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Get summary tables from a ColocBoost output.

Source: R/colocboost_output.R
get_colocboost_summary.Rd

get_colocboost_summary get colocalization and trait-specific summary table with or without the outcomes of interest.

Usage

get_colocboost_summary(
  cb_output,
  summary_level = 1,
  outcome_names = NULL,
  interest_outcome = NULL,
  region_name = NULL,
  min_abs_corr_between_ucos = 0.5,
  median_abs_corr_between_ucos = 0.8
)

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

summary_level

When summary_level = 1, return basic summary table for colocalization results. See details in get_ucos_summary function when summary_level = 2.

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.

min_abs_corr_between_ucos

Minimum absolute correlation for variants across two trait-specific (uncolocalized) effects to be considered colocalized. The default is 0.5.

median_abs_corr_between_ucos

Median absolute correlation for variants across two trait-specific (uncolocalized) effects to be considered colocalized. The default is 0.8.

Value

A list containing results from the ColocBoost analysis:

  • When summary_level = 1 (default):

    • cos_summary: 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 within 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

  • When summary_level = 2:

    • cos_summary: As described above

    • ucos_summary: A summary table for trait-specific (uncolocalized) effects

  • When summary_level = 3:

    • cos_summary: As described above

    • ucos_summary: A summary table for trait-specific (uncolocalized) effects

    • ambiguous_cos_summary: A summary table for ambiguous colocalization events from trait-specific effects

Details

When summary_level = 1, additional details and examples are introduced in get_cos_summary. When summary_level = 2 or summary_level = 3, additional details for trait-specific effects and ambiguous colocalization events are included. See get_ucos_summary for details on these tables.

See also

Other colocboost_inference: get_ambiguous_colocalization(), get_robust_colocalization()

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_colocboost_summary(res)
#> $cos_summary
#>   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
#>