R/GI_screen.R
In ytakemon/GINIR: Genetic inteRaction and EssenTiality neTwork mApper
Defines functions
GI_screen
Documented in
GI_screen
#' @title Perform genetic interaction screen
#'
#' @description Compares dependency probabilities of mutant and control groups to determine
#' whether the mutant group are resistant or sensitive to specific gene perturbations.
#'
#' @param control_id string, A vector containing two or more DepMap_id, Default: NULL
#' @param mutant_id string, A vector containing two or more DepMap_id, Default: NULL
#' @param gene_list string, A vector containing a list of Hugo symbols to subset the screen and to perform a small in-silico screen, Default: NULL
#' @param core_num integer, Number of cores to run analysis, Default: NULL
#' @param output_dir string, Full path to where output file should be saved, Default: NULL
#' @param data_dir string Path to GRETTA_data
#' @param filename string name of file without the '.csv' extension.
#' @param test logical, For test_that to shorten computational time for testing
#'
#' @return A data frame containing results from the genetic screen. A copy is also saved to the
#' directory defined in `output_dir`.
#'
#' @details Description of output data frame
#' * `GeneName_ID` - Hugo symbol with NCBI gene ID
#' * `GeneNames` - Hugo symbol
#' * `_median`, `_mean`, `_sd`, `_iqr` - Control and mutant group's median, mean, standard deviation (sd),
#' and interquartile range (iqr) of dependency probabilities. Dependency probabilities range from zero to one,
#' where one indicates a essential gene (ie. KO of gene was lethal) and zero indicates a non-essential gene
#' (KO of gene was not lethal)
#' * `Pval` - P-value from Mann Whitney U test between control and mutant groups.
#' * `Adj_pval` - BH-adjusted P-value.
#' * `log2FC_by_median` - Log2 normalized median fold change of dependency probabilities (mutant / control).
#' * `log2FC_by_mean` - Log2 normalized mean fold change of dependency probabilities (mutant / control).
#' * `CliffDelta` - Cliff's delta non-parametric effect size between mutant and control dependency probabilities.
#' Ranges between -1 to 1.
#' * `dip_pval` - Hartigan's dip test p-value. Tests whether distribution of mutant dependency probability is unimodel.
#' If dip test is rejected (p-value < 0.05), this indicates that there is a multimodel dependency probability distribution and
#' that there may be another factor contributing to this separation.
#' * `Interaction_score` - Combined value generated from signed p-values: `-log10(Pval) \* sign(log2FC_by_median)`
#' @md
#'
#' @examples
#' gretta_data_dir <- './GRETTA_example/'
#' gretta_output_dir <- './GRETTA_example_output/'
#'
#' if(!dir.exists(gretta_data_dir)){
#' download_example_data(".")
#' }
#'
#' if(!dir.exists(gretta_data_dir)){
#' download_example_data(".")
#' }
#'
#' Screen_results <- GI_screen(
#' control_id = c('ACH-001354', 'ACH-000274', 'ACH-001799'),
#' mutant_id = c('ACH-000911', 'ACH-001957', 'ACH-000075'),
#' gene_list = c('ARID1A', 'ARID1B', 'SMARCA2'),
#' core_num = 2,
#' output_dir = gretta_output_dir,
#' data_dir = gretta_data_dir)
#'
#' @rdname GI_screen
#' @export
#' @importFrom parallel detectCores
#' @importFrom doMC registerDoMC
#' @importFrom dplyr mutate filter group_by summarize pull rename
#' @importFrom forcats fct_relevel
#' @importFrom foreach `%dopar%` foreach
#' @importFrom rcompanion cliffDelta
#' @importFrom diptest dip.test
#' @importFrom broom tidy
#' @importFrom tidyr pivot_longer pivot_wider
#' @importFrom tidyselect everything
#' @importFrom readr write_csv
#' @importFrom stats median sd IQR wilcox.test p.adjust
GI_screen <- function(control_id = NULL, mutant_id = NULL,
gene_list = NULL, core_num = NULL, output_dir = NULL,
data_dir = NULL, filename = NULL, test = FALSE) {
# Check that essential inputs are given:
if (is.null(control_id)) {
stop("No control IDs detected")
}
if (is.null(mutant_id)) {
stop("No mutant IDs detected")
}
if (is.null(core_num)) {
cores_detected <- parallel::detectCores()
message("No cores specified")
message("Detected: ", cores_detected, " cores")
message("Using: ", cores_detected/2, " cores")
doMC::registerDoMC(cores_detected/2)
}
if (is.null(output_dir)) {
output_dir <- paste0(getwd(), "/GRETTA_", Sys.Date())
say <- paste0("No output directory specified. Creating: ",
output_dir)
message(say)
dir.create(output_dir)
}
if (!dir.exists(output_dir)) {
stop("Output directory does not exist. Please provide full path to directory.")
