sceptre is an R package for single-cell CRISPR screen data analysis, emphasizing statistical rigor, massive scalability, and ease of use.
Author
Maintainer: Timothy Barry tbarry@hsph.harvard.edu (ORCID)
Authors:
Joseph Deutsch
Eugene Katsevich ekatsevi@wharton.upenn.edu
Examples
##########################
# 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)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 20729 cells
#> • 299 responses
#> • Low multiplicity-of-infection
#> • 101 targeting gRNAs (distributed across 26 targets)
#> • 9 non-targeting gRNAs
#> • 6 covariates (bio_rep, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✗ set_analysis_parameters()
#> ✗ assign_grnas()
#> ✗ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: not specified
#> • Positive control pairs: not specified
#> • Sidedness of test: not specified
#> • Control group: not specified
#> • Resampling mechanism: not specified
#> • gRNA integration strategy: not specified
#> • Resampling approximation: not specified
#> • Multiple testing adjustment: none
#> • N nonzero treatment cells threshold: not specified
#> • N nonzero control cells threshold: not specified
#> • Formula object: not specified
# 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)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 20729 cells
#> • 299 responses
#> • Low multiplicity-of-infection
#> • 101 targeting gRNAs (distributed across 26 targets)
#> • 9 non-targeting gRNAs
#> • 6 covariates (bio_rep, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✗ assign_grnas()
#> ✗ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 7765 pairs
#> • Positive control pairs: data frame with 9 pairs
#> • Sidedness of test: both
#> • Control group: non-targeting cells
#> • Resampling mechanism: permutations
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: not specified
#> • N nonzero control cells threshold: not specified
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + bio_rep
# 3. assign grnas
plot_grna_count_distributions(sceptre_object)
sceptre_object <- sceptre_object |> assign_grnas()
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 20729 cells
#> • 299 responses
#> • Low multiplicity-of-infection
#> • 101 targeting gRNAs (distributed across 26 targets)
#> • 9 non-targeting gRNAs
#> • 6 covariates (bio_rep, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✗ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 7765 pairs
#> • Positive control pairs: data frame with 9 pairs
#> • Sidedness of test: both
#> • Control group: non-targeting cells
#> • Resampling mechanism: permutations
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: not specified
#> • N nonzero control cells threshold: not specified
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + bio_rep
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: maximum
#> • Mean N cells per gRNA: 188.45
#> • Mean N gRNAs per cell (MOI): not computed when using "maximum" assignment method
# 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)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 20729 cells (15348 after cellwise QC)
#> • 299 responses
#> • Low multiplicity-of-infection
#> • 101 targeting gRNAs (distributed across 26 targets)
#> • 9 non-targeting gRNAs
#> • 6 covariates (bio_rep, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 7765 pairs (6205 after pairwise QC)
#> • Positive control pairs: data frame with 9 pairs (9 after pairwise QC)
#> • Sidedness of test: both
#> • Control group: non-targeting cells
#> • Resampling mechanism: permutations
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + bio_rep
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: maximum
#> • Mean N cells per gRNA: 188.45
#> • Mean N gRNAs per cell (MOI): not computed when using "maximum" assignment method
# 5. run the calibration check
sceptre_object <- run_calibration_check(sceptre_object, parallel = TRUE, n_processors = 2)
#> Constructing negative control pairs. ✓
#> Generating permutation resamples. ✓
#> Running calibration_check in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files calibration_check_*.out for progress updates.
#> ✓
#>
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 20729 cells (15348 after cellwise QC)
#> • 299 responses
#> • Low multiplicity-of-infection
#> • 101 targeting gRNAs (distributed across 26 targets)
#> • 9 non-targeting gRNAs
#> • 6 covariates (bio_rep, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✓ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 7765 pairs (6205 after pairwise QC)
#> • Positive control pairs: data frame with 9 pairs (9 after pairwise QC)
#> • Sidedness of test: both
#> • Control group: non-targeting cells
#> • Resampling mechanism: permutations
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + bio_rep
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: maximum
#> • Mean N cells per gRNA: 188.45
#> • Mean N gRNAs per cell (MOI): not computed when using "maximum" assignment method
#>
#> Summary of results:
#> • N negative control pairs called as significant: 0/6205
#> • Mean log-2 FC for negative control pairs: -0.0027
# 6. run power check
sceptre_object <- run_power_check(sceptre_object, parallel = TRUE, n_processors = 2)
#> Generating permutation resamples. ✓
#> Running power_check in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files power_check_*.out for progress updates.
