Get colocalization summary table from a ColocBoost output.

get_cos_summary get the colocalization summary table with or without the outcomes of interest.

Usage

get_cos_summary(
  cb_output,
  outcome_names = NULL,
  interest_outcome = NULL,
  region_name = NULL
)

Source

See detailed instructions in our tutorial portal: https://statfungen.github.io/colocboost/articles/Interpret_ColocBoost_Output.html

Arguments

cb_output

Output object from colocboost analysis

outcome_names

Optional vector of names of outcomes, which has the same order as Y in the original analysis.

interest_outcome

Optional vector specifying a subset of outcomes from outcome_names to focus on. When provided, only colocalization events that include at least one of these outcomes will be returned.

region_name

Optional character string. When provided, adds a column with this gene name to the output table for easier filtering in downstream analyses.

Value

A summary table for colocalization events with the following columns:

focal_outcome

The focal outcome being analyzed if exists. Otherwise, it is FALSE.

colocalized_outcomes

Colocalized outcomes for colocalization confidence set (CoS)

cos_id

Unique identifier for colocalization confidence set (CoS)

purity

Minimum absolute correlation of variables with in colocalization confidence set (CoS)

top_variable

The variable with highest variant colocalization probability (VCP)

top_variable_vcp

Variant colocalization probability for the top variable

cos_npc

Normalized probability of colocalization

min_npc_outcome

Minimum normalized probability of colocalized traits

n_variables

Number of variables in colocalization confidence set (CoS)

colocalized_index

Indices of colocalized variables

colocalized_variables

List of colocalized variables

colocalized_variables_vcp

Variant colocalization probabilities for all colocalized variables

See also

Other colocboost_utilities: get_cormat(), get_cos(), get_cos_purity(), 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_summary(res)
#>   focal_outcome colocalized_outcomes     cos_id    purity top_variable
#> 1         FALSE               Y1; Y2 cos1:y1_y2 0.9047605        SNP10
#>   top_variable_vcp cos_npc min_npc_outcome n_variables colocalized_index
#> 1        0.8519757  0.9999          0.9999           2             10; 9
#>   colocalized_variables            colocalized_variables_vcp
#> 1           SNP10; SNP9 0.851975728205876; 0.118928231146593