This function packs the entire DC analysis pipeline using the z-score method. It simplifies the implementation of the analysis and increases the flexibility of the analysis (not just limited to all pairwise comparisons).
dcZscore(
emat,
condition,
from = NULL,
to = NULL,
fdrthresh = 0.1,
cor.method = c("spearman", "pearson")
)a matrix, Matrix, data.frame, ExpressionSet, SummarizedExperiment or DGEList
a numeric, (with 1's and 2's representing a binary condition), a factor with 2 levels or a character representing 2 conditions
a character vector, with the names of nodes from which comparisons need to be performed.
a character vector, with the names of nodes to which comparisons need to be performed.
a numeric, specifying the FDR cutoff to apply to the inferred network.
a character, either 'spearman' (default) or 'pearson' specifying the correlation computation method to use.
an igraph object, containing the differential coexpression network.
x <- matrix(rnorm(60), 10, 30)
rownames(x) = 1:10
cond <- rep(1:2, 15)
dcZscore(x, cond)
#> IGRAPH d65685b UN-- 10 34 --
#> + attr: name (v/c), size (v/n), color (v/c), fdr (e/n), z.score (e/n),
#> | cor.1 (e/n), cor.2 (e/n), color (e/c)
#> + edges from d65685b (vertex names):
#> [1] 1 --2 1 --3 1 --4 1 --5 1 --7 1 --10 2 --3 2 --5 2 --6 2 --7
#> [11] 2 --8 2 --9 2 --10 3 --4 3 --5 3 --6 3 --8 3 --9 10--3 4 --5
#> [21] 4 --6 4 --7 4 --8 4 --9 10--4 5 --7 5 --8 6 --7 6 --8 7 --8
#> [31] 7 --9 10--7 8 --9 10--8
dcZscore(x, cond, to = 1:2)
#> IGRAPH 0711382 UN-- 10 13 --
#> + attr: name (v/c), size (v/n), color (v/c), fdr (e/n), z.score (e/n),
#> | cor.1 (e/n), cor.2 (e/n), color (e/c)
#> + edges from 0711382 (vertex names):
#> [1] 1--2 1--3 1--4 1--5 1--7 1--10 2--3 2--5 2--6 2--7 2--8 2--9
#> [13] 2--10