diceR (version 3.0.0)

pcn: 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)

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

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.

Author

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")