simulation_Ic_sample {BioTIP} | R Documentation

## Calculating (and plot) random Ic scores (Mojtahedi et al. 2016) based on shuffling sample labelling.

### Description

Run `B` times of sample-label shuffling to calculate the Ic score, where x should be the same as the length of identified BioTiP and B is self-defined.

### Usage

    simulation_Ic_sample(
      counts,
      sampleNo,
      Ic = NULL,
      genes,
      B = 1000,
      ylim = NULL,
      main = "simulation of samples",
      fun = c("cor", "BioTIP"),
      shrink = TRUE,
      use = c("everything", "all.obs", "complete.obs", "na.or.complete",
        "pairwise.complete.obs"),
      output = c("Ic", "PCCg", "PCCs"),
      plot = FALSE
    )

### Arguments

`counts` | A numeric matrix or data frame. The rows and columns represent unique transcript IDs (geneID) and sample names, respectively.
`sampleNo` | An integer of sample size at the tipping-point state.
`Ic` | A numeric value. Ic score of identified CTS (gene-set), useful when `plot` is TRUE. Default is NULL.
`genes` | A character vector of identified CTS gene unique ids.
`B` | An integer indicating number of times to run this simulation, default 1000.
`ylim` | An integer vector of length 2. Default is NULL.
`main` | A character vector. The title of the plot. Default is NULL.
`fun` | An optional character string indicating the R functon to calculate correlations for all possible pairs of columns of a matrix. When using "BioTIP", The method is modified to ignore missing values, analogous to how `cor(X, use = "pairwise.complete.obs")` works. Note that the "BioTIP" option only function together with shrink = TRUE.
`shrink` | A flag specifying whether to shrink the correlation or not. This appraoch uses the method outlined by Schafer and Strimmer in "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics" (2005) Comparing to fun='cor', the 'BioTIP' method without shinkage is modified to ignore missing values, analogous to how `cor(X, use = "pairwise.complete.obs")` works.
`use` | An optional character string, when fun=="cor", it gives a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs".
`output` | A string. Please select from 'Ic', 'PCCg', or 'PCCs'. Uses 'Ic' by default. 'PCCg' is the PCC between genes (numerator) and 'PCCs' is PCC between samples (denominator)
`plot` | A Bollen value indicating whether a density plot will be plotted.

### Value

A vector of `B` values of BioTIP (or Ic) scores calculated for the state of interest.

### Author(s)

Zhezhen Wang zhezhen@uchicago.edu; Xinan H Yang xyang2@uchicago.edu

### Examples

    counts = matrix(sample(1:100, 27), 3, 9)
    colnames(counts) = 1:9
    row.names(counts) = c('loci1', 'loci2', 'loci3')
    CTS = c('loci1', 'loci2')
    randomS <- simulation_Ic_sample(counts,  sampleNo=3,  Ic=3.4,  genes=CTS,  B=3,
                           fun='BioTIP', plot=TRUE)
    dim(randomS)

[Package BioTIP version 1.4.0 Index]