Rank-in: Enabling Integrative Analysis Across Microarray and RNA-seq for Cancer

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2021 Jul 2

PMID 34214174

Citations 27

Authors

Kailin Tang

Xuejie Ji

Mengdi Zhou

Zeliang Deng

Yuwei Huang

Genhui Zheng

Zhiwei Cao

Affiliations

Soon will be listed here.

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.

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References

Wang P, Yang Y, Han W, Ma D . ImmuSort, a database on gene plasticity and electronic sorting for immune cells. Sci Rep. 2015; 5:10370. PMC: 4437374. DOI: 10.1038/srep10370. View

Tang Z, Han L, Lin H, Cui J, Jia J, Low B . Derivation of stable microarray cancer-differentiating signatures using consensus scoring of multiple random sampling and gene-ranking consistency evaluation. Cancer Res. 2007; 67(20):9996-10003. DOI: 10.1158/0008-5472.CAN-07-1601. View

Johnson W, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006; 8(1):118-27. DOI: 10.1093/biostatistics/kxj037. View

Dembele D . A Flexible Microarray Data Simulation Model. Microarrays (Basel). 2016; 2(2):115-30. PMC: 5003477. DOI: 10.3390/microarrays2020115. View

Xu L, Luo H, Wang R, Wu W, Phue J, Shen R . Novel reference genes in colorectal cancer identify a distinct subset of high stage tumors and their associated histologically normal colonic tissues. BMC Med Genet. 2019; 20(1):138. PMC: 6693228. DOI: 10.1186/s12881-019-0867-y. View

Shahjaman M, Mollah M, Rahman M, Islam S, Haque Mollah M . Robust identification of differentially expressed genes from RNA-seq data. Genomics. 2019; 112(2):2000-2010. DOI: 10.1016/j.ygeno.2019.11.012. View

Cordero D, Sole X, Crous-Bou M, Sanz-Pamplona R, Pare-Brunet L, Guino E . Large differences in global transcriptional regulatory programs of normal and tumor colon cells. BMC Cancer. 2014; 14:708. PMC: 4182786. DOI: 10.1186/1471-2407-14-708. View

Hoyle D, Rattray M, Jupp R, Brass A . Making sense of microarray data distributions. Bioinformatics. 2002; 18(4):576-84. DOI: 10.1093/bioinformatics/18.4.576. View

Caracausi M, Piovesan A, Antonaros F, Strippoli P, Vitale L, Pelleri M . Systematic identification of human housekeeping genes possibly useful as references in gene expression studies. Mol Med Rep. 2017; 16(3):2397-2410. PMC: 5548050. DOI: 10.3892/mmr.2017.6944. View

10.

Zhu Y, Qiu P, Ji Y . TCGA-assembler: open-source software for retrieving and processing TCGA data. Nat Methods. 2014; 11(6):599-600. PMC: 4387197. DOI: 10.1038/nmeth.2956. View

11.

Leek J, Storey J . Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007; 3(9):1724-35. PMC: 1994707. DOI: 10.1371/journal.pgen.0030161. View

12.

Risso D, Ngai J, Speed T, Dudoit S . Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol. 2014; 32(9):896-902. PMC: 4404308. DOI: 10.1038/nbt.2931. View

13.

Thompson J, Tan J, Greene C . Cross-platform normalization of microarray and RNA-seq data for machine learning applications. PeerJ. 2016; 4:e1621. PMC: 4736986. DOI: 10.7717/peerj.1621. View

14.

Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W . An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era. Genome Biol. 2015; 15(12):523. PMC: 4290828. DOI: 10.1186/s13059-014-0523-y. View

15.

Zhang Y, Parmigiani G, Johnson W . : batch effect adjustment for RNA-seq count data. NAR Genom Bioinform. 2020; 2(3):lqaa078. PMC: 7518324. DOI: 10.1093/nargab/lqaa078. View

16.

Leek J, Johnson W, Parker H, Jaffe A, Storey J . The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28(6):882-3. PMC: 3307112. DOI: 10.1093/bioinformatics/bts034. View

17.

Li S, Labaj P, Zumbo P, Sykacek P, Shi W, Shi L . Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat Biotechnol. 2014; 32(9):888-95. PMC: 4160374. DOI: 10.1038/nbt.3000. View

18.

Chen W, Zhang S, Williams J, Ju B, Shaner B, Easton J . A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing. Comput Struct Biotechnol J. 2020; 18:861-873. PMC: 7163294. DOI: 10.1016/j.csbj.2020.03.026. View

19.

Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley D . Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012; 7(3):562-78. PMC: 3334321. DOI: 10.1038/nprot.2012.016. View

20.

Bradford J, Hey Y, Yates T, Li Y, Pepper S, Miller C . A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling. BMC Genomics. 2010; 11:282. PMC: 2877694. DOI: 10.1186/1471-2164-11-282. View