From 45279bbacb30f2335089877a018725761e73c25d Mon Sep 17 00:00:00 2001
From: Habib Rehman <harehman@iu.edu>
Date: Mon, 26 Jan 2026 02:18:11 -0500
Subject: [PATCH 1/4] Added SPLIT, tentatively works

---
 .../split_correction/config.vsh.yaml          | 54 +++++++++++
 .../split_correction/script.R                 | 95 +++++++++++++++++++
 2 files changed, 149 insertions(+)
 create mode 100644 src/methods_expression_correction/split_correction/config.vsh.yaml
 create mode 100644 src/methods_expression_correction/split_correction/script.R

diff --git a/src/methods_expression_correction/split_correction/config.vsh.yaml b/src/methods_expression_correction/split_correction/config.vsh.yaml
new file mode 100644
index 00000000..7137954b
--- /dev/null
+++ b/src/methods_expression_correction/split_correction/config.vsh.yaml
@@ -0,0 +1,54 @@
+__merge__: /src/api/comp_method_expression_correction.yaml
+
+name: split_correction
+label: "SPLIT"
+summary: "Correct doublet/misegmented cells using SPLIT"
+description: "SPLIT (Spatial Purification of Layered Intracellular Transcripts) is a novel method that integrates snRNA-seq with RCTD deconvolution to enhance signal purity. SPLIT effectively resolves mixed transcriptomic signals, improving background correction and cell-type resolution."
+links:
+  documentation: "https://github.com/bdsc-tds/SPLIT"
+  repository: "https://github.com/bdsc-tds/SPLIT"
+references:
+  doi: "10.1101/2025.04.23.649965"
+
+# arguments:
+#   - name: --celltype_key
+#     required: false
+#     direction: input
+#     type: string
+#     default: cell_type
+
+resources:
+  - type: r_script
+    path: script.R
+
+engines:
+  - type: docker
+    image: openproblems/base_r:1
+    setup:
+      #- type: docker
+      #  run: |
+      #    apt-get update && apt-get install -y wget
+      - type: r
+        bioc: [anndataR, rhdf5, devtools, scater]
+      #- type: r
+      #  bioc: [SummarizedExperiment,SingleCellExperiment,SpatialExperiment]
+      #  bioc_force_install: true
+      - type: docker
+        run: |
+          Rscript -e "BiocManager::install('SingleCellExperiment', type = 'source', force = TRUE, ask = FALSE); options(timeout = 600000000); devtools::install_github('dmcable/spacexr', build_vignettes = FALSE); devtools::install_github('bdsc-tds/SPLIT')"
+
+      # SingleCellExperiment part can probably be left out again in the future. It currently fixes a bug described in these issues:
+      # https://github.com/drighelli/SpatialExperiment/issues/171
+      # https://github.com/satijalab/seurat/issues/9889
+      # The reinstall of SingleCellExperiment triggers the correct re-install of SpatialExperiment.
+
+      # Is there a better way to install an r package from github?
+      # The 6 million timeout thing stops it from breaking
+
+  - type: native
+
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [ hightime, highcpu, highmem ]
\ No newline at end of file
diff --git a/src/methods_expression_correction/split_correction/script.R b/src/methods_expression_correction/split_correction/script.R
new file mode 100644
index 00000000..8a92c119
--- /dev/null
+++ b/src/methods_expression_correction/split_correction/script.R
@@ -0,0 +1,95 @@
+library(spacexr)
+library(Matrix)
+library(SingleCellExperiment)
+library(anndataR)
+library(SPLIT)
+library(Seurat)
+library(scuttle)