#> ✓
#>
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 20729 cells (15348 after cellwise QC)
#> • 299 responses
#> • Low multiplicity-of-infection
#> • 101 targeting gRNAs (distributed across 26 targets)
#> • 9 non-targeting gRNAs
#> • 6 covariates (bio_rep, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✓ run_calibration_check()
#> ✓ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 7765 pairs (6205 after pairwise QC)
#> • Positive control pairs: data frame with 9 pairs (9 after pairwise QC)
#> • Sidedness of test: both
#> • Control group: non-targeting cells
#> • Resampling mechanism: permutations
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + bio_rep
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: maximum
#> • Mean N cells per gRNA: 188.45
#> • Mean N gRNAs per cell (MOI): not computed when using "maximum" assignment method
#>
#> Summary of results:
#> • N negative control pairs called as significant: 0/6205
#> • Mean log-2 FC for negative control pairs: -0.0027
#> • Median positive control p-value: 1.3e-18
# 7. run discovery analysis
sceptre_object <- run_discovery_analysis(sceptre_object, parallel = TRUE, n_processors = 2)
#> Generating permutation resamples. ✓
#> Running discovery_analysis in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files discovery_analysis_*.out for progress updates.
#> ✓
#>
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 20729 cells (15348 after cellwise QC)
#> • 299 responses
#> • Low multiplicity-of-infection
#> • 101 targeting gRNAs (distributed across 26 targets)
#> • 9 non-targeting gRNAs
#> • 6 covariates (bio_rep, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✓ run_calibration_check()
#> ✓ run_power_check()
#> ✓ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 7765 pairs (6205 after pairwise QC)
#> • Positive control pairs: data frame with 9 pairs (9 after pairwise QC)
#> • Sidedness of test: both
#> • Control group: non-targeting cells
#> • Resampling mechanism: permutations
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + bio_rep
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: maximum
#> • Mean N cells per gRNA: 188.45
#> • Mean N gRNAs per cell (MOI): not computed when using "maximum" assignment method
#>
#> Summary of results:
#> • N negative control pairs called as significant: 0/6205
#> • Mean log-2 FC for negative control pairs: -0.0027
#> • Median positive control p-value: 1.3e-18
#> • N discovery pairs called as significant: 453/6205
# 8. write results
write_outputs_to_directory(sceptre_object = sceptre_object, "~/sceptre_outputs_lowmoi/")
#> NULL
##########################
# 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
)
#> Processing directory 1. ✓
#> Processing directory 2. ✓
#> Combining matrices across directories. ✓
#> Creating the sceptre object. ✓
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 45919 cells
#> • 526 responses
#> • High multiplicity-of-infection
#> • 70 targeting gRNAs (distributed across 35 targets)
#> • 25 non-targeting gRNAs
#> • 6 covariates (batch, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✗ set_analysis_parameters()
#> ✗ assign_grnas()
#> ✗ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: not specified
#> • Positive control pairs: not specified
#> • Sidedness of test: not specified
#> • Resampling mechanism: not specified
#> • gRNA integration strategy: not specified
#> • Resampling approximation: not specified
#> • Multiple testing adjustment: none
#> • N nonzero treatment cells threshold: not specified
#> • N nonzero control cells threshold: not specified
#> • Formula object: not specified
# 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)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 45919 cells
#> • 526 responses
#> • High multiplicity-of-infection
#> • 70 targeting gRNAs (distributed across 35 targets)
#> • 25 non-targeting gRNAs
#> • 6 covariates (batch, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✗ assign_grnas()
#> ✗ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 610 pairs
#> • Positive control pairs: data frame with 10 pairs
#> • Sidedness of test: left
#> • Resampling mechanism: conditional resampling
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: not specified
#> • N nonzero control cells threshold: not specified
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
# 3. assign grnas
plot_grna_count_distributions(sceptre_object)
sceptre_object <- sceptre_object |> assign_grnas(parallel = TRUE, n_processors = 2)
#> Running gRNA assignments in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files assign_grnas_*.out for progress updates.
