README.md

In huynguyen250896/computeC: Automatically compute the correlation between individual genes and clinical features

computeC v0.1.0

I. Introduction

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 automatically rapidly compute correlation coefficients between each of genes versus each of clinical features of interest, and then adjust identified log-rank P-value following Benjamini-Hochberg FDR.

II. Understanding the tool

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

• clinical: data frame. The data includes its rows are samples and its columns are clinical features (e.g., numbers of positive lymph nodes, the Nottingham prognostic index, and tumour stage, respectively). Note that samples are also included in rows of data and in the same order.

• col: character. name of any columns in clinical. This must be a clinical feature that you are interest.

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

Please download datasets Dataset as examples to well grasp computeC's requirement on data structure and its usage.

III. Pipeline

Figure: Pipeline of the package computeC.

IV. Implementation

Use the following command to install directly from GitHub;

devtools::install_github("huynguyen250896/computeC")

Call the library;

library(computeC)

running example:

computeC(data = exp, clinical = cli, col = "lymph") #compute Spearman's Rank correlation coefficients (default method)
computeC(data = exp, clinical = cli, col = "npi", methodCC = "pearson") #compute Pearson's correlation coefficients
computeC(data = exp, clinical = cli, col = "stage", methodCC = "kendall") #compute Kendall's correlation coefficients

V. What's new

• 2020-09-30: the function now can compute one of the three 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:

Reference Type: Journal Article
Author: Nguyen, Quang-Huy
Le, Duc-Hau
Year: 2020
Title: Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data
Journal: Scientific Reports
Volume: 10
Issue: 1
Pages: 20521
Date: 2020/11/25
ISSN: 2045-2322
DOI: 10.1038/s41598-020-77318-1

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

huynguyen250896/computeC documentation built on June 24, 2021, 5:58 a.m.