Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer. Issue 17 (2nd July 2021)

• Record Type: Journal Article
• Authors: Tang, Kailin; Ji, Xuejie; Zhou, Mengdi; Deng, Zeliang; Huang, Yuwei; Zheng, Genhui; Cao, Zhiwei
• Is Part Of: Nucleic acids research. Volume 49:Issue 17(2021)
• Page Start: e99
• Page End: e99
• Publication Date: 2021-07-02
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab554
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html .