ATC: Ability to correlate to other rows
In cola: A Framework for Consensus Partitioning

Description Usage Arguments Details Value Author(s) See Also Examples

Ability to correlate to other rows

1 2	ATC(mat, cor_fun = stat::cor, min_cor = 0, power = 1, mc.cores = 1, n_sampling = c(1000, 500), q_sd = 0, group = NULL, ...)

`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.
`mc.cores`	Number of cores.
`n_sampling`	When there are too many rows in the matrix, to get the curmulative distribution of how one row correlates other rows, actually we don't need to use all the rows in the matrix, e.g. 1000 rows can already give a very nice estimation.
`q_sd`	Percentile of the standard deviation for the rows. Rows with values less than it are ignored.
`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`.

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:

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    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:

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

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

Zuguang Gu <z.gu@dkfz.de>

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

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

nr2 = 10
require(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)))