Ability to correlate to other rows

Ability to correlate to other rows

ATC(mat, cor_fun = stats::cor, min_cor = 0, power = 1, k_neighbours = -1, group = NULL, mc.cores = 1, cores = mc.cores, ...)

Arguments

mat

A numeric matrix. ATC score is calculated by rows.

cor_fun

A function which calculates correlations.

min_cor

Cutoff for the minimal absolute correlation.

power

Power on the correlation values.

k_neighbours

Nearest k neighbours.

mc.cores

Number of cores. This argument will be removed in future versions.

cores

Number of cores.

group

A categorical variable. If it is specified, the correlation is only calculated for the rows in the same group as current row.

...

Pass to cor_fun.

Details

For a given row in a matrix, the ATC score is the area above the curve of the curmulative density distribution of the absolute correlation to all other rows. Formally, if F_i(X) is the cumulative distribution function of X where X is the absolute correlation for row i with power power (i.e. x = cor^power), ATC_i = 1 - \int_{min_cor}^1 F_i(X).

By default the ATC scores are calculated by Pearson correlation, to use Spearman correlation, you can register a new top-value method by:

    register_top_value_methods(
        "ATC_spearman" = function(m) ATC(m, method = "spearman")
    )  

Similarly, to use a robust correlation method, e.g. bicor function, you can do like:

    register_top_value_methods(
        "ATC_bicor" = function(m) ATC(m, cor_fun = WGCNA::bicor)
    )  

If the number of rows execeeds 30000, it internally uses ATC_approx.

See also

https://jokergoo.github.io/cola_supplementary/suppl_1_ATC/suppl_1_ATC.html

Value

A vector of numeric values with the same order as rows in the input matrix.

Author

Zuguang Gu <z.gu@dkfz.de>

Examples

set.seed(12345)
nr1 = 100
mat1 = matrix(rnorm(100*nr1), nrow = nr1)

nr2 = 10
require(mvtnorm)
#> Loading required package: mvtnorm
sigma = matrix(0.8, nrow = nr2, ncol = nr2); diag(sigma) = 1
mat2 = t(rmvnorm(100, mean = rep(0, nr2), sigma = sigma))

nr3 = 50
sigma = matrix(0.5, nrow = nr3, ncol = nr3); diag(sigma) = 1
mat3 = t(rmvnorm(100, mean = rep(0, nr3), sigma = sigma))

mat = rbind(mat1, mat2, mat3)
ATC_score = ATC(mat)
plot(ATC_score, pch = 16, col = c(rep(1, nr1), rep(2, nr2), rep(3, nr3)))