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Merged
bbimber merged 1 commit into from
Dec 18, 2025
Merged

Switch all uses of slot to layer #144

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22 changes: 16 additions & 6 deletions R/Classification.R
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ utils::globalVariables(
#' @title Creates a binary classifier to classify cells within a Seurat object
#'
#' @description Creates a binary classifier to classify cells
#' @param training_matrix A counts or data slot provided by TrainModelsFromSeurat
#' @param training_matrix A matrix (counts or data layer) provided by TrainModelsFromSeurat
#' @param celltype The celltype (provided by TrainModelsFromSeurat) used as classifier's positive prediction
#' @param hyperparameter_tuning logical that determines whether or not hyperparameter tuning should be performed.
#' @param learner The mlr3 learner that should be used. Currently fixed to "classif.ranger" if hyperparameter tuning is FALSE. Otherwise, "classif.xgboost" and "classif.ranger" are supported.
Expand Down Expand Up @@ -161,7 +161,7 @@ TrainModel <- function(training_matrix, celltype, hyperparameter_tuning = F, lea
#' @param seuratObj The Seurat Object to be updated
#' @param celltype_column The metadata column containing the celltypes. One classifier will be created for each celltype present in this column.
#' @param assay SeuratObj assay containing the desired count matrix/metadata
#' @param slot Slot containing the count data. Should be restricted to counts, data, or scale.data.
#' @param layer Layer containing the count data. Should be restricted to counts, data, or scale.data.
#' @param output_dir The directory in which models, metrics, and training data will be saved.
#' @param hyperparameter_tuning Logical that determines whether or not hyperparameter tuning should be performed.
#' @param learner The mlr3 learner that should be used. Currently fixed to "classif.ranger" if hyperparameter tuning is FALSE. Otherwise, "classif.xgboost" and "classif.ranger" are supported.
Expand All @@ -178,7 +178,7 @@ TrainModel <- function(training_matrix, celltype, hyperparameter_tuning = F, lea
#' @param verbose Whether or not to print the metrics data for each model after training.
#' @param min_cells_per_class If provided, any classes (and corresponding cells) with fewer than this many cells will be dropped from the training data
#' @export
TrainModelsFromSeurat <- function(seuratObj, celltype_column, assay = "RNA", slot = "data", output_dir = "./classifiers", hyperparameter_tuning = F, learner = "classif.ranger", inner_resampling = "cv", outer_resampling = "cv", inner_folds = 4, inner_ratio = 0.8, outer_folds = 3, outer_ratio = 0.8, n_models = 20, n_cores = NULL, gene_list = NULL, gene_exclusion_list = NULL, verbose = TRUE, min_cells_per_class = 20){
TrainModelsFromSeurat <- function(seuratObj, celltype_column, assay = "RNA", layer = "data", output_dir = "./classifiers", hyperparameter_tuning = F, learner = "classif.ranger", inner_resampling = "cv", outer_resampling = "cv", inner_folds = 4, inner_ratio = 0.8, outer_folds = 3, outer_ratio = 0.8, n_models = 20, n_cores = NULL, gene_list = NULL, gene_exclusion_list = NULL, verbose = TRUE, min_cells_per_class = 20){
if (methods::missingArg(celltype_column)) {
stop('Must provide the celltype_column argument')
}
Expand All @@ -196,7 +196,7 @@ TrainModelsFromSeurat <- function(seuratObj, celltype_column, assay = "RNA", slo
}

#Read the raw data from a seurat object and parse into an mlr3-compatible labeled matrix
raw_data_matrix <- attr(x = seuratObj@assays[[assay]], which = slot)
raw_data_matrix <- attr(x = seuratObj@assays[[assay]], which = layer)
if (!all(is.null(gene_list))) {
gene_list <- ExpandGeneList(gene_list)
if (!all(gene_list %in% rownames(raw_data_matrix))) {
Expand Down Expand Up @@ -352,7 +352,7 @@ ScoreCellsWithSavedModel <- function(seuratObj, model, fieldToClass, batchSize =
classifier <- .ResolveModel(modelFile = model)

#De-sparse and transpose seuratObj normalized data & make names unique
gene_expression_matrix <- Matrix::t(Seurat::GetAssayData(seuratObj, assay = assayName, slot = "data"))
gene_expression_matrix <- Matrix::t(Seurat::GetAssayData(seuratObj, assay = assayName, layer = "data"))

# NOTE: makeNames() will convert hyphen to period, and also prefix genes with numeric starts, like 7SK.2 -> X7SK.2
colnames(gene_expression_matrix) <- make.names(colnames(gene_expression_matrix))
Expand Down Expand Up @@ -572,11 +572,12 @@ InterpretModels <- function(output_dir= "./classifiers", plot_type = "ratio"){
#' @param seuratObj The Seurat Object to be updated
#' @param model The trained sPLSDA model to use for prediction. This can be a file path to an RDS file, or a built-in model name.
#' @param modelList A list of trained sPLSDA models to use for prediction. This can be a list of file paths to RDS files, or built-in model names.
#' @param removePreexistingColumns If true, any columns
#' @import nnet
#' @return A Seurat object with the sPLSDA scores and predicted probabilities added to the metadata
#' @export

