Multiclass prediction with partial least square regression for gene expression data: Applications in breast cancer intrinsic taxonomy
Journal
BioMed Research International
Journal Volume
2013
Pages
248648
Date Issued
2013
Author(s)
Abstract
Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS) regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n = 535). The agreement between PAM50 centroid-based single sample prediction (SSP) and PLS-regression was excellent (weighted Kappa: 0.988) within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed). Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes. ? 2013 Chi-Cheng Huang et al.
SDGs
Other Subjects
article; breast cancer; breast cancer intrinsic taxonomy; cancer classification; cancer prognosis; gene expression; gene structure; human; human tissue; major clinical study; microarray analysis; molecular systematics; partial least squares regression; prediction; biosynthesis; breast tumor; classification; female; gene expression regulation; genetics; pathology; prognosis; regression analysis; transcriptome; tumor marker; Breast Neoplasms; Female; Gene Expression Regulation, Neoplastic; Humans; Least-Squares Analysis; Prognosis; Transcriptome; Tumor Markers, Biological
Publisher
Hindawi Publishing Corporation
Type
journal article