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Extract CoS at different coverage

Source: R/colocboost_output.R
get_cos.Rd

get_cos extracts colocalization confidence sets (CoS) at different coverage levels from ColocBoost results. When genotype data (X) or correlation matrix (Xcorr) is provided, it can also calculate and filter CoS based on purity statistics, ensuring that variants within each CoS are sufficiently correlated.

Usage

get_cos(
  cb_output,
  coverage = 0.95,
  X = NULL,
  Xcorr = NULL,
  n_purity = 100,
  min_abs_corr = 0.5,
  median_abs_corr = NULL
)

Arguments

cb_output

Output object from colocboost analysis

coverage

A number between 0 and 1 specifying the “coverage” of the estimated colocalization confidence sets (CoS) (default is 0.95).

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.

min_abs_corr

The minimum absolute correlation value of variants in a CoS to be considered pass (“purity”) statistics.

median_abs_corr

The median absolute correlation value of variants in a CoS to be considered pass (“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 of indices of variables in each CoS.

See also

Other colocboost_utilities: get_cormat(), get_cos_purity(), 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 # SNP10 affects trait 1
true_beta[10, 2] <- 0.4 # SNP10 also affects trait 2 (colocalized)
true_beta[50, 2] <- 0.3 # SNP50 only affects trait 2
true_beta[80, 3] <- 0.6 # SNP80 only affects trait 3
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.
get_cos(res, coverage = 0.99, X = X)
#> $cos
#> $cos$`cos1:y1_y2_coverage_0.99`
#> [1] 10  9 11
#> 
#> 
#> $cos_purity
#> $cos_purity$min_abs_cor
#>                          cos1:y1_y2_coverage_0.99
#> cos1:y1_y2_coverage_0.99                0.8333458
#> 
#> $cos_purity$max_abs_cor
#>                          cos1:y1_y2_coverage_0.99
#> cos1:y1_y2_coverage_0.99                0.8816448
#> 
#> $cos_purity$median_abs_cor
#>                          cos1:y1_y2_coverage_0.99
#> cos1:y1_y2_coverage_0.99                0.9047605
#> 
#> 
get_cos(res, coverage = 0.99, X = X, min_abs_corr = 0.95)
#> $cos
#> NULL
#> 
#> $cos_purity
#> NULL
#>