Home

A solution to identify a gene expression signature in microarray datasets

What is MicroGES?

MicroGES is an R package that provides a suite of tools for identifying gene expression signatures in microarray datasets. It includes functions for preprocessing microarray data, performing differential expression analysis, and training machine learning models for classification. The package includes the following files:

• Preprocessing:
  • preprocessing_affymetrix.R: Preprocess Affymetrix microarray data.
  • preprocessing_illumina.R: Preprocess Illumina microarray data.
  • batchRemoval_ComBat.R: Remove batch effects using the ComBat algorithm.
  • batchRemoval_fsva.R: Remove batch effects using the fsva algorithm.
• Differential expression:
  • featureSelection_limma.R: Perform feature selection using limma.
  • plot_mds.R: Plot multidimensional scaling (MDS) plots.
• Machine learning:
  • linearSvm.R: Train and evaluate a linear support vector machine (SVM).
  • neuralNetwork.R: Train and evaluate a neural network.
  • randomForest.R: Train and evaluate a random forest.
  • select_train_test.R: Split a dataset into training and test sets.

Installation

You can install the latest version of MicroGES from GitHub using the devtools package:

if (!require("devtools")) install.packages("devtools")
devtools::install_github("bioinformatics-ua/MicroGES")