pcn: Simulate and select null distributions on empirical gene-gene...

In diceR: Diverse Cluster Ensemble in R

Simulate and select null distributions on empirical gene-gene correlations

Description

Using a principal component constructed from the sample space, we simulate null distributions with univariate Normal distributions using pcn_simulate. Then a subset of these distributions is chosen using pcn_select.

Usage

pcn_simulate(data, n.sim = 50)

pcn_select(data.sim, cl, type = c("rep", "range"), int = 5)

Arguments

data	data matrix with rows as samples, columns as features
n.sim	The number of simulated datasets to simulate
data.sim	an object from pcn_simulate
cl	vector of cluster memberships
type	select either the representative dataset ("rep") or a range of datasets ("range")
int	every int data sets from median-ranked data.sim are taken. Defaults to 5.

Value

pcn_simulate returns a list of length n.sim. Each element is a simulated matrix using this "Principal Component Normal" (pcn) procedure.

pcn_select returns a list with elements

• ranks: When type = "range", ranks of each extracted dataset shown

• ind: index of representative simulation

• dat: simulation data representation of all in pcNormal

Author(s)

Derek Chiu

Examples

set.seed(9)
A <- matrix(rnorm(300), nrow = 20)
pc.dat <- pcn_simulate(A, n.sim = 50)
cl <- sample(1:4, 20, replace = TRUE)
pc.select <- pcn_select(pc.dat, cl, "rep")

diceR documentation built on May 29, 2024, 8:54 a.m.