Pairwise Nonlinear Dependence Analysis of Genomic Data

02/20/2022
by   Siqi Xiang, et al.
0

In The Cancer Genome Atlas (TCGA) dataset, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of genes from TCGA using a powerful tool called Binary Expansion Testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel.

READ FULL TEXT
research
02/10/2022

A Clustering Approach to Integrative Analysis of Multiomic Cancer Data

Rapid technological advances have allowed for molecular profiling across...
research
04/01/2022

A Class of Semiparametric Models with Homogeneous Structure for Panel Data Analysis

Stimulated by the analysis of a dataset from China about Covid-19, we pr...
research
11/22/2020

Topological Data Analysis of copy number alterations in cancer

Identifying subgroups and properties of cancer biopsy samples is a cruci...
research
10/17/2017

CancerLinker: Explorations of Cancer Study Network

Interactive visualization tools are highly desirable to biologist and ca...
research
11/24/2013

Sparse CCA via Precision Adjusted Iterative Thresholding

Sparse Canonical Correlation Analysis (CCA) has received considerable at...
research
10/17/2016

BET on Independence

We study the problem of nonparametric dependence detection. Many existin...
research
12/30/2022

Topical Hidden Genome: Discovering Latent Cancer Mutational Topics using a Bayesian Multilevel Context-learning Approach

Statistical inference on the cancer-site specificities of collective ult...

Please sign up or login with your details

Forgot password? Click here to reset