extract_TOP_mean_coef: Extracts the posterior mean of regression coefficients for...

In HarteminkLab/TOP: Predict transcription factor occupancy using DNase- or ATAC-seq data

Extracts the posterior mean of regression coefficients for each level of TOP model

Description

Extracts regression coefficients from TOP posterior samples, and returns the posterior mean of coefficients for each level of TOP model.

Usage

extract_TOP_mean_coef(
  TOP_samples,
  tf_cell_combos,
  assembled_training_data,
  n_bins = 5
)

Arguments

TOP_samples	TOP samples combined from all partitions using the combine_TOP_samples() function.
tf_cell_combos	A table with the indices and names of TF and cell type combinations from the assembled training data. If missing, will create the table usi9ng assembled_training_data.
assembled_training_data	Assembled training data as in the assemble_training_data function.
n_bins	Number of DNase or ATAC bins in TOP model (default = 5)

Value

A list of posterior mean regression coefficients for each level of TOP model.

Examples

## Not run: 
TOP_mean_coef <- extract_TOP_mean_coef(TOP_samples,
                                       assembled_training_data = assembled_training_data)

# or
tf_cell_combos <- extract_tf_cell_combos(assembled_training_data)
TOP_mean_coef <- extract_TOP_mean_coef(TOP_samples,
                                       tf_cell_combos = tf_cell_combos)

## End(Not run)

HarteminkLab/TOP documentation built on July 27, 2023, 6:14 p.m.