Run CSD analysis — run

This function implements the CSD algorithm 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.

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. Missing values are not allowed.
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 between each status message (Default=1 - Displayed only if verbose=TRUE)

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.

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. The parallelism gained by nThreads applies to the computations within a single iteration. The iterations are run is serial in order to reduce the memory footprint.

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
)
#> Thu Nov  7 15:37:56 2024 => Running CSD with
#> 504 samples from condition 1
#> 399 samples from condition 2
#> Number of genes: 1000
#> Number of bootstrap iterations: 100
#> Number of threads: 2
#> Thu Nov  7 15:37:56 2024 => Running correlation bootstrapping on first condition...
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#> Thu Nov  7 15:38:07 2024 => Summarizing results
#> Thu Nov  7 15:38:07 2024 => Returning from CSD procedure...
c_max <- max(cor_res$cVal)