arXiv subjects

Yishi Xing

Publications and source records attributed to Yishi Xing.

3 recordsLinked to original sources

arXiv2017

Network induced large correlation matrix estimation

The correlation matrix of massive biomedical data (e.g. gene expression or neuroimaging data) often exhibits a complex and organized, yet latent graph topological structure. We propose a two step procedure that first detects the latent graph topology with parsimony from the sample correlation matrix and then regularizes the correlation matrix by leveraging the detected graph topological information. We show that the graph topological information guided thresholding can reduce false positive and false negative rates simultaneously because it allows edges to borrow strengths from each other precisely. Several examples illustrate that the parsimoniously detected latent graph topological structures may reveal underlying biological networks and guide correlation matrix estimation.

stat.AP↗

arXiv2017

A Network Object Method to Uncover Hidden Disorder-Related Brain Connectome

Neuropsychiatric disorders impact functional connectivity of the brain at the network level. The identification and statistical testing of disorder-related networks remains challenging. We propose novel methods to streamline the detection and testing of the hidden, disorder-related connectivity patterns as network-objects. We define an abnormal connectome subnetwork as a network-object that includes three classes: nodes of brain areas, edges representing brain connectomic features, and an organized graph topology formed by these nodes and edges. Comparing to the conventional statistical methods, the proposed approach simultaneously reduces false positive and negative discovery rates by letting edges borrow strengths precisely with the guidance of graph topological information, which effectively improves the reproducibility of findings across brain connectome studies. The network-object analyses may provide insights into how brain connectome is systematically impaired by brain illnesses.

stat.AP↗

arXiv2016

Differentially Expressed Functional Connectivity Networks with K-partite Graph Topology

Emerging brain network studies suggest that interactions between various distributed neuronal populations may be characterized by an organized complex topological structure. Many brain diseases are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to identify network-level differentially expressed connections that have low false positive rates, sufficient statistical power, and high reproducibility. In this paper, we propose a novel statistical approach to fulfill this goal by leveraging the topological structure of differentially expressed functional connections or edges in a graphical representation. We propose a new algorithm to automatically detect the latent topology of a k-partite graph structure, and we also provide statistical inferential techniques to test the detected topology. We evaluate our new methods via extensive numerical studies. We also apply our new approach to resting state fMRI data (24 cases and 18 controls) for Parkinson's disease research. The detected connectivity network biomaker with the k-partite graph topological structure reveals underlying neural features distinguishing Parkinson's disease patients from healthy control subjects.

q-bio.NC↗