README.md

In mbeccuti/PGS: PGS-Peculiar Genes Selection: a feature selection procedure for binary classification tasks.

Synopsis

PGS is a 3 steps feature selection procedure for binary classification tasks in case of High-Throughput data. It generates .txt files containing signatures extracted by applying Peculiar Genes Selection procedure from gene expression matrix.

# ## Code Example
# X <- DEG_detection(gene_expression_file, contrast_file)
# y <- c(rep(1, 6), rep(0,6))
# pp <- Predictive_Power(X, y, intercept = T, pp_thr = .5, q_thr = .95)
# B_M <- BinaryMatrix(pp, 25, 40, y)

Motivation

PGS is a quick and useful tool to extract signature for classification tasks from high-throughput data sets. It is able to handle both imbalanced and balanced data and it usage is limited to binary classification problems.

Installation

To install it you can use use devtools:

install.packages("devtools")
library(devtools)
install_github("beccuti/PGS", ref="master")

mbeccuti/PGS documentation built on May 23, 2019, 9:34 a.m.