Hongbao Cao

Segmentation of M-FISH Images for Improved Classification of Chromosomes With an Adaptive Fuzzy C-means Clustering Algorithm

An adaptive fuzzy c-means algorithm was developed and applied to the segmentation and classification of multicolor fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means algorithm (FCM) by the use of a gain field, which models and corrects intensity inhomogeneities caused by a microscope imaging system, flairs of targets (chromosomes), and uneven hybridization of DNA. Other than directly simulating the inhomogeneousely distributed intensities over the image, the gain field regulates centers of each intensity cluster. The algorithm has been tested on an M-FISH database that we have established, which demonstrates improved performance in both segmentation and classification. When compared with other FCM clustering-based algorithms and a recently reported region-based segmentation and classification algorithm, our method gave the lowest segmentation and classification error, which will contribute to improved diagnosis of genetic diseases and cancers.

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

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.

Classification of Multicolor Fluorescence In Situ Hybridization (M-FISH) Images With Sparse Representation

There has been a considerable interest in sparse representation and compressive sensing in applied mathematics and signal processing in recent years but with limited success to medical image processing. In this paper we developed a sparse representation-based classification (SRC) algorithm based on L1-norm minimization for classifying chromosomes from multicolor fluorescence in situ hybridization (M-FISH) images. The algorithm has been tested on a comprehensive M-FISH database that we established, demonstrating improved performance in classification. When compared with other pixel-wise M-FISH image classifiers such as fuzzy c-means (FCM) clustering algorithms and adaptive fuzzy c-means (AFCM) clustering algorithms that we proposed earlier the current method gave the lowest classification error. In order to evaluate the performance of different SRC for M-FISH imaging analysis, three different sparse representation methods, namely, Homotopy method, Orthogonal Matching Pursuit (OMP), and Least Angle Regression (LARS), were tested and compared. Results from our statistical analysis have shown that Homotopy based method is significantly better than the other two methods. Our work indicates that sparse representations based classifiers with proper models can outperform many existing classifiers for M-FISH classification including those that we proposed before, which can significantly improve the multicolor imaging system for chromosome analysis in cancer and genetic disease diagnosis.

Association of telomere length and mitochondrial DNA copy number with risperidone treatment response in first-episode antipsychotic-naive schizophrenia.

Accumulating evidence indicates a putative association of telomere length and mitochondrial function with antipsychotics response in schizophrenia (SCZ). However, pharmacological findings were limited and no previous work has assessed this in a prospective longitudinal study. This study assessed telomere length and mitochondrial DNA copy number in first-episode antipsychotic-naïve SCZ patients with 8-week risperidone treatment to evaluate the association between these biomarkers and clinical treatment response. We recruited 137 first-episode antipsychotic-naive SCZ patients (and 144 controls) at baseline and 89 patients completed the 8-week follow-up. Patients, completed follow-up, were divided into Responders (N = 46) and Non-Responders (N = 43) according to the percentage of symptoms improvement. Linear regression analyses show that SCZ patients had significantly lower mtDNA copy number (β = −0.108, p = 0.002), and no alteration of telomere length when compared with healthy controls. In addition, compared with Non-Responders, Responders had significantly lower mtDNA copy number (β = −0.178, p = 0.001), and longer telomere length (β = 0.111, p = 0.071) before the 8-week treatment. After treatment, Responders persisted lower mtDNA copy number comparing with No-Responders (partial η2 = 0.125, p = 0.001). These findings suggest that telomere length and mtDNA copy number may hold the potential to serve as predictors of antipsychotic response of SCZ patients.

Sparse models for correlative and integrative analysis of imaging and genetic data

The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis.

Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method

BackgroundIn recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis.MethodsIn this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method.ResultsResults showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data.ConclusionsThe proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease.

A COMPRESSED SENSING BASED APPROACH FOR SUBTYPING OF LEUKEMIA FROM GENE EXPRESSION DATA

With the development of genomic techniques, the demand for new methods that can handle high-throughput genome-wide data effectively is becoming stronger than ever before. Compressed sensing (CS) is an emerging approach in statistics and signal processing. With the CS theory, a signal can be uniquely reconstructed or approximated from its sparse representations, which can therefore better distinguish different types of signals. However, the application of CS approach to genome-wide data analysis has been rarely investigated. We propose a novel CS-based approach for genomic data classification and test its performance in the subtyping of leukemia through gene expression analysis. The detection of subtypes of cancers such as leukemia according to different genetic markups is significant, which holds promise for the individualization of therapies and improvement of treatments. In our work, four statistical features were employed to select significant genes for the classification. With our selected genes out of 7,129 ones, the proposed CS method achieved a classification accuracy of 97.4% when evaluated with the cross validation and 94.3% when evaluated with another independent data set. The robustness of the method to noise was also tested, giving good performance. Therefore, this work demonstrates that the CS method can effectively detect subtypes of leukemia, implying improved accuracy of diagnosis of leukemia.