}
if (is.null(data_dir)) {
stop("No directory to data was specified. Please provide path to DepMap data.")
}
if (!dir.exists(data_dir)) {
stop("DepMap data directory does not exists. Please check again and provide the full path to the DepMap data directory.")
}
if (!is.null(filename)) {
output_dir_and_filename <- paste0(output_dir,
"/", filename, ".csv")
} else {
output_dir_and_filename <- paste0(output_dir,
"/GRETTA_GI_screen_results.csv")
}
# Set cores:
if (!is.null(core_num)) {
doMC::registerDoMC(core_num)
}
# Check to see enough samples were given:
if (length(control_id) < 2) {
stop("Not enough controls! Provide at least two.")
}
if (length(mutant_id) < 2) {
stop("Not enough mutants! Provide at least two.")
}
# Load necessary data
dep <- dep_annot <- NULL # see: https://support.bioconductor.org/p/24756/
load(paste0(data_dir, "/dep.rda"), envir = environment())
load(paste0(data_dir, "/dep_annot.rda"), envir = environment())
# If a list of genes are provided, check to
# see if they are all available.
if (!is.null(gene_list)) {
if (!all(gene_list %in% dep_annot$GeneNames)) {
# if all there
missing <- gene_list[!gene_list %in% dep_annot$GeneNames]
say <- paste0("The following gene(s) were not found or screened by DepMap. Please remove them and try again. \n ",
paste0(missing, collapse = ", "))
stop(say)
}
}
# Check to see if enough samples were given
# after filtering:
Control_group_avail <- control_id[control_id %in%
dep$DepMap_ID]
Mutant_groups_avail <- mutant_id[mutant_id %in%
dep$DepMap_ID]
if (length(Control_group_avail) < 2) {
say <- paste0("Not enough controls were screened! Only the following control samples were screen: ",
paste0(Control_group_avail, collapse = ", "))
stop(say)
}
if (length(Control_group_avail) < 2) {
say <- paste0("Not enough mutants were screened! Only the following mutant samples were screen: ",
paste0(Mutant_groups_avail, collapse = ", "))
stop(say)
}
# Filter dep probs to only those that are
# used:
select_dep <- dep %>%
tidyr::pivot_longer(cols = tidyselect::matches("\\d"),
names_to = "GeneNameID", values_to = "DepProb") %>%
dplyr::mutate(CellType = case_when(DepMap_ID %in%
Mutant_groups_avail ~ "Mutant", DepMap_ID %in%
Control_group_avail ~ "Control", TRUE ~
"Others")) %>%
dplyr::filter(.data$CellType != "Others") %>%
dplyr::mutate(CellType = forcats::fct_relevel(.data$CellType,
"Control", "Mutant"))
# Filter further if subsetting
if (!is.null(gene_list)) {
select_dep_annot <- dep_annot %>%
filter(.data$GeneNames %in% gene_list)
select_dep <- select_dep %>%
filter(.data$GeneNameID %in% select_dep_annot$GeneNameID)
}
# Need to define function. A fix for a
# strange bug:
`%dopar%` <- foreach::`%dopar%`
# For testing short loops:
if (test == TRUE) {
run <- 3
} else if (test == FALSE) {
run <- length(unique(select_dep$GeneNameID))
}
# Begin loop
All_res <- each <- NULL
All_res <- foreach::foreach(each = seq_len(run),
.combine = bind_rows) %dopar% {
# Give feedback
if (each == 1) {
message("Processing ", each, " of ", length(unique(select_dep$GeneNameID)))
} else if (each == length(unique(select_dep$GeneNameID))) {
message("Processing ", each, " of ", length(unique(select_dep$GeneNameID)))
} else if (each%%1000 == 0) {
message("Processing ", each, " of ", length(unique(select_dep$GeneNameID)))
}
