JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

Abstract

Motivation: A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. Results: We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.

Connectivity patterns between multiple allergen specific IgE antibodies and their association with severe asthma

“…We evaluated the ability of c-sIgE pairwise interactions to classify asthma severity by applying the joint density-based nonparametric differential interaction network analysis and classification (JDINAC; see this article's Methods section in the Online Repository). 30 This model identifies differential interaction patterns of network activation of c-sIgEs that are most closely related to asthma severity, and builds a classification model using the network biomarkers. To ascertain whether c-sIgE pairwise interactions are more informative than individual components to classify asthma severity, we compared the performances of JDINAC with penalized logistic regression.…”

Data‐adaptive test for high‐dimensional multivariate analysis of variance problem

“…d s 0 p = 1 p = 2 p = 3 p = 4 p = 5 p = ∞ were obtained through the standard RNASeq data processing pipeline of the software TCGA-Assembler. For further details of the data pre-processing pipeline, we refer to Ji et al (2017). The TCGA BRCA study contained the information of 1098 breast cancer patients, including their matched mRNA, copy number, methylation, and microRNA data.…”

Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision

“…Many researches show that differential network analysis is potential of predicting and identifying essential modules in life processes transition, e.g. differential subnetwork identification [20], prioritizing driver genes [21], cell types classification [22], etc.…”