simulat: Simulation

In NBBttest: Negative Binomial Beta t-Test

Simulation

Description

This function uses negative binomial (NB) pseudorandom generator to create count datasets of RNA isoform reads based on real data.

Usage

simulat(yy, nci, r1, r2, p, q, A)

Arguments

yy	real count data
nci	nonnegative int value: column number of information related to genes or isoforms.
r1	numeric argument: number of replicate libraries in condition 1.
r2	numeric argument: number of replicate libraries in condition 2.
p	numeric argument: proportion of genes or isoforms differentially expressed. The value is in range of 0~1. Default is 0.
q	numeric argument: proportion of genes or isoforms artificially noised. The value is in range of 0~1. Default is 0.
A	numeric argument: conditional effect value. The value is larger than or equal to 0. Default is 0.

Details

Null count data are created by using R negative binomial pseudorandom generator rnbinom with μ and size. Parameters μ and size are given by mean and variance drawn from real read counts of a gene set or an isoform set in a condition. Condition (or treatment) effect on differential transcription of isoforms is linearly and randomly assigned to genes or isoforms. The conditional effect = AU where U is uniform variable and A is input constant. P percent of genes or isoforms is set to be differentially expressed or differentially spliced. Q percent of genes or isoforms has technical noise. If P = 0, then simulation is null simulation, the data are null data or baseline data.

Value

Return count data.

Author(s)

Yuan-De Tan [e-mail]

References

Yuan-De Tan Anita M. Chandler, Arindam Chaudhury, and Joel R. Neilson(2015) A Powerful Statistical Approach for Large-scale Differential Transcription Analysis. Plos One, 10.1371/journal.pone.0123658.

See Also

NegBinomial

Examples

data(jkttcell)
jknull<-simulat(yy=jkttcell[1:60,],nci=7,r1=3,
r2=3,p=0,q=0.2,A=0)

NBBttest documentation built on May 30, 2022, 1:05 a.m.