# Get each gene
geneID <- unique(select_dep$GeneNameID)[each]
df <- select_dep %>%
dplyr::filter(.data$GeneNameID == geneID) %>%
dplyr::filter(!is.na(.data$DepProb))
df_post_filter_check <- df %>%
count(.data$CellType)
if (any(df_post_filter_check$n < 2)) {
populate <- rep(NA, 11)
} else if (all(df$DepProb == 0)) {
populate <- rep(0, 11)
} else if (all(df$DepProb == 1)) {
populate <- rep(1, 11)
} else {
# # MWU doesn't handle na or zero's
# well so # FOR NOW remove zeros. df
# <- df %>% filter(!is.na(DepProb))
# %>% filter(DepProb != 0)
stats <- df %>%
dplyr::group_by(.data$CellType) %>%
dplyr::summarize(Median = stats::median(.data$DepProb,
na.rm = TRUE), Mean = mean(.data$DepProb,
na.rm = TRUE), SD = stats::sd(.data$DepProb,
na.rm = TRUE), IQR = stats::IQR(.data$DepProb,
na.rm = TRUE), .groups = "drop")
if ((any(is.na(stats)) != TRUE) & (nrow(stats) ==
2)) {
fit_pval <- stats::wilcox.test(DepProb ~
CellType, df, paired = FALSE, alternative = "two.sided",
conf.int = TRUE, na.action = "na.omit")$p.value
# If group size is < 3 cliffDelta
# will have error: missing value
# where TRUE/FALSE needed
# Important note: because
# celltype has a specific factor
# order specified for select_dep,
# a delta > 0 indicates an effect
# score greater in the Control
# (first level) and a delta < 0
# means an effect score greater
# in the Mutant( second level)
CliffDelta <- rcompanion::cliffDelta(DepProb ~
CellType, df)
# Add diptest for uni/multi
# modality null hypothesis if p >
# 0.05 the data is unimodal
# alternative hyp if p < 0.05 the
# data is multimodal
dip_pval <- df %>%
dplyr::filter(.data$CellType == "Mutant") %>%
dplyr::pull(.data$DepProb) %>%
diptest::dip.test() %>%
broom::tidy() %>%
dplyr::pull(.data$p.value)
} else if ((any(is.na(stats)) == TRUE) &
(nrow(stats) == 2)) {
populate <- rep(0, 11)
}
if ((any(is.na(stats)) != TRUE) & (nrow(stats) ==
2)) {
populate <- as.numeric(c(unlist(stats)[-c(1,
2)], fit_pval, -CliffDelta[[1]],
dip_pval))
} else {
populate <- rep(0, 11)
}
}
tibble(Result = c("Control_median", "Mutant_median",
"Control_mean", "Mutant_mean", "Control_sd",
"Mutant_sd", "Control_iqr", "Mutant_iqr",
"Pval", "CliffDelta", "dip_pval")) %>%
dplyr::mutate(!!sym(geneID) := populate) %>%
tidyr::pivot_longer(-.data$Result) %>%
tidyr::pivot_wider(names_from = .data$Result,
values_from = .data$value) %>%
dplyr::rename(GeneNameID = .data$name)
} # End of for loop
# Add mutant group name
output <- All_res %>%
dplyr::mutate(log2FC_by_median = log2(.data$Mutant_median/.data$Control_median),
log2FC_by_mean = log2(.data$Mutant_mean/.data$Control_mean),
Adj_pval = p.adjust(.data$Pval, method = "BH",
length(.data$Pval)), Interaction_score = -log10(.data$Pval) *
sign(.data$log2FC_by_median)) %>%
dplyr::left_join(dep_annot %>%
select(.data$GeneNameID, .data$GeneNames),
by = "GeneNameID") %>%
dplyr::select(.data$GeneNameID, .data$GeneNames,
.data$Control_median:.data$Pval, .data$Adj_pval,
.data$log2FC_by_median, .data$log2FC_by_mean,
tidyselect::everything(), .data$Interaction_score)
# save and return output
output %>%
readr::write_csv(file = output_dir_and_filename)
message("In-silico genetic interaction screen finished. Outputs were also written to: ",
output_dir_and_filename)
return(output)
}
ytakemon/GINIR documentation built on Feb. 27, 2024, 1:33 p.m.
