make_feature_from_expression: Create a feature vector from expression data

In PrInCE: Predicting Interactomes from Co-Elution

Description

Convert a gene or protein expression matrix into a feature vector that matches the dimensions of a data frame used as input to a classifier, such as a naive Bayes, random forests, or support vector machine classifier, by calculating the correlation between each pair of genes or proteins.

Usage

make_feature_from_expression(expr, dat, node_columns = c(1, 2), ...)

Arguments

expr	a matrix containing gene or protein expression data, with genes/proteins in columns and samples in rows
dat	the data frame of features to be used by the classifier, with protein pairs in the columns specified by the node_columns argument
node_columns	a vector of length two, denoting either the indices (integer vector) or column names (character vector) of the columns within the data frame containing the nodes participating in pairwise interactions; defaults to the first two columns of the data frame (c(1, 2))
...	arguments passed to cor

Value

a vector matching the dimensions and order of the feature data frame, to use as input for a classifier in interaction prediction

PrInCE documentation built on Nov. 8, 2020, 6:34 p.m.