run_csd {csdR} | R Documentation

## Run CSD analysis

### Description

This function implements the a CSD based on the one presented by Voigt et al. 2017. All pairs of genes are first compared within each condition by the Spearman correlation and the correlation and its variance are estimated by bootstrapping. Finally, the results for the two conditions are compared and C-, S- and D-values are computed and returned.

### Usage

    run_csd(
      x_1,
      x_2,
      n_it = 20L,
      nThreads = 1L,
      verbose = TRUE,
      iterations_gap = 1L
    )

### Arguments

`x_1` | Numeric matrix, the gene expression matrix for the first condition. Genes are in columns, samples are in rows. The columns must be named with the name of the genes.
`x_2` | Numeric matrix, the gene expression matrix for the second condition.
`n_it` | Integer, number of bootstrap iterations
`nThreads` | Integer, number of threads to use for computations
`verbose` | Logical, should progress be printed?
`iterations_gap` | If output is verbose - number of iterations after issue a status message (Default=1 - displayed only if verbose=TRUE)

### Details

The gene names in `x_1` and `x_2` do not need to be in the same order, but must be in the same namespace. Only genes present in both datasets will be considered for the analysis.

### Value

A `data.frame` with the additional class attribute `csd_res` with the results of the CSD analysis. This frame has a row for each pair of genes and has the following columns:

Gene1

  Character, the name of the first gene

Gene2

  Character, the name of the second gene

rho1

  Mean correlation of the two genes in the first condition

rho2

  Mean correlation of the two genes in the second condition

var1

  The estimated variance of `rho1` determined by bootstrapping

var2

  The estimated variance of `rho2` determined by bootstrapping

cVal

  Numeric, the conserved score. A high value indicates that the co-expression of the two genes have the same sign in both conditions

sVal

  Numeric, the specific score. A high value indicates that the co-expression of the two genes have a high degree of co-expression in one condition, but not the other.

dVal

  Numeric, the differentiated score. A high value indicates that the co-expression of the two genes have a high degree of co-expression in both condition, but the sign of co-expression is different.

### References

Voigt A, Nowick K and Almaas E 'A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma' In: PLOS Computational Biology 13(9): e1005739. (doi: https://doi.org/10.1371/journal.pcbi.1005739)

### Examples

    data("sick_expression")
    data("normal_expression")
    cor_res <- run_csd(
        x_1 = sick_expression, x_2 = normal_expression,
        n_it = 100, nThreads = 2L
    )
    c_max <- max(cor_res$cVal)

[Package csdR version 1.0.0 Index]