sim.counts

total number of genes, the default value is <code>10000</code>.

nGenes

proportion of non-differentially expressed genes, 
the default value is <code>0.8</code>.

a vector (or scalar) of mean counts in control group 
from which to simulate.

a vector (or scalar) of dispersion parameter 
from which to simulate.

disp

a vector (or scalar, or a function that takes an integer n 
          and generates a vector of length n)
of log fold change for differentially expressed (DE) genes.

logfc

proportion of up-regulated genes among all DE genes, 
the default value is <code>0.5</code>.

sample with or without replacement from given parameters. 
See Details for more information.

replace


This function simulates count data from Negative-Binomial distribution
for two-sample RNA-seq experiments with given mean, dispersion 
and log fold change. 
A count data matrix is generated.


We propose a procedure for sample size calculation while
controlling false discovery rate for RNA-seq experimental design. Our
procedure depends on the Voom method proposed for RNA-seq data analysis
by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size
calculation method proposed for microarray experiments by Liu and Hwang
(2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
that calculates appropriate sample sizes for two-sample t-test for RNA-seq
experiments with fixed or varied set of parameters. The outputs also contain a
plot of power versus sample size, a table of power at different sample sizes,
and a table of critical test values at different sample sizes.
To install this package, please use
'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'.

Ran Bi

ssizeRNA

Sample Size Calculation for RNA-Seq Experimental Design

sim.counts function

RNA-seq Count Data Simulation from Negative-Binomial Distribution

We propose a procedure for sample size calculation while
controlling false discovery rate for RNA-seq experimental design. Our
procedure depends on the Voom method proposed for RNA-seq data analysis
by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size
calculation method proposed for microarray experiments by Liu and Hwang
(2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
that calculates appropriate sample sizes for two-sample t-test for RNA-seq
experiments with fixed or varied set of parameters. The outputs also contain a
plot of power versus sample size, a table of power at different sample sizes,
and a table of critical test values at different sample sizes.
To install this package, please use
'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'.
For R version 3.5 or greater, please use
'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.

sim.counts: RNA-seq Count Data Simulation from Negative-Binomial Distribution

Description

Usage

Arguments

Value

Details

Examples