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

Hodzic E; Shrestha R; Zhu K; Cheng K; Collins CC; Sahinalp SC (2019): Supporting data for "Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks" GigaScience Database. http://dx.doi.org/10.5524/100561

DOI10.5524/100561

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.

Additional details

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

Additional information:

https://github.com/ehodzic/cd-CAP

https://portal.gdc.cancer.gov/





File NameSample IDData TypeFile FormatSizeRelease Date 
Tabular dataUNKNOWN17.26 KB2019-01-31
Tabular dataUNKNOWN7.23 KB2019-01-31
Tabular dataUNKNOWN9.53 KB2019-01-31
GitHub archivearchive5.09 MB2019-01-29
readmeTEXT2.39 KB2019-01-29
Displaying 1-5 of 5 File(s).
Funding body Awardee Award ID Comments
Indiana University SC Sahinalp The Indiana University Grant Challenges Program Precision Health Initiative
NIH SC Sahinalp 1R01GM108348
NSF SC Sahinalp CCF-1619081
Date Action
February 13, 2019 Dataset publish
February 13, 2019 Description updated from : 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 identi¬cation 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 identi¬fied large biologically signi¬cant subnetworks with conserved alteration patterns, shared across many tumor samples.
February 13, 2019 Modification date added : 2019-02-13
March 4, 2019 Manuscript Link added : 10.1093/gigascience/giz024