extract_TOP_coef_samples: Extracts regression coefficients from TOP posterior samples

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

Extracts regression coefficients from TOP posterior samples

Description

Extracts regression coefficients from TOP posterior samples

Usage

extract_TOP_coef_samples(
  TOP_samples,
  tf_cell_combos,
  assembled_training_data,
  tf_name,
  cell_type,
  n_bins = 5,
  level = c("bottom", "middle", "top")
)

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 using assembled_training_data.
assembled_training_data	Assembled training data as in the assemble_training_data() function.
tf_name	TF name.
cell_type	Cell type.
n_bins	Number of DNase or ATAC bins in TOP model (default = 5).
level	The level in the TOP model (options: ‘bottom’, ‘middle’, or ‘top’). ‘bottom’ level: TF- and cell-type- specific; ‘middle’ level: TF-specific, cell-type generic; ‘top’ level: TF-generic

Value

A data frame of posterior samples for TOP's regression coefficients.

Examples

## Not run: 
TOP_coef_samples <- extract_TOP_coef_samples(TOP_samples,
                                             assembled_training_data = assembled_training_data)
# or
tf_cell_combos <- extract_tf_cell_combos(assembled_training_data)
TOP_coef_samples <- extract_TOP_coef_samples(TOP_samples,
                                             tf_cell_combos = tf_cell_combos)

## End(Not run)

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