Clique filtering is a technique used to remove “random-like” networks when working with binary similarity and very sparse data. Data types include copy number variations, which are few per patient.
Motivation section TBA
Output format of
A data.frame with per-network stats on clique-filtering:
- NETWORK: network name
- orig_pp: Num (+,+) interactions.
- orig_rest: Num (+,-) and (-,-) interactions
- ENR: Enrichment or bias of (+,+) interactions relative to other interactions. Specifically defined as
(orig_pp-orig_rest)/(orig_pp+orig_rest). Ranges between -1 (all non-(+,+)) to +1 (all (+,+)).
- TOTAL_INT: orig_pp+orig_rest (log-10 transformed)
- numPerm: num permutations done in clique filtering
- shuf_mu: mean ENR of permuted nets (i.e. mean null ENR)
- shuf_sigma: standard deviation of ENR of permuted nets (i.e. s.d. of null ENR)
- Z: Z-score of ENR in real network, relative to null distribution
- pctl: Percentile of ENR in real network, relative to null distribution. Also the p-value
- Q: Benjamini-Hochberg corrected pvalue.