simulat | R Documentation |
This function uses negative binomial (NB) pseudorandom generator to create count datasets of RNA isoform reads based on real data.
simulat(yy, nci, r1, r2, p, q, A)
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. |
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.
Return count data.
Yuan-De Tan tanyuande@gmail.com
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.
NegBinomial
data(jkttcell) jknull<-simulat(yy=jkttcell[1:60,],nci=7,r1=3, r2=3,p=0,q=0.2,A=0)
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