predict_TOP: Predicts quantitative TF occupancy or TF binding probability

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

Predicts quantitative TF occupancy or TF binding probability

Description

Predicts quantitative TF occupancy or TF binding probability using TOP model trained from ChIP-seq read counts or binary labels.

Usage

predict_TOP(
  data,
  TOP_coef,
  tf_name,
  cell_type,
  use_model = c("ATAC", "DukeDNase", "UwDNase"),
  level = c("best", "bottom", "middle", "top"),
  logistic_model = FALSE,
  transform = c("asinh", "log2", "log", "none")
)

Arguments

data	A data frame containing motif PWM score and DNase (or ATAC) bins.
TOP_coef	A list containing the posterior mean of TOP regression coefficients.
tf_name	TF name to make predictions for. It will find the model parameters trained for this TF. This is not needed (not used) when level = 'top'.
cell_type	Cell type to make predictions for. It will find the model parameters trained for this cell type. This is not needed (not used) when level = 'middle' or level = 'top'.
use_model	Uses pretrained model if TOP_coef is not supplied. Options: ‘ATAC’, ‘DukeDNase’, ‘UwDNase’.
level	TOP model level to use. Options: ‘best’, ‘bottom’, ‘middle’, or ‘top’. When level = 'best', uses the best (lowest available) level of the hierarchy for the TF x cell type combination. If the TF motif and cell type is available in the training data, then uses the bottom level (TF- and cell-type-specific model). otherwise, if TF motif (but not cell type) is available in the training data, chooses the middle level (TF-specific model) of that TF motif; otherwise, uses the top level TF-generic model. When level = 'bottom', uses the bottom level (TF- and cell-type-specific model), if the TF motif and cell type is available in the training data. When level = 'middle', uses the middle level (TF-specific model) of that TF. When level = 'top', uses the top level TF-generic model.
logistic_model	Logical. Whether to use the logistic version of TOP model. If logistic_model = TRUE, uses the logistic version of TOP model to predict TF binding probability. If logistic_model = FALSE, uses the quantitative occupancy model (default).
transform	Type of transformation performed for ChIP-seq read counts when preparing the input training data. Options are: ‘asinh’(asinh transformation), ‘log2’ (log2 transformation), ‘sqrt’ (sqrt transformation), and ‘none’ (no transformation). This only applies when logistic_model = FALSE.

Value

Returns a list with the following elements,

model	TOP model name.
level	selected hierarchy level.
coef	posterior mean of regression coefficients.
predictions	a data frame with the data and predicted values.

Examples

## Not run: 
# Predicts CTCF occupancy in K562 using the quantitative occupancy model:

# Predicts using the 'bottom' level model
result <- predict_TOP(data, TOP_coef,
                      tf_name = 'CTCF', cell_type = 'K562',
                      level = 'bottom',
                      logistic_model = FALSE,
                      transform = 'asinh')

# Predicts using the 'best' model
# Since CTCF in K562 cell type is included in training,
# the 'best' model is the 'bottom' level model.
result <- predict_TOP(data, TOP_coef,
                      tf_name = 'CTCF', cell_type = 'K562', level = 'best',
                      logistic_model = FALSE,
                      transform = 'asinh')

# We can use the 'middle' model to predict CTCF in K562
# or other cell types or conditions
result <- predict_TOP(data, TOP_coef,
                      tf_name = 'CTCF', level = 'middle',
                      logistic_model = FALSE,
                      transform = 'asinh')

# Predicts CTCF binding probability using the logistic version of the model:
# No need to set the argument for 'transform' for the logistic model.

# Predicts using the 'bottom' level model
result <- predict_TOP(data, TOP_coef,
                     tf_name = 'CTCF', cell_type = 'K562',
                     level = 'best',
                     logistic_model = TRUE)

# Predicts using the 'middle' level model
result <- predict_TOP(data, TOP_coef,
                     tf_name = 'CTCF', level = 'middle',
                     logistic_model = TRUE)

# If TOP_coef is not specified, it will automatically use the
# pretrained models included in the package.

# Predicts using pretrained ATAC quantitative occupancy model
result <- predict_TOP(data,
                      tf_name = 'CTCF', cell_type = 'K562',
                      use_model = 'ATAC', level = 'best',
                      logistic_model = FALSE,
                      transform = 'asinh')

# Predicts using pretrained ATAC logistic model
result <- predict_TOP(data,
                      tf_name = 'CTCF', cell_type = 'K562',
                      use_model = 'ATAC', level = 'best',
                      logistic_model = TRUE)

## End(Not run)

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