R Package Documentation
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Defines functions GI_screen
Documented in GI_screen
#' @title Perform genetic interaction screen
#'
#' @description Compares dependency probabilities of mutant and control groups to determine
#' whether the mutant group are resistant or sensitive to specific gene perturbations.
#'
#' @param control_id string, A vector containing two or more DepMap_id, Default: NULL
#' @param mutant_id string, A vector containing two or more DepMap_id, Default: NULL
#' @param gene_list string, A vector containing a list of Hugo symbols to subset the screen and to perform a small in-silico screen, Default: NULL
#' @param core_num integer, Number of cores to run analysis, Default: NULL
#' @param output_dir string, Full path to where output file should be saved, Default: NULL
#' @param data_dir string Path to GRETTA_data
#' @param filename string name of file without the '.csv' extension.
#' @param test logical, For test_that to shorten computational time for testing
#'
#' @return A data frame containing results from the genetic screen. A copy is also saved to the
#' directory defined in `output_dir`.
#'
#' @details Description of output data frame
#' * `GeneName_ID` - Hugo symbol with NCBI gene ID
#' * `GeneNames` - Hugo symbol
#' * `_median`, `_mean`, `_sd`, `_iqr` - Control and mutant group's median, mean, standard deviation (sd),
#' and interquartile range (iqr) of dependency probabilities. Dependency probabilities range from zero to one,
#' where one indicates a essential gene (ie. KO of gene was lethal) and zero indicates a non-essential gene
#' (KO of gene was not lethal)
#' * `Pval` - P-value from Mann Whitney U test between control and mutant groups.
#' * `Adj_pval` - BH-adjusted P-value.
#' * `log2FC_by_median` - Log2 normalized median fold change of dependency probabilities (mutant / control).
#' * `log2FC_by_mean` - Log2 normalized mean fold change of dependency probabilities (mutant / control).
#' * `CliffDelta` - Cliff's delta non-parametric effect size between mutant and control dependency probabilities.
#' Ranges between -1 to 1.
#' * `dip_pval` - Hartigan's dip test p-value. Tests whether distribution of mutant dependency probability is unimodel.
#' If dip test is rejected (p-value < 0.05), this indicates that there is a multimodel dependency probability distribution and
#' that there may be another factor contributing to this separation.
#' * `Interaction_score` - Combined value generated from signed p-values: `-log10(Pval) \* sign(log2FC_by_median)`
#' @md
#'
#' @examples
#' gretta_data_dir <- './GRETTA_example/'
#' gretta_output_dir <- './GRETTA_example_output/'
#'
#' if(!dir.exists(gretta_data_dir)){
#' download_example_data(".")
#' }
#'
#' if(!dir.exists(gretta_data_dir)){
#' download_example_data(".")
#' }
#'
#' Screen_results <- GI_screen(
#' control_id = c('ACH-001354', 'ACH-000274', 'ACH-001799'),
#' mutant_id = c('ACH-000911', 'ACH-001957', 'ACH-000075'),
#' gene_list = c('ARID1A', 'ARID1B', 'SMARCA2'),
#' core_num = 2,
#' output_dir = gretta_output_dir,
#' data_dir = gretta_data_dir)
#'
#' @rdname GI_screen
#' @export
#' @importFrom parallel detectCores
#' @importFrom doMC registerDoMC
#' @importFrom dplyr mutate filter group_by summarize pull rename
#' @importFrom forcats fct_relevel
#' @importFrom foreach `%dopar%` foreach
#' @importFrom rcompanion cliffDelta
#' @importFrom diptest dip.test
#' @importFrom broom tidy
#' @importFrom tidyr pivot_longer pivot_wider
#' @importFrom tidyselect everything
#' @importFrom readr write_csv
#' @importFrom stats median sd IQR wilcox.test p.adjust
GI_screen <- function(control_id = NULL, mutant_id = NULL,
gene_list = NULL, core_num = NULL, output_dir = NULL,
data_dir = NULL, filename = NULL, test = FALSE) {
# Check that essential inputs are given:
if (is.null(control_id)) {
stop("No control IDs detected")
}
if (is.null(mutant_id)) {
stop("No mutant IDs detected")
}
if (is.null(core_num)) {
cores_detected <- parallel::detectCores()
message("No cores specified")
message("Detected: ", cores_detected, " cores")
message("Using: ", cores_detected/2, " cores")
doMC::registerDoMC(cores_detected/2)
}
if (is.null(output_dir)) {
output_dir <- paste0(getwd(), "/GRETTA_", Sys.Date())
say <- paste0("No output directory specified. Creating: ",
output_dir)
message(say)
dir.create(output_dir)
}
if (!dir.exists(output_dir)) {
stop("Output directory does not exist. Please provide full path to directory.")
