasets, as we have also previously described [ 20].
These sample-wise amounts of aberrant splicing, relative to the average amount within the dataset, are referred to as TIN-estimates.
These values are referred to as relative amounts of deviating skipping, inclusion or exon usage, the latter representing the combination of the two former, that is, the total sample-wise amounts of differential exon usage.
Common cancers of the breast, cervix, large bowel, lung, and prostate all show large variation in sample-wise amounts of aberrant exon skipping and inclusion (TIN-estimates) that are significant negatively correlated with the expression levels of splicing factor genes.
Although the approach taken here to analyze sample-wise amounts of deviating exon usage was shown to correctly classify known splicing events, the main purpose of the study was to describe splicing on a genome-wide scale and does not allow for detailed analysis of individual transcript structures and individual splicing events.
Amounts of aberrant exon skipping or inclusion per sample are presented on a log2-scale, relative to the average sample-wise amount within the individual datasets.
The oTIN subtype, reflecting sample-wise total relative amounts of deviating exon usage, is negatively correlated to the expression level of the majority of splicing factor encoding genes.
Based on alternative splicing scores across the genomes, we calculated sample-wise relative amounts of aberrant exon skipping and inclusion.