PredictTcellActivation <- function(seuratObj, model = NULL, modelList = NULL) {
PredictTcellActivation <- function(seuratObj, model = NULL, modelList = NULL, removePreexistingColumns = TRUE) {
#################
### Sanitize ###
#################
Expand Down Expand Up @@ -638,6 +639,15 @@ PredictTcellActivation <- function(seuratObj, model = NULL, modelList = NULL) {
#check if component scores already exist in metadata and score if missing or contain NAs
metadataNames <- colnames(seuratObj@meta.data)
for (modelName in names(modelList)) {
if (removePreexistingColumns) {
toDrop <- grep(names(seuratObj@meta.data), pattern = paste0(modelName, "_Activation_sPLSDA_Score_"), value = TRUE)
if (length(toDrop) > 0) {
print(paste0('Dropping pre-existing columns: ', paste0(toDrop, collapse = ',')))
for (colName in toDrop) {
seuratObj[[toDrop]] <- NULL
}
}
}
#determine number of components for this model
nComponents <- .GetModelComponentCount(modelName)
modelVersion <- modelVersions[[modelName]]
Expand Down
4 changes: 2 additions & 2 deletions R/GeneModules.R
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Expand Up @@ -111,7 +111,7 @@ PlotUcellCorrelation <- function(seuratObj, toCalculate, assayName = 'RNA') {
# Drop any genes with all zeros
genesToSkip <- NULL
for (gene in geneList) {
if (sum(Seurat::GetAssayData(seuratObj, assay = assayName, slot = 'data')[gene,] > 0) == 0) {
if (sum(Seurat::GetAssayData(seuratObj, assay = assayName, layer = 'data')[gene,] > 0) == 0) {
genesToSkip <- c(genesToSkip, gene)
}
}
Expand All @@ -126,7 +126,7 @@ PlotUcellCorrelation <- function(seuratObj, toCalculate, assayName = 'RNA') {
next
}

geneData <- as.data.frame(t(as.matrix(Seurat::GetAssayData(seuratObj, assay = assayName, slot = 'data')[geneList,, drop = FALSE])))
geneData <- as.data.frame(t(as.matrix(Seurat::GetAssayData(seuratObj, assay = assayName, layer = 'data')[geneList,, drop = FALSE])))
if (! paste0(moduleName, '_UCell') %in% names(seuratObj@meta.data)) {
stop(paste0('Missing column: ', paste0(moduleName, '_UCell')))
}
Expand Down
6 changes: 3 additions & 3 deletions R/Utils.R
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Expand Up @@ -22,12 +22,12 @@ GetSeed <- function() {
return(pkg.env$RANDOM_SEED)
}

SeuratToMatrix <- function(seuratObj, outDir, assayName, slot = 'counts'){
SeuratToMatrix <- function(seuratObj, outDir, assayName, layer = 'counts'){
if (endsWith(outDir, "/")){
outDir <- gsub(outDir, pattern = "/$", replacement = "")
}

DropletUtils::write10xCounts(x = Seurat::GetAssayData(seuratObj, assay = assayName, slot = slot), path = outDir, overwrite = TRUE, type = 'sparse')
DropletUtils::write10xCounts(x = Seurat::GetAssayData(seuratObj, assay = assayName, layer = layer), path = outDir, overwrite = TRUE, type = 'sparse')

return(paste0(outDir, '/matrix.mtx'))
}
Expand Down Expand Up @@ -57,7 +57,7 @@ SeuratToMatrix <- function(seuratObj, outDir, assayName, slot = 'counts'){

assayObj <- Seurat::GetAssay(seuratObj, assay = assayName)
if (class(assayObj)[1] == 'Assay') {
return(!identical(Seurat::GetAssayData(seuratObj, assay = assayName, slot = 'counts'), Seurat::GetAssayData(seuratObj, assay = assayName, slot = 'data')))
return(!identical(Seurat::GetAssayData(seuratObj, assay = assayName, layer = 'counts'), Seurat::GetAssayData(seuratObj, assay = assayName, layer = 'data')))
} else if (class(assayObj)[1] == 'Assay5') {
return('data' %in% names(assayObj@layers))
} else {
Expand Down
9 changes: 8 additions & 1 deletion man/PredictTcellActivation.Rd
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2 changes: 1 addition & 1 deletion man/TrainModel.Rd
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4 changes: 2 additions & 2 deletions man/TrainModelsFromSeurat.Rd
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