sceptre-package | R Documentation |
sceptre
is an R package for single-cell CRISPR screen data analysis, emphasizing statistical rigor, massive scalability, and ease of use.
Maintainer: Timothy Barry tbarry@hsph.harvard.edu (ORCID)
Authors:
Joseph Deutsch
Eugene Katsevich ekatsevi@wharton.upenn.edu
Useful links:
##########################
# Low-MOI CRISPRko example
##########################
library(sceptredata)
# 1. create the sceptre object
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"
)
print(sceptre_object)
# 2. set the analysis parameters
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"
)
sceptre_object <- set_analysis_parameters(
sceptre_object = sceptre_object,
discovery_pairs = discovery_pairs,
positive_control_pairs = positive_control_pairs
)
print(sceptre_object)
# 3. assign grnas
plot_grna_count_distributions(sceptre_object)
sceptre_object <- sceptre_object |> assign_grnas()
plot(sceptre_object)
print(sceptre_object)
# 4. run qc
plot_covariates(sceptre_object, p_mito_threshold = 0.075)
sceptre_object <- sceptre_object |> run_qc(p_mito_threshold = 0.075)
plot(sceptre_object)
print(sceptre_object)
# 5. run the calibration check
sceptre_object <- run_calibration_check(sceptre_object, parallel = TRUE, n_processors = 2)
plot(sceptre_object)
print(sceptre_object)
# 6. run power check
sceptre_object <- run_power_check(sceptre_object, parallel = TRUE, n_processors = 2)
plot(sceptre_object)
print(sceptre_object)
# 7. run discovery analysis
sceptre_object <- run_discovery_analysis(sceptre_object, parallel = TRUE, n_processors = 2)
plot(sceptre_object)
print(sceptre_object)
# 8. write results
write_outputs_to_directory(sceptre_object = sceptre_object, "~/sceptre_outputs_lowmoi/")
##########################
# High-MOI CRISPRi example
##########################
# 1. create the sceptre object from cellranger output
directories <- paste0(
system.file("extdata", package = "sceptredata"),
"/highmoi_example/gem_group_", c(1, 2)
)
data(grna_target_data_frame_highmoi)
sceptre_object <- import_data_from_cellranger(
directories = directories,
moi = "high",
grna_target_data_frame = grna_target_data_frame_highmoi
)
print(sceptre_object)
# 2. set the analysis parameters
positive_control_pairs <- construct_positive_control_pairs(sceptre_object)
discovery_pairs <- construct_cis_pairs(sceptre_object,
positive_control_pairs = positive_control_pairs,
distance_threshold = 5e6
)
sceptre_object <- set_analysis_parameters(
sceptre_object = sceptre_object,
discovery_pairs = discovery_pairs,
positive_control_pairs = positive_control_pairs,
side = "left"
)
print(sceptre_object)
# 3. assign grnas
plot_grna_count_distributions(sceptre_object)
sceptre_object <- sceptre_object |> assign_grnas(parallel = TRUE, n_processors = 2)
plot(sceptre_object)
print(sceptre_object)
# 4. run qc
plot_covariates(sceptre_object, p_mito_threshold = 0.075)
sceptre_object <- sceptre_object |> run_qc(p_mito_threshold = 0.075)
plot(sceptre_object)
print(sceptre_object)
# 5. run the calibration check
sceptre_object <- run_calibration_check(sceptre_object, parallel = TRUE, n_processors = 2)
plot(sceptre_object)
print(sceptre_object)
# 6. run the power check
sceptre_object <- run_power_check(sceptre_object, parallel = TRUE, n_processors = 2)
plot(sceptre_object)
print(sceptre_object)
# 7. run discovery analysis
sceptre_object <- run_discovery_analysis(sceptre_object, parallel = TRUE, n_processors = 2)
plot(sceptre_object)
print(sceptre_object)
# 8. write results
write_outputs_to_directory(sceptre_object = sceptre_object, "~/sceptre_outputs_highmoi/")
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