Spatiotemporal ICA improves the selection of differentially expressed genes

Selecting differentially expressed genes with respect to some phenotype of interest is a difficult task, especially in the presence of confounding factors. We propose to use a spatiotemporal independent component analysis to model those factors, and to combine information from different spatiotemporal parameter values to improve the set of selected genes. We show on real datasets that the proposed method allows to significantly increase the proportion of genes related to the phenotype of interest in the final selection.

[1]  Andrew E. Teschendorff,et al.  Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies , 2011, Bioinform..

[2]  H. Ishwaran,et al.  Lung metastasis genes couple breast tumor size and metastatic spread , 2007, Proceedings of the National Academy of Sciences.

[3]  J. Bergh,et al.  Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series , 2007, Clinical Cancer Research.

[4]  J. Bergh,et al.  Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  H. Gunshin,et al.  A review of independent component analysis application to microarray gene expression data. , 2008, BioTechniques.

[6]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[7]  P. Hall,et al.  An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[8]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[9]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[10]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[11]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[12]  David M. Simcha,et al.  Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.

[13]  Pierre-Antoine Absil,et al.  Capturing confounding sources of variation in DNA methylation data by spatiotemporal independent component analysis , 2014, ESANN.