Open AccessSSIG: Single‐Sample Information Gain Model for Integrating Multi‐Omics Data to Identify Cancer Subtypes
Author(s) -
Yuanyuan Zhang,
Ziqi Wang,
Shudong Wang,
Chuanhua Kou
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.01.011
Subject(s) - cancer , sample (material) , identification (biology) , omics , computer science , mechanism (biology) , computational biology , data mining , cluster analysis , bioinformatics , biology , machine learning , genetics , philosophy , chemistry , botany , epistemology , chromatography
Different living environments of cancer samples lead to different molecular mechanisms of cancer development, which in turn leads to different cancer subtypes. How to identify cancer subtypes is a key issue for the realization of precision medicine. With the development of high‐throughput technologies, multi‐omics data which can better understand different causes of cancer have emerged. However, the current methods of analyzing cancer subtypes using multi‐omics data is mostly derived from population cancer sample data and ignores the differences between different cancer samples. Therefore, the joint analysis of multi‐omics based on a single sample may reveal more information about the differences between individual cancers. A strategy for identifying cancer subtypes is proposed based on Single‐sample information gain (SSIG) which construct sample feature matrix by considering the heterogeneity of sample. Applying this strategy to current popular subtype identification methods, cancer subtypes can be identified more accurately and the mechanism of cancer can be found from the perspective of a single sample. By comparing different methods in different clustering measure, and using survival analysis, it is shown that SSIG is more suitable for cancer subtype identification than the original multi‐omics data, and it is easier to mine the cancer subtype classification mechanism hidden behind the data.