update scFeartures script

src/metrics/ks_statistic_sc_features/script.R

@@ -8,11 +8,28 @@ requireNamespace("CARD", quietly = TRUE)
 requireNamespace("SingleCellExperiment", quietly = TRUE)
 requireNamespace("SummarizedExperiment", quietly = TRUE)
 
+<<<<<<< Updated upstream
 ## VIASH START
 par <- list(
   input_spatial_dataset = "resources_test/spatialsimbench_mobnew/dataset_sp.h5ad",
   input_singlecell_dataset = "resources_test/spatialsimbench_mobnew/dataset_sc.h5ad",
   input_simulated_dataset = "resources_test/spatialsimbench_mobnew/simulated_dataset.h5ad",
+=======
+packages <- c("SingleCellExperiment", "SummarizedExperiment", "concaveman", "sp", 
+              "Matrix", "methods", "ggplot2", "ggcorrplot", "MuSiC", "fields", 
+              "MCMCpack", "dplyr", "sf", "RANN", "stats", "reshape2", "RColorBrewer", 
+              "scatterpie", "grDevices", "nnls", "pbmcapply", "spatstat", "gtools", 
+              "RcppML", "NMF")
+
+# Load each package
+lapply(packages, library, character.only = TRUE)
+
+## VIASH START
+par <- list(
+  input_spatial_dataset = "temp_pdac.positive.ks_statistic_sc_features/_viash_par/input_spatial_dataset_1/output_sp.h5ad",
+  input_singlecell_dataset = "temp_pdac.positive.ks_statistic_sc_features/_viash_par/input_singlecell_dataset_1/output_sc.h5ad",
+  input_simulated_dataset = "temp_pdac.positive.ks_statistic_sc_features/_viash_par/input_simulated_dataset_1/pdac.positive.generate_sim_spatialcluster.output_sp.h5ad",
+>>>>>>> Stashed changes
   output = "output.h5ad"
 )
 meta <- list(
@@ -31,11 +48,26 @@ real_log_count <- t(input_real_sp$layers[["logcounts"]])
 real_prob_matrix <- input_real_sp$obsm[["celltype_proportions"]]
 colnames(real_prob_matrix) <- paste0("ct", seq_len(ncol(real_prob_matrix)))
 
+<<<<<<< Updated upstream
 sim_log_count <- scuttle::normalizeCounts(t(input_simulated_sp$layers[["counts"]]))
+=======
+real_log_count <- real_log_count[  , colnames(real_log_count)  %in% rownames(real_prob_matrix) ]
 
-# build cell type deconvolution in simulated data
+
+sim_sce <- scater::logNormCounts(SingleCellExperiment::SingleCellExperiment(
+  list(counts = Matrix::t(input_simulated_sp$layers[["counts"]])),
+  colData = input_simulated_sp$obs,
+  metadata = input_simulated_sp$obsm
+))
+
+sim_log_count <- SummarizedExperiment::assay(sim_sce, "logcounts")
+>>>>>>> Stashed changes
+
+# build cell type deconvolution in simulated data ## error
 sim_prob_matrix <- CARD_processing(input_real_sp, input_sc)
 
+sim_log_count <- sim_log_count[  , colnames(sim_log_count)  %in% rownames(sim_prob_matrix) ]
+
 feat_types <- c("L_stats", "celltype_interaction", "nn_correlation", "morans_I")
 
 real_scfeatures_result <- scFeatures::scFeatures(real_log_count,
@@ -48,13 +80,23 @@ real_scfeatures_result <- scFeatures::scFeatures(real_log_count,
 )
 
 sim_scfeatures_result <- scFeatures::scFeatures(sim_log_count,
+<<<<<<< Updated upstream
   sample = rep("sample1", ncol(sim_log_count)),
   spatialCoords = input_simulated_sp$obs[c("row", "col")],
   feature_types = feat_types,
   type = "spatial_t",
   species = sc_species,
   spotProbability =  t(sim_prob_matrix)
 )
+=======
+                                                sample = rep("sample1", ncol(sim_log_count)),
+                                                spatialCoords = list( as.numeric( unlist( lapply( strsplit( colnames(sim_log_count) , "x"), `[`, 1) ) )    ,
+                                                                      as.numeric( unlist( lapply( strsplit( colnames(sim_log_count) , "x"), `[`, 2) ) )  )  ,
+                                                feature_types = feat_types,
+                                                type = "spatial_t",
+                                                species = sc_species,
+                                                spotProbability =  t(sim_prob_matrix) )
+>>>>>>> Stashed changes
 
 ks_statistic_L_stats <- ks::kde.test(x1 = as.numeric(real_scfeatures_result$L_stats), x2 = as.numeric(sim_scfeatures_result$L_stats))
 ks_statistic_celltype_interaction <- ks::kde.test(x1 = as.numeric(real_scfeatures_result$celltype_interaction), x2 = as.numeric(sim_scfeatures_result$celltype_interaction))