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Neuroimage. 2014 Nov 15;102 Pt 1:220-8. doi: 10.1016/j.neuroimage.2014.01.021. Epub 2014 Feb 12.

Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs.

Author information

1
Unit on Statistical Genomics, Intramural Program of Research, National Institute of Mental Health, NIH, Bethesda, 20852, USA.
2
Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA; Department of Biostatistics & Bioinformatics, Tulane University, New Orleans, LA, USA.
3
Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA.
4
The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering at the University of New Mexico, Albuquerque, NM, USA.
5
Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA; Department of Biostatistics & Bioinformatics, Tulane University, New Orleans, LA, USA. Electronic address: wyp@tulane.edu.

Abstract

Integrative analysis of multiple data types can take advantage of their complementary information and therefore may provide higher power to identify potential biomarkers that would be missed using individual data analysis. Due to different natures of diverse data modality, data integration is challenging. Here we address the data integration problem by developing a generalized sparse model (GSM) using weighting factors to integrate multi-modality data for biomarker selection. As an example, we applied the GSM model to a joint analysis of two types of schizophrenia data sets: 759,075 SNPs and 153,594 functional magnetic resonance imaging (fMRI) voxels in 208 subjects (92 cases/116 controls). To solve this small-sample-large-variable problem, we developed a novel sparse representation based variable selection (SRVS) algorithm, with the primary aim to identify biomarkers associated with schizophrenia. To validate the effectiveness of the selected variables, we performed multivariate classification followed by a ten-fold cross validation. We compared our proposed SRVS algorithm with an earlier sparse model based variable selection algorithm for integrated analysis. In addition, we compared with the traditional statistics method for uni-variant data analysis (Chi-squared test for SNP data and ANOVA for fMRI data). Results showed that our proposed SRVS method can identify novel biomarkers that show stronger capability in distinguishing schizophrenia patients from healthy controls. Moreover, better classification ratios were achieved using biomarkers from both types of data, suggesting the importance of integrative analysis.

KEYWORDS:

SNP; Schizophrenia; Sparse representations; Variable selection; fMRI

PMID:
24530838
PMCID:
PMC4130811
DOI:
10.1016/j.neuroimage.2014.01.021
[Indexed for MEDLINE]
Free PMC Article

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