+
+## VIASH START
+par <- list(
+  "input_spatial_with_cell_types" = "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_aggregated_counts.h5ad",
+  "input_scrnaseq_reference"= "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/scrnaseq_reference.h5ad",
+  "output" = "task_ist_preprocessing/tmp/split_corrected.h5ad"
+)
+
+meta <- list(
+  'cpus': 4,
+)
+
+## VIASH END
+
+# Read the input h5ad file and convert to SingleCellExperiment and Seurat
+sce <- read_h5ad(par$input_spatial_with_cell_types, as = "SingleCellExperiment")
+xe <- read_h5ad(par$input_spatial_with_cell_types, as = "Seurat")
+
+# Extract spatial coordinates and counts matrix
+centroid_x <- colData(sce)$centroid_x
+centroid_y <- colData(sce)$centroid_y
+coords <- data.frame(centroid_x, centroid_y)
+counts <- assay(sce,"counts")
+rownames(coords) <- colData(sce)$cell_id
+puck <- SpatialRNA(coords, counts)
+
+# Read reference scrnaseq
+ref <- read_h5ad(par$input_scrnaseq_reference, as = "SingleCellExperiment")
+
+#filter reference cell types to those with >25 cells
+valid_celltypes <- names(table(colData(ref)$cell_type))[table(colData(ref)$cell_type) >= 25] 
+filtered_ref <- ref[,colData(ref)$cell_type %in% valid_celltypes]
+
+ref_counts <- assay(filtered_ref, "counts")
+# factor to drop filtered cell types
+colData(filtered_ref)$cell_type <- factor(colData(filtered_ref)$cell_type) 
+cell_types <- colData(filtered_ref)$cell_type 
+names(cell_types) <- colnames(ref_counts)
+reference <- Reference(ref_counts, cell_types, min_UMI = 0)
+
+# check cores
+cores <- 1
+if ("cpus" %in% names(meta) && !is.null(meta$cpus)) cores <- meta$cpus
+cat(sprintf("Number of cores: %s\n", cores))
+
+# Run the algorithm
+myRCTD <- create.RCTD(puck, reference, max_cores = cores)
+myRCTD <- run.RCTD(myRCTD, doublet_mode = "doublet")
+
+# Extract results
+# results <- myRCTD@results
+# spatial_cell_types <- results$results_df$first_type 
+# # Include None Spatial cell type for the "reject" cells
+# levels(spatial_cell_types) <- c(levels(spatial_cell_types), "None_sp")
+# spatial_cell_types[results$results_df$spot_class == "reject"] <- "None_sp"
+# names(spatial_cell_types) <- rownames(results$results_df)
+
+# #
+# colData(sce)$cell_type <- "None_sp"
+# colData(sce)[names(spatial_cell_types),"cell_type"] <- as.character(spatial_cell_types)
+
+
+# Post-process RCTD output
+RCTD <- SPLIT::run_post_process_RCTD(myRCTD)
+
+# Run SPLIT purification
+res_split <- SPLIT::purify(
+  counts = GetAssayData(xe, assay = 'RNA', layer = 'counts'), # or any gene x cells counts matrix
+  rctd = RCTD,
+  DO_purify_singlets = TRUE # optional
+)
+
+# create corrected counts layer in original SingleCell object
+assay(sce, "corrected_counts") <- assay(sce, "counts")
+assay(sce, "corrected_counts")[rownames(res_split$purified_counts), colnames(res_split$purified_counts)] <- res_split$purified_counts
+
+#log normalized
+size_factors <- librarySizeFactors(assay(sce, "corrected_counts"))
+
+size_factors[size_factors == 0] <- 1 #why
+
+#print(min(size_factors))
+assay(sce, "normalized") <- assay(logNormCounts(sce, size_factors=size_factors, assay.type = "corrected_counts"),"logcounts")
+
+# Write the final object to h5ad format
+# set to 'w', is this ok?
+dir.create(dirname(par$output), showWarnings = FALSE, recursive = TRUE)
+write_h5ad(sce, par$output, mode = "w")

