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Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.

ABSTRACT: Close to three percent of the world's population suffer from diabetes. Despite the range of treatment options available for diabetes patients, not all patients benefit from them. Investigating how different pathways correlate with phenotype of interest may help unravel novel drug targets and discover a possible cure. Many pathway-based methods have been developed to incorporate biological knowledge into the study of microarray data. Most of these methods can only analyze individual pathways but cannot deal with two or more pathways in a model based framework. This represents a serious limitation because, like genes, individual pathways do not work in isolation, and joint modeling may enable researchers to uncover patterns not seen in individual pathway-based analysis. In this paper, we propose a random effects model to analyze two or more pathways. We also derive score test statistics for significance of pathway effects. We apply our method to a microarray study of Type II diabetes. Our method may eludicate how pathways crosstalk with each other and facilitate the investigation of pathway crosstalks. Further hypothesis on the biological mechanisms underlying the disease and traits of interest may be generated and tested based on this method.

SUBMITTER: Pang H

PROVIDER: S-EPMC4666561 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.

Pang Herbert H Kim Inyoung I Zhao Hongyu H

Statistics in biosciences 20140130 2

Close to three percent of the world's population suffer from diabetes. Despite the range of treatment options available for diabetes patients, not all patients benefit from them. Investigating how different pathways correlate with phenotype of interest may help unravel novel drug targets and discover a possible cure. Many pathway-based methods have been developed to incorporate biological knowledge into the study of microarray data. Most of these methods can only analyze individual pathways but ...[more]

PMID: 26640601

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Project description:BackgroundThe wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind of dataset, biologists need to identify a small number of informative genes that can be used as biomarkers for a disease.ResultsThis paper introduces a Balanced Iterative Random Forest (BIRF) algorithm to select the most relevant genes for a disease from imbalanced high-throughput gene expression microarray data. Balanced iterative random forest is applied on four cancer microarray datasets: a childhood leukaemia dataset, which represents the main target of this paper, collected from The Children's Hospital at Westmead, NCI 60, a Colon dataset and a Lung cancer dataset. The results obtained by BIRF are compared to those of Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Multi-class SVM-RFE (MSVM-RFE), Random Forest (RF) and Naive Bayes (NB) classifiers. The results of the BIRF approach outperform these state-of-the-art methods, especially in the case of imbalanced datasets. Experiments on the childhood leukaemia dataset show that a 7% ∼ 12% better accuracy is achieved by BIRF over MSVM-RFE with the ability to predict patients in the minor class. The informative biomarkers selected by the BIRF algorithm were validated by repeating training experiments three times to see whether they are globally informative, or just selected by chance. The results show that 64% of the top genes consistently appear in the three lists, and the top 20 genes remain near the top in the other three lists.ConclusionThe designed BIRF algorithm is an appropriate choice to select genes from imbalanced high-throughput gene expression microarray data. BIRF outperforms the state-of-the-art methods, especially the ability to handle the class-imbalanced data. Moreover, the analysis of the selected genes also provides a way to distinguish between the predictive genes and those that only appear to be predictive.

Project description:Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA), which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE), Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS), Gradient based Leave-one-out Gene Selection (GLGS). To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II) breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC) is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and AUC errors.

Project description:Aortic endothelia from mice exposed to dietary model of type II diabetes at 4, 6 and 8 weeks of exposure to Bio-Serv #S3282 diet. Objective: Type II diabetes is associated with endothelial dysfunction, which can contribute to accelerated atherosclerosis and subsequent cardiovascular events. We seek to identify transcripts dysregulated in vivo in response to chronic exposure to high dietary fat that leads to diabetes. Methods: Beginning at 8 weeks of age, male Tie2-GFP mice (transgenically expressing green fluorescent protein exclusively within the endothelia) were fed a 60% fat calorie diet (Bio-Serv #S3282); age-matched mice were fed normal chow. After 4, 6, and 8 weeks on the diet, aortae and skeletal muscles (gastrocnemius, biceps femoris, and plantaris) were excised, minced, and collagenolytically digested. Each tissue digest was then subjected to FACS in order to obtain 10,000 endothelial cells. Transcriptomic analyses were performed with microarrays containing the Operon Murine V4 oligo set, and highly dysregulated genes were confirmed by real-time PCR. Results: By 4 weeks, Tie2-GFP mice receiving a high fat diet exhibited a fasting glucose of 215+17 mg/dL vs. 134+23 mg/dL in controls; by 6 weeks, a high fat diet resulted in lower glucose tolerance vs. control diet. Following 4, 6, and 8 weeks of high-fat regimen, aortic endothelial transcripts up-regulated by greater than 2-fold in biologically replicate experiments included macrophage inflammatory protein 2 (Mip2), chemokine (C-C motif) ligand 9 (CCL9), galectin-3 (Gal-3), and 5-lipoxygenase-activating protein (FLAP). Endothelial transcripts up-regulated in skeletal muscle included Mip2, CCL8 and 9, FLAP, gal-1 and 3, and ferritin light chain 1 (FTL1); transcripts down-regulated in muscle included endothelin-1 (ET-1) and insulin-like growth factor II (IGF II). Discussion: Gal-3 and FTL-1 are known to increase in response to advanced glycation end-products and oxidized LDL, respectively. However, the down-regulation of ET-1 and IGFII was surprising, as the transcription of these genes has previously been thought to exacerbate atherosclerosis. In conclusion, a comprehensive analysis of the endothelial transcript-level response to a dietary model of Type II diabetes has revealed novel regulation of transcripts with roles in inflammation, insulin sensitivity, oxidative stress, and atherosclerosis. Understanding the mechanism of diabetes-associated endothelial dysfunction may lead to improved therapies that lower the risk of cardiovascular complications in diabetic patients. Each experiment is performed with a technical replicate i.e. dye reversal. there is a biological replicate of the 4 week time point

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Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.

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Random Effects Model for Multiple Pathway Analysis with Applications to Type II Diabetes Microarray Data.

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