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

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 nature 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: 759075 SNPs and 153594 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 univariant 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.

Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure

“…Considering n  ≫  m , we employed a SRVS method, proposed by [10] to solve Eq. (2) and identify potential biomarkers (gene expressions/SNPs) associated with BP.…”

“…The SRVS method has been shown to be feasible in identifying schizophrenia candidate biomarkers, while integrating functional magnetic resonance imaging data and SNP data [10]. It has also been demonstrated that the use of multiple data types may provide higher power to identify potential biomarkers that would be missed by using independent data analysis [11].…”

Schizophrenia Prediction Using Integrated Imaging Genomic Networks

“…After a series of quality controls, we selected 184 subjects, including 80 SZ cases (age: 34 ± 11, 20 females and 60 males) and 104 healthy controls (age:32 ± 11, 38 females and 66 males). After pre-processing, 27,508 DNA methylation sites, 41,s236 fMRI voxels and 722,177 SNP loci were obtained for the subsequent biomarker selections [22,23,24,25,26,27].…”

Randomized Structural Sparsity-Based Support Identification with Applications to Locating Activated or Discriminative Brain Areas: A Multicenter Reproducibility Study

“…Another important kind of feature selection methods for high dimensional data analysis is stability selection [44], [45], [46], which is based on resamplings (bootstrapping would behave similarly) and aims to alleviate the disadvantage of the plain ℓ 1 model, which either selected by chance non-informative regions, or neglected relevant regions that provide duplicate or redundant classification information [47], [48], [8], [49], [50], [51], [52]. One main advantages of stability selection is the finite sample control of false positives.…”

“…Recently, stability selection [44] based on the plain ℓ 1 model has been widely applied [50], [51], [52], due to its finite sample control of the false positives. In addition, it makes the choice of the regularization parameter insensitive.…”