From 6080a68c75c6816e1771338ea86368d4c9b6b70a Mon Sep 17 00:00:00 2001
From: Habib Rehman <harehman@iu.edu>
Date: Mon, 2 Feb 2026 00:23:56 -0500
Subject: [PATCH 2/4] Fixed filtering and container for SPLIT

---
 .../split_correction/config.vsh.yaml          | 13 ++---
 .../split_correction/script.R                 | 51 +++++++++++--------
 2 files changed, 36 insertions(+), 28 deletions(-)

diff --git a/src/methods_expression_correction/split_correction/config.vsh.yaml b/src/methods_expression_correction/split_correction/config.vsh.yaml
index 7137954b..3c865d11 100644
--- a/src/methods_expression_correction/split_correction/config.vsh.yaml
+++ b/src/methods_expression_correction/split_correction/config.vsh.yaml
@@ -10,12 +10,13 @@ links:
 references:
   doi: "10.1101/2025.04.23.649965"
 
-# arguments:
-#   - name: --celltype_key
-#     required: false
-#     direction: input
-#     type: string
-#     default: cell_type
+arguments:
+  - name: --keep_all_cells
+    required: false
+    direction: input
+    type: boolean
+    default: false
+    description: Whether to keep cells with 0 counts (may cause errors if set to TRUE)
 
 resources:
   - type: r_script
diff --git a/src/methods_expression_correction/split_correction/script.R b/src/methods_expression_correction/split_correction/script.R
index 8a92c119..1678d3ec 100644
--- a/src/methods_expression_correction/split_correction/script.R
+++ b/src/methods_expression_correction/split_correction/script.R
@@ -10,7 +10,8 @@ library(scuttle)
 par <- list(
   "input_spatial_with_cell_types" = "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_aggregated_counts.h5ad",
   "input_scrnaseq_reference"= "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/scrnaseq_reference.h5ad",
-  "output" = "task_ist_preprocessing/tmp/split_corrected.h5ad"
+  "output" = "task_ist_preprocessing/tmp/split_corrected.h5ad",
+  "keep_all_cells" = FALSE,
 )
 
 meta <- list(
@@ -23,25 +24,32 @@ meta <- list(
 sce <- read_h5ad(par$input_spatial_with_cell_types, as = "SingleCellExperiment")
 xe <- read_h5ad(par$input_spatial_with_cell_types, as = "Seurat")
 
+# filter out 0 cells
+if (!par$keep_all_cells) {
+  cat("Filtering cells with 0 counts\n")
+  sce <- sce[, colSums(counts(sce)) > 0]
+  xe <- subset(xe, subset = nCount_RNA > 0)
+}
+
 # Extract spatial coordinates and counts matrix
 centroid_x <- colData(sce)$centroid_x
 centroid_y <- colData(sce)$centroid_y
 coords <- data.frame(centroid_x, centroid_y)
-counts <- assay(sce,"counts")
+counts <- assay(sce, "counts")
 rownames(coords) <- colData(sce)$cell_id
 puck <- SpatialRNA(coords, counts)
 
 # Read reference scrnaseq
 ref <- read_h5ad(par$input_scrnaseq_reference, as = "SingleCellExperiment")
 
-#filter reference cell types to those with >25 cells
+#filter reference cell types to those with >25 cells (minimum for RCTD)
 valid_celltypes <- names(table(colData(ref)$cell_type))[table(colData(ref)$cell_type) >= 25] 
 filtered_ref <- ref[,colData(ref)$cell_type %in% valid_celltypes]
 
 ref_counts <- assay(filtered_ref, "counts")
 # factor to drop filtered cell types
-colData(filtered_ref)$cell_type <- factor(colData(filtered_ref)$cell_type) 
-cell_types <- colData(filtered_ref)$cell_type 
+colData(filtered_ref)$cell_type <- factor(colData(filtered_ref)$cell_type)
+cell_types <- colData(filtered_ref)$cell_type
 names(cell_types) <- colnames(ref_counts)
 reference <- Reference(ref_counts, cell_types, min_UMI = 0)
 
@@ -51,45 +59,44 @@ if ("cpus" %in% names(meta) && !is.null(meta$cpus)) cores <- meta$cpus
 cat(sprintf("Number of cores: %s\n", cores))
 
 # Run the algorithm
+cat("Running RCTD\n")
 myRCTD <- create.RCTD(puck, reference, max_cores = cores)
 myRCTD <- run.RCTD(myRCTD, doublet_mode = "doublet")
 
-# Extract results
+# Get the "spot_class" annotation from RCTD
+# cat("Saving RCTD spot_class\n")
 # results <- myRCTD@results
-# spatial_cell_types <- results$results_df$first_type 
-# # Include None Spatial cell type for the "reject" cells
-# levels(spatial_cell_types) <- c(levels(spatial_cell_types), "None_sp")
-# spatial_cell_types[results$results_df$spot_class == "reject"] <- "None_sp"
-# names(spatial_cell_types) <- rownames(results$results_df)
-
-# #
-# colData(sce)$cell_type <- "None_sp"
-# colData(sce)[names(spatial_cell_types),"cell_type"] <- as.character(spatial_cell_types)
-
+# rctd_spot_class <- results$results_df$spot_class
+# names(rctd_spot_class) <- rownames(results$results_df)
+# colData(sce)$RCTD_class <- "not_included"
+# colData(sce)[names(rctd_spot_class),"RCTD_class"] <- as.character(rctd_spot_class)
 
