期刊
BRIEFINGS IN BIOINFORMATICS
卷 21, 期 5, 页码 1818-1824出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz116
关键词
gene expression; cancer subtyping; batch effect; cross-platform
资金
- National Key R&D Program of China [2017YFA0106700]
- National Natural Science Foundation of China [31771462, 81772614, 31471252, 31500813, U1611261, 81802438]
- Programfor Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07S096]
- Guangdong Natural Science Foundation [2017A030313134]
- Pearl River S&T Nova Program of Guangzhou [201906010088]
Unsupervised clustering of high-throughput gene expression data is widely adopted for cancer subtyping. However, cancer subtypes derived from a single dataset are usually not applicable across multiple datasets from different platforms. Merging different datasets is necessary to determine accurate and applicable cancer subtypes but is still embarrassing due to the batch effect. CrossICC is an R package designed for the unsupervised clustering of gene expression data from multiple datasets/platforms without the requirement of batch effect adjustment. CrossICC utilizes an iterative strategy to derive the optimal gene signature and cluster numbers from a consensus similarity matrix generated by consensus clustering. This package also provides abundant functions to visualize the identified subtypes and evaluate subtyping performance. We expected that CrossICC could be used to discover the robust cancer subtypes with significant translational implications in personalized care for cancer patients.
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