Identification of Genes for Complex Diseases Using Integrated Analysis of Multiple Types of Genomic Data

Various types of genomic data (e.g., SNPs and mRNA transcripts) have been employed to identify risk genes for complex diseases. However, the analysis of these data has largely been performed in isolation. Combining these multiple data for integrative analysis can take advantage of complementary information and thus can have higher power to identify genes (and/or their functions) that would otherwise be impossible with individual data analysis. Due to the different nature, structure, and format of diverse sets of genomic data, multiple genomic data integration is challenging. Here we address the problem by developing a sparse representation based clustering (SRC) method for integrative data analysis. As an example, we applied the SRC method to the integrative analysis of 376821 SNPs in 200 subjects (100 cases and 100 controls) and expression data for 22283 genes in 80 subjects (40 cases and 40 controls) to identify significant genes for osteoporosis (OP). Comparing our results with previous studies, we identified some genes known related to OP risk (e.g., ‘THSD4’, ‘CRHR1’, ‘HSD11B1’, ‘THSD7A’, ‘BMPR1B’ ‘ADCY10’, ‘PRL’, ‘CA8’,’ESRRA’, ‘CALM1’, ‘CALM1’, ‘SPARC’, and ‘LRP1’). Moreover, we uncovered novel osteoporosis susceptible genes (‘DICER1’, ‘PTMA’, etc.) that were not found previously but play functionally important roles in osteoporosis etiology from existing studies. In addition, the SRC method identified genes can lead to higher accuracy for the diagnosis/classification of osteoporosis subjects when compared with the traditional T-test and Fisher-exact test, which further validates the proposed SRC approach for integrative analysis.

Three dysconnectivity patterns in treatment-resistant schizophrenia patients and their unaffected siblings

Among individuals diagnosed with schizophrenia, approximately 20%–33% are recognized as treatment-resistant schizophrenia (TRS) patients. These TRS patients suffer more severely from the disease but struggle to benefit from existing antipsychotic treatments. A few recent studies suggested that schizophrenia may be caused by impaired synaptic plasticity that manifests as functional dysconnectivity in the brain, however, few of those studies focused on the functional connectivity changes in the brains of TRS groups. In this study, we compared the whole brain connectivity variations in TRS patients, their unaffected siblings, and healthy controls. Connectivity network features between and within the 116 automated anatomical labeling (AAL) brain regions were calculated and compared using maps created with three contrasts: patient vs. control, patient vs. sibling, and sibling vs. control. To evaluate the predictive power of the selected features, we performed a multivariate classification approach. We also evaluated the influence of six important clinical measures (e.g. age, education level) on the connectivity features. This study identified abnormal significant connectivity changes of three patterns in TRS patients and their unaffected siblings: 1) 69 patient-specific connectivity (PCN); 2) 102 shared connectivity (SCN); and 3) 457 unshared connectivity (UCN). While the first two patterns were widely reported by previous non-TRS specific studies, we were among the first to report widespread significant connectivity differences between TRS patient groups and their healthy sibling groups. Observations of this study may provide new insights for the understanding of the neurophysiological mechanisms of TRS.

Bio marker identification for diagnosis of schizophrenia with integrated analysis of fMRI and SNPs

It is important to identify significant biomarkers such as SNPs for medical diagnosis and treatment. However, the size of a biological sample is usually far less than the number of measurements, which makes the problem more challenging. To overcome this difficulty, we propose a sparse representation based variable selection (SRVS) approach. A simulated data set was first tested to demonstrate the advantages and properties of the proposed method. Then, we applied the algorithm to a joint analysis of 759075 SNPs and 153594 functional magnetic resonance imaging (fMRJ) voxels in 208 subjects (92 cases/116 controls) to identify significant biomarkers for schizophrenia (SZ). When compared with previous studies, our proposed method located 20 genes out of the top 45 SZ genes that are publicly reported We also detected some interesting functional brain regions from the fMRI study. In addition, a leave one out (LOO) cross-validation was performed and the results were compared with that of a previously reported method, which showed that our method gave significantly higher classification accuracy. In addition, the identification accuracy with integrative analysis is much better than that of using single type of data, suggesting that integrative analysis may lead to better diagnostic accuracy by combining complementary SNP and fMRI data.