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 45919 cells
#> • 526 responses
#> • High multiplicity-of-infection
#> • 70 targeting gRNAs (distributed across 35 targets)
#> • 25 non-targeting gRNAs
#> • 6 covariates (batch, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✗ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 610 pairs
#> • Positive control pairs: data frame with 10 pairs
#> • Sidedness of test: left
#> • Resampling mechanism: conditional resampling
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: not specified
#> • N nonzero control cells threshold: not specified
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: mixture
#> • Mean N cells per gRNA: 567.77
#> • Mean N gRNAs per cell (MOI): 1.17
#> • gRNA assignment formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
# 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)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 45919 cells (44308 after cellwise QC)
#> • 526 responses
#> • High multiplicity-of-infection
#> • 70 targeting gRNAs (distributed across 35 targets)
#> • 25 non-targeting gRNAs
#> • 6 covariates (batch, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✗ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 610 pairs (599 after pairwise QC)
#> • Positive control pairs: data frame with 10 pairs (10 after pairwise QC)
#> • Sidedness of test: left
#> • Resampling mechanism: conditional resampling
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: mixture
#> • Mean N cells per gRNA: 567.77
#> • Mean N gRNAs per cell (MOI): 1.17
#> • gRNA assignment formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
# 5. run the calibration check
sceptre_object <- run_calibration_check(sceptre_object, parallel = TRUE, n_processors = 2)
#> Constructing negative control pairs. ✓
#> Running calibration_check in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files calibration_check_*.out for progress updates. ✓
#>
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 45919 cells (44308 after cellwise QC)
#> • 526 responses
#> • High multiplicity-of-infection
#> • 70 targeting gRNAs (distributed across 35 targets)
#> • 25 non-targeting gRNAs
#> • 6 covariates (batch, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✓ run_calibration_check()
#> ✗ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 610 pairs (599 after pairwise QC)
#> • Positive control pairs: data frame with 10 pairs (10 after pairwise QC)
#> • Sidedness of test: left
#> • Resampling mechanism: conditional resampling
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: mixture
#> • Mean N cells per gRNA: 567.77
#> • Mean N gRNAs per cell (MOI): 1.17
#> • gRNA assignment formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> Summary of results:
#> • N negative control pairs called as significant: 0/599
#> • Mean log-2 FC for negative control pairs: 0.0039
# 6. run the power check
sceptre_object <- run_power_check(sceptre_object, parallel = TRUE, n_processors = 2)
#> Running power_check in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files power_check_*.out for progress updates. ✓
#>
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 45919 cells (44308 after cellwise QC)
#> • 526 responses
#> • High multiplicity-of-infection
#> • 70 targeting gRNAs (distributed across 35 targets)
#> • 25 non-targeting gRNAs
#> • 6 covariates (batch, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✓ run_calibration_check()
#> ✓ run_power_check()
#> ✗ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 610 pairs (599 after pairwise QC)
#> • Positive control pairs: data frame with 10 pairs (10 after pairwise QC)
#> • Sidedness of test: left
#> • Resampling mechanism: conditional resampling
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: mixture
#> • Mean N cells per gRNA: 567.77
#> • Mean N gRNAs per cell (MOI): 1.17
#> • gRNA assignment formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> Summary of results:
#> • N negative control pairs called as significant: 0/599
#> • Mean log-2 FC for negative control pairs: 0.0039
#> • Median positive control p-value: 1.1e-137
# 7. run discovery analysis
sceptre_object <- run_discovery_analysis(sceptre_object, parallel = TRUE, n_processors = 2)
#> Running discovery_analysis in parallel. Change directories to /var/folders/7v/5sqjgh8j28lgf8qx3gbtq1h00000gp/T//RtmpHhxNRw/sceptre_logs/ and view the files discovery_analysis_*.out for progress updates. ✓
#>
plot(sceptre_object)
print(sceptre_object)
#> An object of class sceptre_object.
#>
#> Attributes of the data:
#> • 45919 cells (44308 after cellwise QC)
#> • 526 responses
#> • High multiplicity-of-infection
#> • 70 targeting gRNAs (distributed across 35 targets)
#> • 25 non-targeting gRNAs
#> • 6 covariates (batch, grna_n_nonzero, grna_n_umis, response_n_nonzero, response_n_umis, response_p_mito)
#>
#> Analysis status:
#> ✓ import_data()
#> ✓ set_analysis_parameters()
#> ✓ assign_grnas()
#> ✓ run_qc()
#> ✓ run_calibration_check()
#> ✓ run_power_check()
#> ✓ run_discovery_analysis()
#>
#> Analysis parameters:
#> • Discovery pairs: data frame with 610 pairs (599 after pairwise QC)
#> • Positive control pairs: data frame with 10 pairs (10 after pairwise QC)
#> • Sidedness of test: left
#> • Resampling mechanism: conditional resampling
#> • gRNA integration strategy: union
#> • Resampling approximation: skew normal
#> • Multiple testing adjustment: BH at level 0.1
#> • N nonzero treatment cells threshold: 7
#> • N nonzero control cells threshold: 7
#> • Formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> gRNA-to-cell assignment information:
#> • Assignment method: mixture
#> • Mean N cells per gRNA: 567.77
#> • Mean N gRNAs per cell (MOI): 1.17
#> • gRNA assignment formula object: log(response_n_nonzero) + log(response_n_umis) + log(grna_n_nonzero) + log(grna_n_umis) + batch
#>
#> Summary of results:
#> • N negative control pairs called as significant: 0/599
#> • Mean log-2 FC for negative control pairs: 0.0039
#> • Median positive control p-value: 1.1e-137
#> • N discovery pairs called as significant: 11/599
# 8. write results
write_outputs_to_directory(sceptre_object = sceptre_object, "~/sceptre_outputs_highmoi/")
#> NULL