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      CrossICC: iterative consensus clustering of cross-platform gene expression data without adjusting batch effect

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          Abstract

          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.

          Availability and Implementation

          The package is implemented in R and available at GitHub (https://github.com/bioinformatist/CrossICC) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/CrossICC.html) under the GPL v3 License.

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          Author and article information

          Journal
          Briefings in Bioinformatics
          Oxford University Press (OUP)
          1467-5463
          1477-4054
          November 06 2019
          November 06 2019
          Affiliations
          [1 ]State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 E Dongfeng Road, Guangzhou, Guangdong, 510060, China
          [2 ]School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, 510060, China
          Article
          10.1093/bib/bbz116
          32978617
          94329edf-fadf-4ef5-8bac-6ed09eca53e8
          © 2019

          https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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