simulateReadCounts {TCC} | R Documentation

## Generate simulation data from negative binomial (NB) distribution

### Description

This function generates simulation data with arbitrary defined experimental condition.

### Usage

    simulateReadCounts(Ngene = 10000, PDEG = 0.20, DEG.assign = NULL,
                       DEG.foldchange = NULL, replicates = NULL, group = NULL,
                       fc.matrix = NULL)

### Arguments

`Ngene` | numeric scalar specifying the number of genes.
`PDEG` | numeric scalar specifying the proportion of differentially expressed genes (DEGs).
`DEG.assign` | numeric vector specifying the proportion of DEGs up- or down-regulated in individual groups to be compared. The number of elements should be the same as that of `replicates` if `replicates` is specified. The indication of `replicates` means a single-factor experimental design. The number of elements in `DEG.assign` should be the same as the number of columns in `DEG.foldchange`. Both `DEG.foldchange` as data frame and `group` should simultaneously be specified and those indication means a multi-factor experimental design.
`DEG.foldchange` | numeric vector for single-factor experimental design and data frame for multi-factor experimental design. Both `DEG.foldchange` as numeric vector and `replicates` should simultaneously be specified for single-factor experimental design. The i-th element in `DEG.foldchange` vector indicates the degree of fold-change for Group i. The default is `DEG.foldchange = c(4, 4)`, indicating that the levels of DE are four-fold in both groups. Both `DEG.foldchange` as data frame and `group` should simultaneously be specified for multi-factor experimental design. Numeric values in the `DEG.foldchange` object indicate the degree of fold-change for individual conditions or factors.
`replicates` | numeric vector indicating the numbers of (biological) replicates for individual groups compared. Ignored if `group` is specified.
`group` | data frame specifying the multi-factor experimental design.
`fc.matrix` | fold change matrix generated by `makeFCMatrix` for simulating DEGs with the fold-change under un-uniform distributions.

### Details

The empirical distribution of read counts used in this function is calculated from a RNA-seq dataset obtained from Arabidopsis data (three biological replicates for both the treated and non-treated samples), the `arab` object, in NBPSeq package (Di et al., 2011). The overall design about the simulation conditions introduced can be viewed as a pseudo-color image by the `plotFCPseudocolor` function.

### Value

A TCC-class object containing following fields:

`count` | numeric matrix of simulated count data.
`group` | data frame indicating which group (or condition or factor) each sample belongs to.
`norm.factors` | numeric vector as a placeholder for normalization factors.
`stat` | list for storing results after the execution of the `calcNormFactors` (and `estimateDE`) function.
`estimatedDEG` | numeric vector as a placeholder for indicating which genes are up-regulated in particular group compared to the others. The values in this field will be populated after the execution of the `estimateDE` function.
`simulation` | list containing four fields: `trueDEG`, `DEG.foldchange`, `PDEG`, and `params`. The `trueDEG` field (numeric vector) stores information about DEGs: 0 for non-DEG, 1 for DEG up-regulated in Group 1, 2 for DEG up-regulated in Group 2, and so on. The information for the remaining three fields is the same as those indicated in the corresponding arguments.

### Examples

    # Generating a simulation data for comparing two groups
    # (G1 vs. G2) without replicates (single-factor experimental design).
    # the levels of DE are 3-fold in G1 and 7-fold in G2.
    tcc <- simulateReadCounts(Ngene = 10000, PDEG = 0.2,
                             DEG.assign = c(0.9, 0.1),
                             DEG.foldchange = c(3, 7),
                             replicates = c(1, 1))
    dim(tcc$count)
    head(tcc$count)
    str(tcc$simulation)
    head(tcc$simulation$trueDEG)

    # Generating a simulation data for comparing three groups
    # (G1 vs. G2 vs. G3) with biological replicates
    # (single-factor experimental design).
    # the first 3000 genes are DEGs, where the 70%, 20%, and 10% are
    # up-regulated in G1, G2, G3, respectively. The levels of DE are
    # 3-, 10-, and 6-fold in individual groups.
    tcc <- simulateReadCounts(Ngene = 10000, PDEG = 0.3,
                             DEG.assign = c(0.7, 0.2, 0.1),
                             DEG.foldchange = c(3, 10, 6),
                             replicates = c(2, 4, 3))
    dim(tcc$count)
    head(tcc$count)
    str(tcc$simulation)
    head(tcc$simulation$trueDEG)

    # Generating a simulation data consisting of 10,000 rows (i.e., Ngene = 10000)
    # and 8 columns (samples) for two-factor experimental design
    # (condition and time). The first 3,000 genes are DEGs (i.e., PDEG = 0.3).
    # Of the 3,000 DEGs, 40% are differentially expressed in condition (or GROUP) "A"
    # compared to the other condition (i.e., condition "B"), 40% are differentially
    # expressed in condition (or GROUP) "B" compared to the other condition
    # (i.e., condition "A"), and the remaining 20% are differentially expressed at
    # "10h" in association with the second factor: DEG.assign = c(0.4, 0.4, 0.2).
    # The levels of fold-change are (i) 2-fold up-regulation in condition "A" for
    # the first 40% of DEGs, (ii) 4-fold up-regulation in condition "B" for the
    # second 40%, and (iii) 0.4- and 0.6-fold up-regulation at "10h" in "A" and
    # 5-fold up-regulation at "10h" in "B".

    group <- data.frame(
       GROUP = c( "A",  "A",   "A",   "A",  "B",  "B",   "B",   "B"),
       TIME  = c("2h", "2h", "10h", "10h", "2h", "2h", "10h", "10h")
    )
    DEG.foldchange <- data.frame(
       FACTOR1 = c(2, 2,   2,   2, 1, 1, 1, 1),
       FACTOR1 = c(1, 1,   1,   1, 4, 4, 4, 4),
       FACTOR2 = c(1, 1, 0.4, 0.6, 1, 1, 5, 5)
    )
    tcc <- simulateReadCounts(Ngene = 10000, PDEG = 0.3,
                              DEG.assign = c(0.4, 0.4, 0.2),
                              DEG.foldchange = DEG.foldchange,
                              group = group)
    tcc

[Package TCC version 1.30.0 Index]