README.md

In huynguyen250896/geneCor: geneCor: Identification of correlation between two pairs of three datasets, visualization of Z-score distributions of those two pairs on a page, and examination of the significance of the skewness of those distributions.

geneCor v0.1.1

I. Introduction

The package geneCor is built to serve as a support tool for the paper "Multi-omics analysis detects novel prognostic subgroups of breast cancer". It automatically computes correlation coefficients of individual genes that share between the first data dat1 and its corresponding third data cordat1, and those that share between the second data dat2 and its corresponding third data cordat2; 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.

II. Understanding the tool

The following are parameters provided by geneCor: - dat1: data.frame or matrix. The first input data includes its rows are samples and its columns are genes.

• cordat1: data.frame or matrix. The data includes its rows are samples and its columns are clinical features. This itself is the corresponding third data of dat1. Namely, correlation analysis will be implemented between dat1 and cordat1.

• alternative1: a character string specifying the alternative hypothesis for Z-score distribution between dat1 and cordat1. Must be one of "two.sided", "greater" or "less". You can specify just the initial letter.

• dat2: data.frame or matrix. The second input data includes its rows are samples and its columns are genes.

• cordat2: data.frame or matrix. The data includes its rows are samples and its columns are clinical features. This itself is the corresponding third data of dat2. Namely, correlation analysis will be implemented between dat2 and cordat2.

• alternative2: a character string specifying the alternative hypothesis for Z-score distribution between dat2 and cordat2. Must be one of "two.sided", "greater" or "less". You can specify just the initial letter.

• methodCC: character. correlation method. Allowed values are spearman, pearson (default), kendall.

• adjustedP: logical. Whether we should adjust the P-values gained from correlation analyses using the Benjamini-Hochberg procedure. Default is adjustedP = T.

Please download datasets Dataset as examples to well grasp geneCor's requirement on data structure.

III. Pipeline and gained results

Figure 1: Pipeline of the package geneCor.

Figure 2: Statistical significance of the skewness is printed in the R environment. dat1_cor is the result of association between dat1 and cordat1, and their relationship is positively skewed. In contrast, dat2_cor is the result of association between dat2 and cordat2, and their relationship is negatively skewed.

Figure 3: the Z-score distributions of between copy number alterations (CNA, dat1) versus its corresponding gene expression (its corresponding third data cordat1), and methylation (MET, dat2) versus its corresponding gene expression (its corresponding third data cordat2) on a page.

IV. Implementation

Use the following command to install directly from GitHub;

devtools::install_github("huynguyen250896/geneCor")

Call the library;

library(geneCor)

running example:

geneCor(dat1 = cna, cordat1 = exp1, alternative1="less", dat2 = met, cordat2 = exp2, alternative2="greater") #compute Pearson's correlation coefficients.
#' #dat1 receives copy number alterations data, and cordat1 receives its corresponding gene expression data.
#' #dat2 receives methylation data, and cordat2 receives its corresponding gene expression data.

geneCor(dat1 = cna, cordat1 = exp1, alternative1="less", dat2 = met, cordat2 = exp2, alternative2="greater", method = "spearman")  #compute Spearman's Rank correlation coefficients.

geneCor(dat1 = cna, cordat1 = exp1, alternative1="less", dat2 = met, cordat2 = exp2, alternative2="greater", method = "kendall") #compute Kendall's correlation coefficients.

V. What's new

• 2021-01-20 : The function now can adjust gained P-values from the process of correlation analysis using the Benjamini-Hochberg procedure.
• 2020-09-30 : The function now can compute one of the three common correlation methods: Pearson, Spearman's rank, or Kendall's tau-b.

VI. Citation

Please kindly cite the following paper (and Star this Github repository if you find this tool of interest) if you use the tool in this repo:

Author: Nguyen, Quang-Huy
Nguyen, Hung
Nguyen, Tin
Le, Duc-Hau
Year: 2020
Title: Multi-omics analysis detects novel prognostic subgroups of breast cancer
Journal: Frontiers in Genetics
Type of Article: ORIGINAL RESEARCH
DOI: 10.3389/fgene.2020.574661

Feel free to contact Quang-Huy Nguyen for any questions about the code and results.

huynguyen250896/geneCor documentation built on Aug. 8, 2021, 5:26 p.m.