}
if (is.null(data_dir)) {
stop("No directory to data was specified. Please provide path to DepMap data.")
}
if (!dir.exists(data_dir)) {
stop("DepMap data directory does not exists. Please check again and provide the full path to the DepMap data directory.")
}
if (!is.null(filename)) {
output_dir_and_filename <- paste0(output_dir,
"/", filename, ".csv")
} else {
output_dir_and_filename <- paste0(output_dir,
"/GRETTA_GI_screen_results.csv")
}
# Set cores:
if (!is.null(core_num)) {
doMC::registerDoMC(core_num)
}
# Check to see enough samples were given:
if (length(control_id) < 2) {
stop("Not enough controls! Provide at least two.")
}
if (length(mutant_id) < 2) {
stop("Not enough mutants! Provide at least two.")
}
# Load necessary data
dep <- dep_annot <- NULL # see: https://support.bioconductor.org/p/24756/
load(paste0(data_dir, "/dep.rda"), envir = environment())
load(paste0(data_dir, "/dep_annot.rda"), envir = environment())
# If a list of genes are provided, check to
# see if they are all available.
if (!is.null(gene_list)) {
if (!all(gene_list %in% dep_annot$GeneNames)) {
# if all there
missing <- gene_list[!gene_list %in% dep_annot$GeneNames]
say <- paste0("The following gene(s) were not found or screened by DepMap. Please remove them and try again. \n ",
paste0(missing, collapse = ", "))
stop(say)
}
}
# Check to see if enough samples were given
# after filtering:
Control_group_avail <- control_id[control_id %in%
dep$DepMap_ID]
Mutant_groups_avail <- mutant_id[mutant_id %in%
dep$DepMap_ID]
if (length(Control_group_avail) < 2) {
say <- paste0("Not enough controls were screened! Only the following control samples were screen: ",
paste0(Control_group_avail, collapse = ", "))
stop(say)
}
if (length(Control_group_avail) < 2) {
say <- paste0("Not enough mutants were screened! Only the following mutant samples were screen: ",
paste0(Mutant_groups_avail, collapse = ", "))
stop(say)
}
# Filter dep probs to only those that are
# used:
select_dep <- dep %>%
tidyr::pivot_longer(cols = tidyselect::matches("\\d"),
names_to = "GeneNameID", values_to = "DepProb") %>%
dplyr::mutate(CellType = case_when(DepMap_ID %in%
Mutant_groups_avail ~ "Mutant", DepMap_ID %in%
Control_group_avail ~ "Control", TRUE ~
"Others")) %>%
dplyr::filter(.data$CellType != "Others") %>%
dplyr::mutate(CellType = forcats::fct_relevel(.data$CellType,
"Control", "Mutant"))
# Filter further if subsetting
if (!is.null(gene_list)) {
select_dep_annot <- dep_annot %>%
filter(.data$GeneNames %in% gene_list)
select_dep <- select_dep %>%
filter(.data$GeneNameID %in% select_dep_annot$GeneNameID)
}
# Need to define function. A fix for a
# strange bug:
`%dopar%` <- foreach::`%dopar%`
# For testing short loops:
if (test == TRUE) {
run <- 3
} else if (test == FALSE) {
run <- length(unique(select_dep$GeneNameID))
}
# Begin loop
All_res <- each <- NULL
All_res <- foreach::foreach(each = seq_len(run),
.combine = bind_rows) %dopar% {
# Give feedback
if (each == 1) {
message("Processing ", each, " of ", length(unique(select_dep$GeneNameID)))
} else if (each == length(unique(select_dep$GeneNameID))) {
message("Processing ", each, " of ", length(unique(select_dep$GeneNameID)))
} else if (each%%1000 == 0) {
message("Processing ", each, " of ", length(unique(select_dep$GeneNameID)))
}
