TY - JOUR
T1 - Gene integrated set profile analysis
T2 - A context-based approach for inferring biological endpoints
AU - Kowalski, Jeanne
AU - Dwivedi, Bhakti
AU - Newman, Scott
AU - Switchenko, Jeffery M.
AU - Pauly, Rini
AU - Gutman, David A.
AU - Arora, Jyoti
AU - Gandhi, Khanjan
AU - Ainslie, Kylie
AU - Doho, Gregory
AU - Qin, Zhaohui
AU - Moreno, Carlos S.
AU - Rossi, Michael R.
AU - Vertino, Paula M.
AU - Lonial, Sagar
AU - Bernal-Mizrachi, Leon
AU - Boise, Lawrence H.
N1 - Funding Information:
Leukemia and Lymphoma Society Translational Research Program Award (to J.K.); Georgia Research Alliance Scientist Award (J.K.); a Team Science Seed Funding from the Winship Cancer Institute of Emory University (L.H.B., S.L.,M.R.R.); Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University and NIH/NCI [Award number P30CA138292, in part]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Funding for open access charge: Georgia Research Alliance Scientist Award.
Publisher Copyright:
© 2016 The Author(s).
PY - 2016/1/29
Y1 - 2016/1/29
N2 - The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.
AB - The identification of genes with specific patterns of change (e.g. down-regulated and methylated) as phenotype drivers or samples with similar profiles for a given gene set as drivers of clinical outcome, requires the integration of several genomic data types for which an 'integrate by intersection' (IBI) approach is often applied. In this approach, results from separate analyses of each data type are intersected, which has the limitation of a smaller intersection with more data types. We introduce a new method, GISPA (Gene Integrated Set Profile Analysis) for integrated genomic analysis and its variation, SISPA (Sample Integrated Set Profile Analysis) for defining respective genes and samples with the context of similar, a priori specified molecular profiles. With GISPA, the user defines a molecular profile that is compared among several classes and obtains ranked gene sets that satisfy the profile as drivers of each class. With SISPA, the user defines a gene set that satisfies a profile and obtains sample groups of profile activity. Our results from applying GISPA to human multiple myeloma (MM) cell lines contained genes of known profiles and importance, along with several novel targets, and their further SISPA application to MM coMMpass trial data showed clinical relevance.
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U2 - 10.1093/nar/gkv1503
DO - 10.1093/nar/gkv1503
M3 - Article
C2 - 26826710
AN - SCOPUS:84965154111
SN - 0305-1048
VL - 44
SP - e69
JO - Nucleic acids research
JF - Nucleic acids research
IS - 7
ER -