 # Post-process RCTD output
 RCTD <- SPLIT::run_post_process_RCTD(myRCTD)
 
 # Run SPLIT purification
+cat("Running SPLIT\n")
 res_split <- SPLIT::purify(
   counts = GetAssayData(xe, assay = 'RNA', layer = 'counts'), # or any gene x cells counts matrix
   rctd = RCTD,
   DO_purify_singlets = TRUE # optional
 )
 
+
 # create corrected counts layer in original SingleCell object
+cat("Normalizing counts\n")
+
+# First copy in counts
 assay(sce, "corrected_counts") <- assay(sce, "counts")
+
+# Then, replace only the updated cells
 assay(sce, "corrected_counts")[rownames(res_split$purified_counts), colnames(res_split$purified_counts)] <- res_split$purified_counts
 
-#log normalized
+# Library size normalization - see note in resolVI
 size_factors <- librarySizeFactors(assay(sce, "corrected_counts"))
-
-size_factors[size_factors == 0] <- 1 #why
-
-#print(min(size_factors))
 assay(sce, "normalized") <- assay(logNormCounts(sce, size_factors=size_factors, assay.type = "corrected_counts"),"logcounts")
 
 # Write the final object to h5ad format
-# set to 'w', is this ok?
+cat("Writing to h5ad\n")
 dir.create(dirname(par$output), showWarnings = FALSE, recursive = TRUE)
 write_h5ad(sce, par$output, mode = "w")

From 9eb1755abe95a49d06cd5cd044ac0042aed6ab01 Mon Sep 17 00:00:00 2001
From: LouisK92 <louiskuemmerle@googlemail.com>
Date: Mon, 23 Feb 2026 17:41:08 +0100
Subject: [PATCH 3/4] Save uncorrected counts in split script

---
 .../split_correction/config.vsh.yaml          | 14 ++++-------
 .../split_correction/script.R                 | 25 +++++++++++--------
 2 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/src/methods_expression_correction/split_correction/config.vsh.yaml b/src/methods_expression_correction/split_correction/config.vsh.yaml
index 3c865d11..882841fb 100644
--- a/src/methods_expression_correction/split_correction/config.vsh.yaml
+++ b/src/methods_expression_correction/split_correction/config.vsh.yaml
@@ -26,14 +26,11 @@ engines:
   - type: docker
     image: openproblems/base_r:1
     setup:
-      #- type: docker
-      #  run: |
-      #    apt-get update && apt-get install -y wget
+      - type: docker
+        run: |
+          apt-get update
       - type: r
         bioc: [anndataR, rhdf5, devtools, scater]
-      #- type: r
-      #  bioc: [SummarizedExperiment,SingleCellExperiment,SpatialExperiment]
-      #  bioc_force_install: true
       - type: docker
         run: |
           Rscript -e "BiocManager::install('SingleCellExperiment', type = 'source', force = TRUE, ask = FALSE); options(timeout = 600000000); devtools::install_github('dmcable/spacexr', build_vignettes = FALSE); devtools::install_github('bdsc-tds/SPLIT')"
@@ -43,8 +40,7 @@ engines:
       # https://github.com/satijalab/seurat/issues/9889
       # The reinstall of SingleCellExperiment triggers the correct re-install of SpatialExperiment.
 
-      # Is there a better way to install an r package from github?
-      # The 6 million timeout thing stops it from breaking
+      # Using a large timeout here to reduce failures during GitHub package installation.
 