# Get each gene
geneID <- unique(select_dep$GeneNameID)[each]
df <- select_dep %>%
dplyr::filter(.data$GeneNameID == geneID) %>%
dplyr::filter(!is.na(.data$DepProb))
df_post_filter_check <- df %>%
count(.data$CellType)
if (any(df_post_filter_check$n < 2)) {
populate <- rep(NA, 11)
} else if (all(df$DepProb == 0)) {
populate <- rep(0, 11)
} else if (all(df$DepProb == 1)) {
populate <- rep(1, 11)
} else {
# # MWU doesn't handle na or zero's
# well so # FOR NOW remove zeros. df
# <- df %>% filter(!is.na(DepProb))
# %>% filter(DepProb != 0)
stats <- df %>%
dplyr::group_by(.data$CellType) %>%
dplyr::summarize(Median = stats::median(.data$DepProb,
na.rm = TRUE), Mean = mean(.data$DepProb,
na.rm = TRUE), SD = stats::sd(.data$DepProb,
na.rm = TRUE), IQR = stats::IQR(.data$DepProb,
na.rm = TRUE), .groups = "drop")
if ((any(is.na(stats)) != TRUE) & (nrow(stats) ==
2)) {
fit_pval <- stats::wilcox.test(DepProb ~
CellType, df, paired = FALSE, alternative = "two.sided",
conf.int = TRUE, na.action = "na.omit")$p.value
# If group size is < 3 cliffDelta
# will have error: missing value
# where TRUE/FALSE needed
# Important note: because
# celltype has a specific factor
# order specified for select_dep,
# a delta > 0 indicates an effect
# score greater in the Control
# (first level) and a delta < 0
# means an effect score greater
# in the Mutant( second level)
CliffDelta <- rcompanion::cliffDelta(DepProb ~
CellType, df)
# Add diptest for uni/multi
# modality null hypothesis if p >
# 0.05 the data is unimodal
# alternative hyp if p < 0.05 the
# data is multimodal
dip_pval <- df %>%
dplyr::filter(.data$CellType == "Mutant") %>%
dplyr::pull(.data$DepProb) %>%
diptest::dip.test() %>%
broom::tidy() %>%
dplyr::pull(.data$p.value)
} else if ((any(is.na(stats)) == TRUE) &
(nrow(stats) == 2)) {
populate <- rep(0, 11)
}
if ((any(is.na(stats)) != TRUE) & (nrow(stats) ==
2)) {
populate <- as.numeric(c(unlist(stats)[-c(1,
2)], fit_pval, -CliffDelta[[1]],
dip_pval))
} else {
populate <- rep(0, 11)
}
}
tibble(Result = c("Control_median", "Mutant_median",
"Control_mean", "Mutant_mean", "Control_sd",
"Mutant_sd", "Control_iqr", "Mutant_iqr",
"Pval", "CliffDelta", "dip_pval")) %>%
dplyr::mutate(!!sym(geneID) := populate) %>%
tidyr::pivot_longer(-.data$Result) %>%
tidyr::pivot_wider(names_from = .data$Result,
values_from = .data$value) %>%
dplyr::rename(GeneNameID = .data$name)
} # End of for loop
# Add mutant group name
output <- All_res %>%
dplyr::mutate(log2FC_by_median = log2(.data$Mutant_median/.data$Control_median),
log2FC_by_mean = log2(.data$Mutant_mean/.data$Control_mean),
Adj_pval = p.adjust(.data$Pval, method = "BH",
length(.data$Pval)), Interaction_score = -log10(.data$Pval) *
sign(.data$log2FC_by_median)) %>%
dplyr::left_join(dep_annot %>%
select(.data$GeneNameID, .data$GeneNames),
by = "GeneNameID") %>%
dplyr::select(.data$GeneNameID, .data$GeneNames,
.data$Control_median:.data$Pval, .data$Adj_pval,
.data$log2FC_by_median, .data$log2FC_by_mean,
tidyselect::everything(), .data$Interaction_score)
# save and return output
output %>%
readr::write_csv(file = output_dir_and_filename)
message("In-silico genetic interaction screen finished. Outputs were also written to: ",
output_dir_and_filename)
return(output)
}
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