TY - JOUR
T1 - CaMoDi
T2 - A new method for cancer module discovery
AU - Manolakos, Alexandros
AU - Ochoa, Idoia
AU - Venkat, Kartik
AU - Goldsmith, Andrea J.
AU - Gevaert, Olivier
N1 - Funding Information:
The publication costs for this article were funded by the NSF Center for Science of Information (CSoI) grant agreement CCF-0939370. This article has been published as part of BMC Genomics Volume 15 Supplement 10, 2014: Proceedings of the 25th International Conference on Genome Informatics (GIW/ISCB-Asia): Genomics. The full contents of the supplement are available online at http://www.biomedcentral.com/ bmcgenomics/supplements/15/S10.
Funding Information:
Acknowledgements This work is supported by the NSF Center for Science of Information (CSoI) grant agreement CCF0939370, the Stanford Cancer Target Discovery and Development Center (CTD, U01 CA176299), the Center for Cancer Systems Biology (CCSB) at Stanford (U54 CA149145), R01 CA184968 and R01 CA160251, two Stanford Graduate Fellowships and by a fellowship from the Basque country. The authors would like to thank Tsachy Weissman for helpful discussions.
Funding Information:
This work is supported by the NSF Center for Science of Information (CSoI) grant agreement CCF0939370, the Stanford Cancer Target Discovery and Development Center (CTD, U01 CA176299), the Center for Cancer Systems Biology (CCSB) at Stanford (U54 CA149145), R01 CA184968 and R01 CA160251, two Stanford Graduate Fellowships and by a fellowship from the Basque country. The authors would like to thank Tsachy Weissman for helpful discussions.
Publisher Copyright:
© 2014 Manolakos et al.
PY - 2014
Y1 - 2014
N2 - Background: Identification of genomic patterns in tumors is an important problem, which would enable the community to understand and extend effective therapies across the current tissue-based tumor boundaries. With this in mind, in this work we develop a robust and fast algorithm to discover cancer driver genes using an unsupervised clustering of similarly expressed genes across cancer patients. Specifically, we introduce CaMoDi, a new method for module discovery which demonstrates superior performance across a number of computational and statistical metrics. Results: The proposed algorithm CaMoDi demonstrates effective statistical performance compared to the state of the art, and is algorithmically simple and scalable - which makes it suitable for tissue-independent genomic characterization of individual tumors as well as groups of tumors. We perform an extensive comparative study between CaMoDi and two previously developed methods (CONEXIC and AMARETTO), across 11 individual tumors and 8 combinations of tumors from The Cancer Genome Atlas. We demonstrate that CaMoDi is able to discover modules with better average consistency and homogeneity, with similar or better adjusted R2 performance compared to CONEXIC and AMARETTO. Conclusions: We present a novel method for Cancer Module Discovery, CaMoDi, and demonstrate through extensive simulations on the TCGA Pan-Cancer dataset that it achieves comparable or better performance than that of CONEXIC and AMARETTO, while achieving an order-of-magnitude improvement in computational run time compared to the other methods.
AB - Background: Identification of genomic patterns in tumors is an important problem, which would enable the community to understand and extend effective therapies across the current tissue-based tumor boundaries. With this in mind, in this work we develop a robust and fast algorithm to discover cancer driver genes using an unsupervised clustering of similarly expressed genes across cancer patients. Specifically, we introduce CaMoDi, a new method for module discovery which demonstrates superior performance across a number of computational and statistical metrics. Results: The proposed algorithm CaMoDi demonstrates effective statistical performance compared to the state of the art, and is algorithmically simple and scalable - which makes it suitable for tissue-independent genomic characterization of individual tumors as well as groups of tumors. We perform an extensive comparative study between CaMoDi and two previously developed methods (CONEXIC and AMARETTO), across 11 individual tumors and 8 combinations of tumors from The Cancer Genome Atlas. We demonstrate that CaMoDi is able to discover modules with better average consistency and homogeneity, with similar or better adjusted R2 performance compared to CONEXIC and AMARETTO. Conclusions: We present a novel method for Cancer Module Discovery, CaMoDi, and demonstrate through extensive simulations on the TCGA Pan-Cancer dataset that it achieves comparable or better performance than that of CONEXIC and AMARETTO, while achieving an order-of-magnitude improvement in computational run time compared to the other methods.
UR - http://www.scopus.com/inward/record.url?scp=84964314422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964314422&partnerID=8YFLogxK
U2 - 10.1186/1471-2164-15-S10-S8
DO - 10.1186/1471-2164-15-S10-S8
M3 - Article
C2 - 25560933
AN - SCOPUS:84964314422
SN - 1471-2164
VL - 15
JO - BMC Genomics
JF - BMC Genomics
M1 - S8
ER -