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Automatically add features to scale.data in ScoreUsingSavedComponent #146

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Merged
bbimber merged 2 commits into from
Dec 29, 2025
Merged

Automatically add features to scale.data in ScoreUsingSavedComponent #146

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70f5772
Automatically add features to scale.data in ScoreUsingSavedComponent
bbimber Dec 29, 2025
4653618
Add testing
bbimber Dec 29, 2025
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26 changes: 24 additions & 2 deletions R/ComponentScores.R
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,30 @@ ScoreUsingSavedComponent <- function(seuratObj, componentOrName, fieldName, assa
names(geneWeights) <- savedComponent$feature

ad <- Seurat::GetAssayData(seuratObj, assay = assayName, layer = layer)
toDrop <- names(geneWeights)[!names(geneWeights) %in% rownames(ad)]

# Perform the check first for scale.data, since we can add missing genes:
if (length(toDrop) > 0 && layer == 'scale.data') {
dataLayer <- Seurat::GetAssayData(seuratObj, assay = assayName, layer = 'data')
toScale <- toDrop[toDrop %in% rownames(dataLayer)]
if (length(toScale) > 0) {
print(paste0('The following ', length(toScale), ' genes are missing from scale.data and will be added now: ', paste0(toScale, collapse = ',')))
scaled <- Seurat::ScaleData(
dataLayer,
features = toScale,
do.scale = TRUE,
do.center = TRUE
)

if (nrow(scaled) != length(toScale)) {
stop('The number of rows after scaling did not match the expected number')
}

ad <- rbind(ad, scaled)
seuratObj <- suppressWarnings(Seurat::SetAssayData(seuratObj, assay = assayName, layer = layer, new.data = ad))
}
}

toDrop <- names(geneWeights)[!names(geneWeights) %in% rownames(ad)]
if (length(toDrop) > 0) {
print(paste0('The following ', length(toDrop), ' genes were in the component but not the assay, skipping: ', paste0(toDrop, collapse = ',')))
Expand All @@ -47,8 +71,6 @@ ScoreUsingSavedComponent <- function(seuratObj, componentOrName, fieldName, assa

if (length(names(seuratObj@reductions)) > 0) {
suppressMessages(print(FeaturePlot(seuratObj, features = fieldName, order = T) & ggplot2::scale_colour_gradientn(colours = c("navy", "dodgerblue", "gold", "red"))))
} else {
print('No reductions present, cannot plot')
}

graphics::hist(seuratObj@meta.data[[fieldName]], breaks = 300, main = fieldName)
Expand Down
17 changes: 13 additions & 4 deletions tests/testthat/test-classification.R
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Expand Up @@ -5,7 +5,7 @@ testthat::context("Classification")

source('installSeuratData.R')

SimulateSeuratData <- function() {
SimulateSeuratDataForTcrPrediction <- function() {
set.seed(GetSeed())

#union features from all activation component files
Expand Down Expand Up @@ -52,7 +52,9 @@ SimulateSeuratData <- function() {
))

seuratObj <- Seurat::NormalizeData(seuratObj, verbose = FALSE)
seuratObj <- Seurat::ScaleData(seuratObj, features = features, verbose = FALSE)

# NOTE: only scale a subset of genes, so we can test whether PredictTcrActivation automatically adds others:
seuratObj <- Seurat::ScaleData(seuratObj, features = features[1:10], verbose = FALSE)
return(seuratObj)
}

Expand Down Expand Up @@ -141,8 +143,15 @@ test_that("Cell type classification works", {

test_that("Predict TCell Activation works ", {

SimulateSeuratObj <- SimulateSeuratData()
SimulateSeuratObj <- SimulateSeuratDataForTcrPrediction()
testthat::expect_equal(2995, nrow(Seurat::GetAssayData(SimulateSeuratObj, assay = 'RNA', layer = 'data')))
testthat::expect_equal(10, nrow(Seurat::GetAssayData(SimulateSeuratObj, assay = 'RNA', layer = 'scale.data')))
SimulateSeuratObj <- PredictTcellActivation(SimulateSeuratObj)

# This tests whether features were added to scale.data:
testthat::expect_equal(2995, nrow(Seurat::GetAssayData(SimulateSeuratObj, assay = 'RNA', layer = 'data')))
testthat::expect_equal(2874, nrow(Seurat::GetAssayData(SimulateSeuratObj, assay = 'RNA', layer = 'scale.data')))

SimulateSeuratObj <- PredictTcellActivation(SimulateSeuratObj, modelName = 'CD4')
SimulateSeuratObj <- PredictTcellActivation(SimulateSeuratObj, modelName = 'CD8')

Expand Down Expand Up @@ -190,7 +199,7 @@ test_that("Predict TCell Activation works ", {


test_that("CombineTcellActivationClasses combines classes and writes versioned columns", {
seuratObj <- SimulateSeuratData()
seuratObj <- SimulateSeuratDataForTcrPrediction()
seuratObj <- PredictTcellActivation(seuratObj, combineClasses = FALSE)

classMapping <- list(
Expand Down