pRP: Parallel Rank Product

In sprint: Simple Parallel R INTerface

Description

Parallel rank product function helps identifying differentially regulated genes in replicated microarray experiments.

Usage

pRP(data, cl, num.perm, logged = TRUE, na.rm = FALSE, gene.names = NULL, 
    plot = FALSE, rand = NULL, sum = FALSE)

Arguments

data	array, input data
cl	vector, class labels of the samples
num.perm	integer, the number of permutations used in the calculation of the null density. The default value is 100.
logged	boolean, whether the data is logged or not. The default value is TRUE.
na.rm	boolean, whether missing values are to be replaced by the gene-wise mean of the non-missing values and used in computing rank. The default value is FALSE.
gene.names	the gene name to be assigned to the estimated percentage of false positive predictions. The default value is NULL.
plot	boolean, whether to plot the estimated percentage of false positive predictions against the rank of each gene. The default value is FALSE.
rand	number, the seed for the random number generator if specified. The default value is NULL.
sum	boolean, whether to perform a rank sum analysis. The default value is NULL.

Details

The SPRINT task parallel implementation of the rank product method is approximately twice as fast in serial as the existing RP() function from the RankProd package and it shows excellent scaling.

N.B. Please see the SPRINT User Guide for how to run the code in parallel using the mpiexec command.

Author(s)

University of Edinburgh SPRINT Team [e-mail] www.r-sprint.org

See Also

RP SPRINT

sprint documentation built on May 30, 2017, 8:25 a.m.