huynguyen250896 Goto Github PK
Name: Quang-Huy Nguyen
Type: User
Company: Department of CS and Software Engineering, Auburn Graduate School
Location: Auburn, Alabama, USA
Name: Quang-Huy Nguyen
Type: User
Company: Department of CS and Software Engineering, Auburn Graduate School
Location: Auburn, Alabama, USA
This package is built to serve as a support tool for the paper "Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data". The package aims to compute automatically rapidly the correlation between genes and clinical features, and then compute Q-value (Benjamini-Hochberg FDR) based on the identified p-values.
DrGA is a novel R package that has been developed based on the idea of our recent driver gene analysis scheme. It wholly automates the analysis process and attached improvements to maximize user experience with the highest convenience. In particular, it facilitates users with limited IT backgrounds and rapidly creates consistent and reproducible results. We describe the usage of the DrGA on driver genes of human breast cancer using a multi-omics dataset. Besides, we also provide users with another potential application of DrGA on analyzing genomic biomarkers of a complex disease from other species.
Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
Given a gene from a list of genes of interest, it will be specifically distributed to either of the identified subgroups based on the mean values (e.g., CNA changes, MET changes, and expression levels). Then, a gene was considered as a subtype-specific one if P-value <= 0.05 (one-way ANOVA test).
It automatically computes correlation coefficients of individual genes that share between the first data and its corresponding third data, and those that share between the second data and its corresponding third data; visualizes the Z-score distributions of between the first and second data versus their corresponding third data on a page; and examines the significance of the skewness for those distributions using D'Agostino test.
a log-rank test in univariate Cox regression analysis with a proportional hazards model is performed to examine the association between the expression of each gene and the survival rates of patients. Genes with Q-value <= 0.05 (Benjamini-Hocberg procedure) are preserved.
Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares - Stephen Boyd & Lieven Vandenberghe
Introduction to Data Science: Data Analysis and Prediction Algorithms with R - Rafael A. Irizarry
When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. A recent study have reported that the decomposition methods are the most appropriate ones for solving these challenges. In this study, we represent an R tool, termed overlapping co-expressed gene module (oCEM), which possesses those methods with a wholly automatic analysis framework to help non-technical users to easily perform complicated statistical analyses and then gain robust results. We also develop a novel auxiliary statistical approach to select the optimal number of principle components using a permutation procedure. Two example datasets are used, related to human breast cancer and mouse metabolic syndrome, to enable the illustration of the straightforward use of the tool. Computational experiment results show that overlappingCGM outperforms state-of-the-art techniques.
Uveal melanoma (UM) is a comparatively rare cancer but requires significant consideration since patients with developing metastatic diagnosis survive only for about 6-12 months. Fortunately, increasingly large multi-omics databases allow us to further understand cancer initiation and development. Moreover, previous studies have observed that the association between copy number aberrations (CNA) and methylation (MET) has affected these processes. From that, we decided to explore the effect of this association on a case study of UM. Also, the current subtypes of UM display its weak association with biological phenotypes and its lack of therapy suggestions. Therefore, the re-identification of molecular subtype is essential for UM patients. This repository provides data and source code for the task.
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