construct_trans_pairs() is a helper function to facilitate construction the set of trans pairs. construct_trans_pairs() returns the entire set of possible target-response pairs. construct_trans_pairs() is a useful pair constructor function for analyses in which we seek to conduct a trans analysis, testing each target against each response. construct_trans_pairs() takes as arguments sceptre_object (required), positive_control_pairs (optional), and pairs_to_exclude (optional). By default construct_trans_pairs() returns a data frame with columns grna_target and response_id, where each gRNA target is mapped to each response ID.
Usage
construct_trans_pairs(
sceptre_object,
positive_control_pairs = data.frame(),
pairs_to_exclude = "none"
)Arguments
- sceptre_object
a
sceptre_object- positive_control_pairs
(optional) the set of positive control pairs
- pairs_to_exclude
(optional; default
"none") a string specifying pairs to exclude from the trans pairs, one of"none","pc_pairs", or"pairs_containing_pc_targets"
Details
The optional argument pairs_to_exclude enables the user to remove specific pairs from the trans set and takes values "none", "pc_pairs", or "pairs_containing_pc_targets". If pairs_to_exclude is set to "none" (the default), then no pairs are removed from the trans set. Next, if pairs_to_exclude is set to "pc_pairs" (and the positive_control_pairs data frame is passed), then then the positive control target-response pairs are excluded from the trans set. Finally, if pairs_to_exclude is set to "pairs_containing_pc_targets" (and positive_control_pairs is passed), then all pairs containing a positive control gRNA target are excluded from the trans pairs. (In this sense setting pairs_to_exclude to "pairs_containing_pc_targets" is stronger than setting pairs_to_exclude to "pc_pairs".) Typically, in gene-targeting (resp., noncoding-regulatory-element-targeting) screens, we set pairs_to_exclude to "pc_pairs" (resp., "pairs_containing_pc_targets"). See Section 2.2.2 of the manual for more detailed information about this function.
Examples
library(sceptredata)
# 1. low-moi, gene-targeting screen
data("lowmoi_example_data")
sceptre_object <- import_data(
response_matrix = lowmoi_example_data$response_matrix,
grna_matrix = lowmoi_example_data$grna_matrix,
extra_covariates = lowmoi_example_data$extra_covariates,
grna_target_data_frame = lowmoi_example_data$grna_target_data_frame,
moi = "low"
)
positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
discovery_pairs <- construct_trans_pairs(
sceptre_object = sceptre_object,
positive_control_pairs = positive_control_pairs,
pairs_to_exclude = "pc_pairs"
)
# 2. high-moi, enhancer-targeting screen
data(highmoi_example_data)
data(grna_target_data_frame_highmoi)
# import data
sceptre_object <- import_data(
response_matrix = highmoi_example_data$response_matrix,
grna_matrix = highmoi_example_data$grna_matrix,
grna_target_data_frame = grna_target_data_frame_highmoi,
moi = "high",
extra_covariates = highmoi_example_data$extra_covariates,
response_names = highmoi_example_data$gene_names
)
positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
discovery_pairs <- construct_trans_pairs(
sceptre_object = sceptre_object,
positive_control_pairs = positive_control_pairs,
pairs_to_exclude = "pairs_containing_pc_targets"
)