Supporting data for "Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks"
Dataset type: Network-Analysis, Genomic, Transcriptomic
Data released on February 05, 2019
Advances in large scale tumor sequencing have lead to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identification of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging. We introduce a novel combinatorial method, cd-CAP, for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not), and simultaneously detects multiple alteration profile conserved subnetworks. In a number of The Cancer Genome Atlas (TCGA) data sets, cd-CAP identified large biologically significant subnetworks with conserved alteration patterns, shared across many tumor samples.
Read the peer-reviewed publication(s):
Hodzic, E., Shrestha, R., Zhu, K., Cheng, K., Collins, C. C., & Cenk Sahinalp, S. (2019). Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks. GigaScience, 8(4). doi:10.1093/gigascience/giz024