In kkolmus/MolecularAnalyzeR: This package aims at checking correlation between gene expression and copy number variation for cohorts of patients analyzed within The Cancer Genome Atlas project

knitr::opts_chunk$set(
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Overview of MolecularAnalyzeR

The goal of MolecularAnalyzeR is to checking correlation between gene expression and copy number variation for cohorts of patients analyzed within The Cancer Genome Atlas project.

According to Travis, tests are run for R 4.0.2, which exploits Bioconductor 3.12. ``` {r, eval = FALSE} $ Rscript -e 'sessionInfo()' R version 4.0.2 (2020-06-22) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 16.04.6 LTS

In my package, I am dependent on Bioconductor 3.14 with R 4.1.0.
Thus, I cannot check my package with Travis.
See other related issues:
https://travis-ci.community/t/bioconductor-version-error/3332

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## Installation

In order to install the MolecularAnalyzeR package, 
you should pre-install devtools.

``` {r, eval = FALSE}
install.package("devtools")
devtools::install_github("MolecularAnalyzeR")

Analysis workflow

1. Attach the package. Have a look at the list of TCGA projects which could be potentially analyzed.

library(MolecularAnalyzeR)
listTCGAcohorts()

1. Download copy number variation data.

CNVdata <- downloadCNVdata(projectID = c("TCGA-READ", "TCGA-COAD"))

1. Download expression data.

expData <- downloadExpData(projectID = c("TCGA-READ", "TCGA-COAD"))

1. Normalize expression data.

normExpData <- normalizeExpData(expData = expData, method = "vst")

1. Prepare CNV and expression data for analysis and visualization. This function (a) extracts copy number variation data for gene 1 from CNV data matrix and transforms them into the following categorical format: losses, gains, and wild type (b) and extracts normalized expression data for gene 2 from expression data matrix. The newly formed data frame is an input for statistical analysis and visualization. The package offers a toy dataset to play with.

``` {r data, eval = FALSE} data_input <- prepareData( gene1 = "TP53", gene2 = "TSPAN6", CNVdata = CNVdata, normExpData = normExpData)

toy data set

data(inputSLanalyzeR)

6. Run statistical analysis. This function looks for statistical difference in expression of gene 2 for copy number states of gene 1 (WT vs. loss state and WT vs. gain state).

``` {r stat, eval = FALSE}
stat <- runGenotypeComparison(
  data_input,
  gene1 = "CDK11A",
  gene2 = "CFLAR",
  threshold = 10)

1. Generate boxplots showing differences in expression of gene 2 split by the status of gene 1.

{r vis, eval = FALSE} visualizeGenotypeComparison(data, gene1 = "CDK11A", gene2 = "CFLAR", method = "vst", threshold = 10)

kkolmus/MolecularAnalyzeR documentation built on Dec. 21, 2021, 6:46 a.m.