Store the RIRA model names in the seurat object misc slot (#123)

* Store the RIRA model names in the seurat object misc slot * Add test
BimberLab · Dec 19, 2024 · 4de8cb2 · 4de8cb2
1 parent b0564fa
commit 4de8cb2
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Showing 2 changed files with 15 additions and 3 deletions.
diff --git a/R/CellTypist.R b/R/CellTypist.R
@@ -436,8 +436,9 @@ TrainCellTypist <- function(seuratObj, labelField, modelFile, minCellsPerClass =
 #'
 #' @export
 Classify_TNK <- function(seuratObj, assayName = Seurat::DefaultAssay(seuratObj), columnPrefix = 'RIRA_TNK_v2.', maxAllowableClasses = 6, minFractionToInclude = 0.01, minCellsToRun = 200, maxBatchSize = 600000, retainProbabilityMatrix = FALSE) {
+  modelName <- "RIRA_TNK_v2"
   seuratObj <- RunCellTypist(seuratObj = seuratObj,
-         modelName = "RIRA_TNK_v2",
+         modelName = modelName,
          # These are optimized for this model:
          pThreshold = 0.5, minProp = 0, useMajorityVoting = FALSE, mode = "prob_match",
 
@@ -455,6 +456,8 @@ Classify_TNK <- function(seuratObj, assayName = Seurat::DefaultAssay(seuratObj),
     seuratObj$RIRA_TNK_v2.cellclass[seuratObj$RIRA_Immune_v2.majority_voting != 'T_NK'] <- 'Other'
   }
 
+  seuratObj@misc$RIRA_TNK_Model <- modelName
+
   return(seuratObj)
 }
 
@@ -473,8 +476,9 @@ Classify_TNK <- function(seuratObj, assayName = Seurat::DefaultAssay(seuratObj),
 #'
 #' @export
 Classify_Myeloid <- function(seuratObj, assayName = Seurat::DefaultAssay(seuratObj), columnPrefix = 'RIRA_Myeloid_v3.', maxAllowableClasses = 6, minFractionToInclude = 0.01, minCellsToRun = 200, maxBatchSize = 600000, retainProbabilityMatrix = FALSE) {
+  modelName <- "RIRA_FineScope_Myeloid_v3"
   seuratObj <- RunCellTypist(seuratObj = seuratObj,
-                       modelName = "RIRA_FineScope_Myeloid_v3",
+                       modelName = modelName,
                        # These are optimized for this model:
                        pThreshold = 0.5, minProp = 0, useMajorityVoting = FALSE, mode = "prob_match",
 
@@ -498,6 +502,8 @@ Classify_Myeloid <- function(seuratObj, assayName = Seurat::DefaultAssay(seuratO
   vect[seuratObj@meta.data[[fn2]] %in% c('DC', 'Mature DC')] <- 'DC'
   seuratObj[[fn]] <- as.factor(vect)
 
+  seuratObj@misc$RIRA_Myeloid_Model <- modelName
+
   return(seuratObj)
 }
 
@@ -517,6 +523,7 @@ Classify_Myeloid <- function(seuratObj, assayName = Seurat::DefaultAssay(seuratO
 #'
 #' @export
 Classify_ImmuneCells <- function(seuratObj, assayName = Seurat::DefaultAssay(seuratObj), columnPrefix = 'RIRA_Immune_v2.', maxAllowableClasses = 6, minFractionToInclude = 0.01, minCellsToRun = 200, maxBatchSize = 600000, retainProbabilityMatrix = FALSE, filterDisallowedClasses = TRUE) {
+  modelName <- 'RIRA_Immune_v2'
   if ('RIRA_Immune_v1.cellclass' %in% names(seuratObj@meta.data)) {
     print('Dropping legacy RIRA_Immune_v1 columns')
     toDrop <- grep(names(seuratObj@meta.data), pattern = 'RIRA_Immune_v1', value = TRUE)
@@ -526,7 +533,7 @@ Classify_ImmuneCells <- function(seuratObj, assayName = Seurat::DefaultAssay(seu
   }
 
   seuratObj <- RunCellTypist(seuratObj = seuratObj,
-                       modelName = 'RIRA_Immune_v2',
+                       modelName = modelName,
 
                        # These are optimized for this model:
                        minProp = 0.5, useMajorityVoting = TRUE, mode = "prob_match",
@@ -569,6 +576,8 @@ Classify_ImmuneCells <- function(seuratObj, assayName = Seurat::DefaultAssay(seu
       )
   )
 
+  seuratObj@misc$RIRA_Immune_Model <- modelName
+
   return(seuratObj)
 }
 

diff --git a/tests/testthat/test-celltypist.R b/tests/testthat/test-celltypist.R
@@ -80,6 +80,7 @@ test_that("celltypist runs for RIRA models", {
   seuratObj <- Classify_TNK(seuratObj, retainProbabilityMatrix = TRUE)
   print(table(seuratObj$RIRA_TNK_v2.cellclass))
 
+  expect_equal('RIRA_TNK_v2', seuratObj@misc$RIRA_TNK_Model)
   expect_equal(4, length(unique(seuratObj$RIRA_TNK_v2.cellclass)), info = 'using RIRA T_NK', tolerance = 1)
   expect_equal(221, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['CD4+ T Cells']), tolerance = 1)
   expect_equal(1028, unname(table(seuratObj$RIRA_TNK_v2.cellclass)['CD8+ T Cells']), tolerance = 1)
@@ -93,6 +94,7 @@ test_that("celltypist runs for RIRA models", {
   print(table(seuratObj$RIRA_Myeloid_v3.cellclass))
   print(table(seuratObj$RIRA_Myeloid_v3.coarseclass))
 
+  expect_equal('RIRA_FineScope_Myeloid_v3', seuratObj@misc$RIRA_Myeloid_Model)
   expect_equal(5, length(unique(seuratObj$RIRA_Myeloid_v3.cellclass)), info = 'using RIRA Myeloid')
   expect_equal(32, unname(table(seuratObj$RIRA_Myeloid_v3.cellclass)['DC']), tolerance = 1)
   expect_equal(32, unname(table(seuratObj$RIRA_Myeloid_v3.coarseclass)['DC']), tolerance = 1)
@@ -108,6 +110,7 @@ test_that("FilterDisallowedClasses works as expected", {
   print('RIRA_Immune_v2.cellclass:')
   print(table(seuratObj$RIRA_Immune_v2.cellclass))
 
+  expect_equal('RIRA_Immune_v2', seuratObj@misc$RIRA_Immune_Model)
   expect_equal(256, sum(seuratObj$RIRA_Immune_v2.cellclass == 'Bcell', na.rm = T), tolerance = 1)
   expect_equal(571, sum(seuratObj$RIRA_Immune_v2.cellclass == 'Myeloid', na.rm = T))
   expect_equal(1303, sum(seuratObj$RIRA_Immune_v2.cellclass == 'T_NK', na.rm = T))