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README.Rmd
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README.Rmd
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---
title: ""
output:
md_document:
variant: markdown_github
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
## Principal aim
In this repository we provide the codes necessary to replicate the findings presented in the research article **Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks** authored by Cesare Miglioli, Gaetan Bakalli, Samuel Orso, Mucyo Karemera, Roberto Molinari, Stephane Guerrier and Nabil Mili.
The statistical analysis performed in this study is based on the data presented in the paper **Subtype-specific micro-RNA expression signatures in breast cancer progression** by *Haakensen et al* in 2016. We thank the authors for having made available the [AHUS data set](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3759/?query=AHUS) on the free access ArrayExpress platform.
## Get the data from ArrayExpress in R
First install the **ArrayExpress** package, through bioconductor, with the following code:
```{r,echo=TRUE,eval=FALSE}
### R version 3.5 and older ###
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ArrayExpress", version = "3.8")
### R version 3.6 ###
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ArrayExpress")
require(ArrayExpress) #load the new package
```
Now you can create a temporary directory to store the raw data listed as *E-MTAB-3759* on **ArrayExpress**. Then you just need to extract the data thanks to the function *ae2bioc()* of the package. To have a first impression of the AHUS dataset, you can click on the following [Visual Impression](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3759/samples/?s_page=4&s_pagesize=25&s_sortby=col_25&s_sortorder=ascending).
```{r, eval=F,echo=TRUE}
temp_dir <- "C:/..." #choose your preferred path
dir.create(temp_dir)
setwd(temp_dir)
# Download the raw files
# To run only the first time!
ae_obj <- ArrayExpress::getAE('E-MTAB-3759', type = 'full', path = temp_dir)
str(ae_obj) #to explore the new object
### Extraction ###
mtab3759raw <- ae2bioc(mageFiles = ae_obj)
```
The response $y$ (i.e. if the subject has breast cancer or not) can be obtained directly from the *mtab3759raw* object together with the type of breast cancer $y_{sub}$ (i.e. either benign, DCIS or invasive) which indicates the sub-populations. However, in order to get the final design matrix $X$ of miRNAs used in the study, we need to apply the function *normalizeBetweenArrays()* of the **limma** package to our previously found object. This function normalizes expression intensities so that the same intensities (or log-ratios) have similar distributions across a set of arrays.
```{r, eval=F,echo=TRUE}
# Response variable
y <- mtab3759raw@phenoData@data$Factor.Value.disease.
y_sub <- mtab3759raw@phenoData@data$Characteristics.clinical.history.
# Design matrix X of miRNAs
mtab3759raw <- normalizeBetweenArrays(mtab3759raw,method="quantile")
X <- [email protected][[1]]
X <- t(X) #to have subjects on the rows and miRNAs on the columns
colnames(X) <- mtab3759raw$genes$GeneName #Assign gene names to each column
```
## Reproduce the results
Having now the final design matrix $X$ and the response $y$, we are able to reproduce the results and the graphs contained in the research article. Please refer to the file `breast_cancer_data_analysis.R` in this repository for a detailed explanation.