   - type: native
 
@@ -52,4 +48,4 @@ runners:
   - type: executable
   - type: nextflow
     directives:
-      label: [ hightime, highcpu, highmem ]
\ No newline at end of file
+      label: [ hightime, highcpu, highmem ]
diff --git a/src/methods_expression_correction/split_correction/script.R b/src/methods_expression_correction/split_correction/script.R
index 1678d3ec..5210aab1 100644
--- a/src/methods_expression_correction/split_correction/script.R
+++ b/src/methods_expression_correction/split_correction/script.R
@@ -7,12 +7,12 @@ library(Seurat)
 library(scuttle)
 
 ## VIASH START
-par <- list(
-  "input_spatial_with_cell_types" = "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_aggregated_counts.h5ad",
-  "input_scrnaseq_reference"= "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/scrnaseq_reference.h5ad",
-  "output" = "task_ist_preprocessing/tmp/split_corrected.h5ad",
-  "keep_all_cells" = FALSE,
-)
+par <- list(
+  "input_spatial_with_cell_types" = "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_with_celltypes.h5ad",
+  "input_scrnaseq_reference"= "task_ist_preprocessing/resources_test/task_ist_preprocessing/mouse_brain_combined/scrnaseq_reference.h5ad",
+  "output" = "task_ist_preprocessing/tmp/split_corrected.h5ad",
+  "keep_all_cells" = FALSE,
+)
 
 meta <- list(
   'cpus': 4,
@@ -83,11 +83,14 @@ res_split <- SPLIT::purify(
 )
 
 
-# create corrected counts layer in original SingleCell object
-cat("Normalizing counts\n")
-
-# First copy in counts
-assay(sce, "corrected_counts") <- assay(sce, "counts")
+# create corrected counts layer in original SingleCell object
+cat("Normalizing counts\n")
+
+# Preserve original normalized values before overwriting with corrected normalization
+assay(sce, "normalized_uncorrected") <- assay(sce, "normalized")
+
+# First copy in counts
+assay(sce, "corrected_counts") <- assay(sce, "counts")
 
 # Then, replace only the updated cells
 assay(sce, "corrected_counts")[rownames(res_split$purified_counts), colnames(res_split$purified_counts)] <- res_split$purified_counts

From 6f76c6c0588a65fa085bd039170edbd7c66559c1 Mon Sep 17 00:00:00 2001
From: LouisK92 <louiskuemmerle@googlemail.com>
Date: Mon, 23 Feb 2026 17:50:32 +0100
Subject: [PATCH 4/4] Rename split_correction to split and add method to
 workflow and scripts

---
 scripts/run_benchmark/config.yaml                              | 0
 scripts/run_benchmark/run_full_local.sh                        | 1 +
 scripts/run_benchmark/run_full_seqeracloud.sh                  | 1 +
 scripts/run_benchmark/run_test_local.sh                        | 1 +
 scripts/run_benchmark/run_test_seqeracloud.sh                  | 1 +
 .../{split_correction => split}/config.vsh.yaml                | 2 +-
 .../{split_correction => split}/script.R                       | 0
 src/workflows/run_benchmark/config.vsh.yaml                    | 3 ++-
 src/workflows/run_benchmark/main.nf                            | 3 ++-
 9 files changed, 9 insertions(+), 3 deletions(-)
 create mode 100644 scripts/run_benchmark/config.yaml
 rename src/methods_expression_correction/{split_correction => split}/config.vsh.yaml (96%)
 rename src/methods_expression_correction/{split_correction => split}/script.R (100%)

diff --git a/scripts/run_benchmark/config.yaml b/scripts/run_benchmark/config.yaml
new file mode 100644
index 00000000..e69de29b
diff --git a/scripts/run_benchmark/run_full_local.sh b/scripts/run_benchmark/run_full_local.sh
index 1464a6df..2d828361 100755
--- a/scripts/run_benchmark/run_full_local.sh
+++ b/scripts/run_benchmark/run_full_local.sh
@@ -65,6 +65,7 @@ expression_correction_methods:
   - no_correction
   # - gene_efficiency_correction
   # - resolvi_correction
+  # - split
 method_parameters_yaml: /tmp/method_params.yaml
 HERE
 
