Calculate purity statistics between all pairs of colocalization confidence sets (CoS)
Arguments
- cos
List of variables in CoS
- X
Genotype matrix of values of the p variables. Used to compute correlations if Xcorr is not provided.
- Xcorr
Correlation matrix of correlations between variables. Alternative to X.
- n_purity
The maximum number of CoS variables used in calculating the correlation (“purity”) statistics. When the number of variables included in the CoS is greater than this number, the CoS variables are randomly subsampled.
Value
A list containing three matrices (min_abs_cor, max_abs_cor, median_abs_cor) with purity statistics for all pairs of CoS. Diagonal elements represent within-CoS purity.
See also
Other colocboost_utilities:
get_cormat(),
get_cos(),
get_cos_summary(),
get_hierarchical_clusters(),
get_ucos_summary()
Examples
# colocboost example
set.seed(1)
N <- 1000
P <- 100
# Generate X with LD structure
sigma <- 0.9^abs(outer(1:P, 1:P, "-"))
X <- MASS::mvrnorm(N, rep(0, P), sigma)
colnames(X) <- paste0("SNP", 1:P)
L <- 3
true_beta <- matrix(0, P, L)
true_beta[10, 1] <- 0.5
true_beta[10, 2] <- 0.4
true_beta[50, 2] <- 0.3
true_beta[80, 3] <- 0.6
Y <- matrix(0, N, L)
for (l in 1:L) {
Y[, l] <- X %*% true_beta[, l] + rnorm(N, 0, 1)
}
res <- colocboost(X = X, Y = Y)
#> Validating input data.
#> Starting gradient boosting algorithm.
#> Gradient boosting for outcome 1 converged after 98 iterations!
#> Gradient boosting for outcome 3 converged after 106 iterations!
#> Gradient boosting for outcome 2 converged after 107 iterations!
#> Performing inference on colocalization events.
cos_res <- get_cos(res, coverage = 0.8)
get_cos_purity(cos_res$cos, X = X)
#> $min_abs_cor
#> cos1:y1_y2_coverage_0.8
#> cos1:y1_y2_coverage_0.8 1
#>
#> $max_abs_cor
#> cos1:y1_y2_coverage_0.8
#> cos1:y1_y2_coverage_0.8 1
#>
#> $median_abs_cor
#> cos1:y1_y2_coverage_0.8
#> cos1:y1_y2_coverage_0.8 1
#>