For example, one co-splicing cluster has seven host genes: HNRNPUL1, HNRNPC, DHX9, BAT1, PSMA5, RAD23 and RPS9.
The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach.
A frequent co-splicing cluster is one that appears in multiple datasets, and appears as a heavy region of the tensor.
In all four types of enrichment results, the likelihood that a co-splicing cluster is biologically meaningful increases with its recurrence.
A co-splicing cluster appears as a heavy subgraph in the co-splicing network, which in turn corresponds to a heavy region in the adjacency matrix.
To identify possible splicing factors associated with a co-splicing cluster, for each exon of a co-splicing cluster, we retrieved the internal exon region and its 50bp flanking intron region which are enriched in the motifs of those 62 splicing factors by performing BLAST search (E-score < 0.001).
The exons in a splicing module can belong to different genes, but they exhibit correlated splicing patterns (in terms of being included or excluded in their respective transcripts) across different conditions, thus form an exon co-splicing cluster.
We found that some splicing factors tend to co-bind to the cis-regulatory regions of exons in a co-splicing cluster, suggesting the combinatorial regulation of those splicing factors.
The exons in a frequent co-splicing cluster can belong to different genes, but are very likely to be co-regulated by the same splicing factors, thus forming a splicing module.
The problem of discovering a frequent co-splicing cluster can be formulated as a discrete combinatorial optimization problem: among all patterns of fixed size (K1 member exons and K2 member networks), we look for the heaviest.
After discovering a pattern, we can mask its edges in those networks where they occur (replacing those elements of the tensor with zeroes) and optimize Eq. (2) again to search for the next frequent co-splicing cluster.
