CAMUR: Knowledge Extraction from RNA-seq Cancer Data Through Equivalent Classification Rules

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2015 Nov 1

PMID 26519501

Citations 13

Authors

Valerio Cestarelli

Giulia Fiscon

Giovanni Felici

Paola Bertolazzi

Emanuel Weitschek

Affiliations

Soon will be listed here.

Abstract

Motivation: Nowadays, knowledge extraction methods from Next Generation Sequencing data are highly requested. In this work, we focus on RNA-seq gene expression analysis and specifically on case-control studies with rule-based supervised classification algorithms that build a model able to discriminate cases from controls. State of the art algorithms compute a single classification model that contains few features (genes). On the contrary, our goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the genes related to the predicted class.

Results: We propose CAMUR, a new method that extracts multiple and equivalent classification models. CAMUR iteratively computes a rule-based classification model, calculates the power set of the genes present in the rules, iteratively eliminates those combinations from the data set, and performs again the classification procedure until a stopping criterion is verified. CAMUR includes an ad-hoc knowledge repository (database) and a querying tool.We analyze three different types of RNA-seq data sets (Breast, Head and Neck, and Stomach Cancer) from The Cancer Genome Atlas (TCGA) and we validate CAMUR and its models also on non-TCGA data. Our experimental results show the efficacy of CAMUR: we obtain several reliable equivalent classification models, from which the most frequent genes, their relationships, and the relation with a particular cancer are deduced.

Availability And Implementation: dmb.iasi.cnr.it/camur.php

Contact: emanuel@iasi.cnr.it

Supplementary Information: Supplementary data are available at Bioinformatics online.

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References

Howe E, Sinha R, Schlauch D, Quackenbush J . RNA-Seq analysis in MeV. Bioinformatics. 2011; 27(22):3209-10. PMC: 3208390. DOI: 10.1093/bioinformatics/btr490. View

Lehr T, Yuan J, Zeumer D, Jayadev S, Ritchie M . Rule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies. BioData Min. 2011; 4:4. PMC: 3060133. DOI: 10.1186/1756-0381-4-4. View

Tothill R, Shi F, Paiman L, Bedo J, Kowalczyk A, Mileshkin L . Development and validation of a gene expression tumour classifier for cancer of unknown primary. Pathology. 2014; 47(1):7-12. DOI: 10.1097/PAT.0000000000000194. View

Pirooznia M, Yang J, Yang M, Deng Y . A comparative study of different machine learning methods on microarray gene expression data. BMC Genomics. 2008; 9 Suppl 1:S13. PMC: 2386055. DOI: 10.1186/1471-2164-9-S1-S13. View

Li B, Dewey C . RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011; 12:323. PMC: 3163565. DOI: 10.1186/1471-2105-12-323. View

Nogueira F, De Rosa Jr V, Menossi M, Ulian E, Arruda P . RNA expression profiles and data mining of sugarcane response to low temperature. Plant Physiol. 2003; 132(4):1811-24. PMC: 181268. DOI: 10.1104/pp.102.017483. View

Weinstein J, Collisson E, Mills G, Mills Shaw K, Ozenberger B, Ellrott K . The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013; 45(10):1113-20. PMC: 3919969. DOI: 10.1038/ng.2764. View

Tan A, Gilbert D . Ensemble machine learning on gene expression data for cancer classification. Appl Bioinformatics. 2004; 2(3 Suppl):S75-83. View

Walz A, Ooms A, Gadd S, Gerhard D, Smith M, Guidry Auvil J . Recurrent DGCR8, DROSHA, and SIX homeodomain mutations in favorable histology Wilms tumors. Cancer Cell. 2015; 27(2):286-97. PMC: 4800737. DOI: 10.1016/j.ccell.2015.01.003. View

10.

Uhlen M, Fagerberg L, Hallstrom B, Lindskog C, Oksvold P, Mardinoglu A . Proteomics. Tissue-based map of the human proteome. Science. 2015; 347(6220):1260419. DOI: 10.1126/science.1260419. View

11.

Hvidsten T, Laegreid A, Komorowski J . Learning rule-based models of biological process from gene expression time profiles using gene ontology. Bioinformatics. 2003; 19(9):1116-23. DOI: 10.1093/bioinformatics/btg047. View

12.

Weitschek E, Fiscon G, Felici G . Supervised DNA Barcodes species classification: analysis, comparisons and results. BioData Min. 2014; 7(1):4. PMC: 4022351. DOI: 10.1186/1756-0381-7-4. View

13.

Kuehn H, Liberzon A, Reich M, Mesirov J . Using GenePattern for gene expression analysis. Curr Protoc Bioinformatics. 2008; Chapter 7:7.12.1-7.12.39. PMC: 3893799. DOI: 10.1002/0471250953.bi0712s22. View

14.

Wang Z, Gerstein M, Snyder M . RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2008; 10(1):57-63. PMC: 2949280. DOI: 10.1038/nrg2484. View

15.

Shipp M, Ross K, Tamayo P, Weng A, Kutok J, Aguiar R . Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002; 8(1):68-74. DOI: 10.1038/nm0102-68. View

16.

Shaik R, Ramakrishna W . Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Plant Physiol. 2013; 164(1):481-95. PMC: 3875824. DOI: 10.1104/pp.113.225862. View

17.

Deb K, Raji Reddy A . Reliable classification of two-class cancer data using evolutionary algorithms. Biosystems. 2003; 72(1-2):111-29. DOI: 10.1016/s0303-2647(03)00138-2. View

18.

Mortazavi A, Williams B, McCue K, Schaeffer L, Wold B . Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008; 5(7):621-8. DOI: 10.1038/nmeth.1226. View

19.

Li T, Zhang C, Ogihara M . A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics. 2004; 20(15):2429-37. DOI: 10.1093/bioinformatics/bth267. View

20.

Geman D, dAvignon C, Naiman D, Winslow R . Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol. 2006; 3:Article19. PMC: 1989150. DOI: 10.2202/1544-6115.1071. View