TY - JOUR
T1 - Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data
AU - Akter, Sadia
AU - Xu, Dong
AU - Nagel, Susan C.
AU - Bromfield, John J.
AU - Pelch, Katherine
AU - Wilshire, Gilbert B.
AU - Joshi, Trupti
N1 - Publisher Copyright:
© Copyright © 2019 Akter, Xu, Nagel, Bromfield, Pelch, Wilshire and Joshi.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/9/4
Y1 - 2019/9/4
N2 - Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
AB - Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
KW - DNA methylation
KW - RNA-seq
KW - classification
KW - endometriosis
KW - machine learning
KW - methylomics
KW - transcriptomics
KW - translational bioinformatics
UR - http://www.scopus.com/inward/record.url?scp=85072829656&partnerID=8YFLogxK
U2 - 10.3389/fgene.2019.00766
DO - 10.3389/fgene.2019.00766
M3 - Article
AN - SCOPUS:85072829656
VL - 10
JO - Frontiers in Genetics
JF - Frontiers in Genetics
SN - 1664-8021
M1 - 766
ER -