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Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.

Authors
  • Tang, Kailin1
  • Ji, Xuejie1
  • Zhou, Mengdi1
  • Deng, Zeliang1
  • Huang, Yuwei1, 2
  • Zheng, Genhui1
  • Cao, Zhiwei1
  • 1 Department of Gastroenterology, Shanghai 10th People's Hospital and School of Life Sciences and Technology, Tongji University, 1239 Siping Road, Shanghai 200092, P.R. China. , (China)
  • 2 CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, P.R. China. , (China)
Type
Published Article
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Publication Date
Sep 27, 2021
Volume
49
Issue
17
Identifiers
DOI: 10.1093/nar/gkab554
PMID: 34214174
Source
Medline
Language
English
License
Unknown

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. © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

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