Process Credible Set Information and Determine Updating Strategy

This function categorizes Credible Sets (CS) within a study block into different updating strategies based on their statistical properties and correlations.

Usage

auto_decision(df, high_corr_cols)

Arguments

df

Data frame. Contains information about Credible Sets for a specific study and block.

high_corr_cols

Character vector. Names of columns in df that represent high correlations.

Value

A modified data frame with additional columns attached to the diagnostic table:

top_cs

Logical. TRUE for the CS with the highest absolute Z-score.

tagged_cs

Logical. TRUE for CS that are considered "tagged" based on p-value and correlation criteria.

method

Character. The determined updating strategy ("BVSR", "SER", or "BCR").

Details

This function performs the following steps: 1. Identifies the top CS based on the highest absolute Z-score. 2. Identifies tagged CS based on high p-value and high correlations. 3. Counts total, tagged, and remaining CS. 4. Determines the appropriate updating method based on these counts.

The updating methods are: - BVSR (Bayesian Variable Selection Regression): Used when there's only one CS or all CS are accounted for. - SER (Single Effect Regression): Used when there are tagged CS but no remaining untagged CS. - BCR (Bayesian Conditional Regression): Used when there are remaining untagged CS.

Note

This function is part of a developing methodology for automatically handling finemapping results. The thresholds and criteria used (e.g., p-value > 1e-4 for tagging) are subject to refinement and may change in future versions.