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.
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.
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.
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.
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.
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