diff --git a/scripts/run_benchmark/run_full_seqeracloud.sh b/scripts/run_benchmark/run_full_seqeracloud.sh
index d006f7e0..9d930046 100755
--- a/scripts/run_benchmark/run_full_seqeracloud.sh
+++ b/scripts/run_benchmark/run_full_seqeracloud.sh
@@ -57,6 +57,7 @@ expression_correction_methods:
   - no_correction
   - gene_efficiency_correction
   - resolvi_correction
+  - split
 method_parameters_yaml: /tmp/method_params.yaml
 HERE
 
diff --git a/scripts/run_benchmark/run_test_local.sh b/scripts/run_benchmark/run_test_local.sh
index 686326b9..2957d152 100755
--- a/scripts/run_benchmark/run_test_local.sh
+++ b/scripts/run_benchmark/run_test_local.sh
@@ -60,6 +60,7 @@ expression_correction_methods:
   - no_correction
   # - gene_efficiency_correction
   # - resolvi_correction
+  # - split
 method_parameters_yaml: /tmp/method_params.yaml
 HERE
 
diff --git a/scripts/run_benchmark/run_test_seqeracloud.sh b/scripts/run_benchmark/run_test_seqeracloud.sh
index 39ab8c5e..de9a52e5 100755
--- a/scripts/run_benchmark/run_test_seqeracloud.sh
+++ b/scripts/run_benchmark/run_test_seqeracloud.sh
@@ -56,6 +56,7 @@ expression_correction_methods:
   - no_correction
   - gene_efficiency_correction
   - resolvi_correction
+  - split
 #method_parameters_yaml: /tmp/method_params.yaml
 HERE
 
diff --git a/src/methods_expression_correction/split_correction/config.vsh.yaml b/src/methods_expression_correction/split/config.vsh.yaml
similarity index 96%
rename from src/methods_expression_correction/split_correction/config.vsh.yaml
rename to src/methods_expression_correction/split/config.vsh.yaml
index 882841fb..18a3fabb 100644
--- a/src/methods_expression_correction/split_correction/config.vsh.yaml
+++ b/src/methods_expression_correction/split/config.vsh.yaml
@@ -1,6 +1,6 @@
 __merge__: /src/api/comp_method_expression_correction.yaml
 
-name: split_correction
+name: split
 label: "SPLIT"
 summary: "Correct doublet/misegmented cells using SPLIT"
 description: "SPLIT (Spatial Purification of Layered Intracellular Transcripts) is a novel method that integrates snRNA-seq with RCTD deconvolution to enhance signal purity. SPLIT effectively resolves mixed transcriptomic signals, improving background correction and cell-type resolution."
diff --git a/src/methods_expression_correction/split_correction/script.R b/src/methods_expression_correction/split/script.R
similarity index 100%
rename from src/methods_expression_correction/split_correction/script.R
rename to src/methods_expression_correction/split/script.R
diff --git a/src/workflows/run_benchmark/config.vsh.yaml b/src/workflows/run_benchmark/config.vsh.yaml
index c25e39ce..665d540f 100644
--- a/src/workflows/run_benchmark/config.vsh.yaml
+++ b/src/workflows/run_benchmark/config.vsh.yaml
@@ -104,7 +104,7 @@ argument_groups:
           A list of expression correction methods to run.
         type: string
         multiple: true
-        default: "no_correction:gene_efficiency_correction:resolvi_correction"
+        default: "no_correction:gene_efficiency_correction:resolvi_correction:split"
   - name: Method parameters
     description: |
       Use these arguments to control the parameter sets that are run for each
@@ -175,6 +175,7 @@ dependencies:
   - name: methods_expression_correction/no_correction
   - name: methods_expression_correction/gene_efficiency_correction
   - name: methods_expression_correction/resolvi_correction
+  - name: methods_expression_correction/split
   - name: methods_data_aggregation/aggregate_spatial_data
   - name: metrics/similarity
   - name: metrics/quality
diff --git a/src/workflows/run_benchmark/main.nf b/src/workflows/run_benchmark/main.nf
index 6783873d..6ab1f6d3 100644
--- a/src/workflows/run_benchmark/main.nf
+++ b/src/workflows/run_benchmark/main.nf
@@ -414,7 +414,8 @@ workflow run_wf {
   expr_corr_methods = [
     no_correction,
     gene_efficiency_correction,
-    resolvi_correction
+    resolvi_correction,
+    split
   ]
   
   expr_corr_ch = cta_ch