{
"cells": [
{
"cell_type": "markdown",
"id": "15e71d29",
"metadata": {},
"source": [
"# Consistency comparison of `MCube`, `CELINA`, and `C-SIDE` on the CRC datasets from different technologies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "833e8ca8-b82a-46ca-9df1-3c7c0723dad8",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"set.seed(20250502)\n",
"\n",
"library(Matrix)\n",
"library(spacexr)\n",
"library(MCube)\n",
"library(CELINA)\n",
"library(ggplot2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "82892b33",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"RAW_DATA_PATH <- \"/import/home/share/zw/data/CRC\"\n",
"DATA_PATH <- \"/import/home/share/zw/pql/data/CRC\"\n",
"RESULT_PATH <- \"/import/home/share/zw/pql/results/CRC\"\n",
"\n",
"if (!dir.exists(file.path(RESULT_PATH, \"Visium\"))) {\n",
" dir.create(file.path(RESULT_PATH, \"Visium\"), recursive = TRUE)\n",
"}\n",
"if (!dir.exists(file.path(RESULT_PATH, \"VisiumHD\"))) {\n",
" dir.create(file.path(RESULT_PATH, \"VisiumHD\"), recursive = TRUE)\n",
"}\n",
"if (!dir.exists(file.path(RESULT_PATH, \"comparison\"))) {\n",
" dir.create(file.path(RESULT_PATH, \"comparison\"), recursive = TRUE)\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "335bed19",
"metadata": {},
"source": [
"## Load in scRNA-seq reference data"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "f8919e3a",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: SeuratObject\n",
"\n",
"Loading required package: sp\n",
"\n",
"\n",
"Attaching package: ‘SeuratObject’\n",
"\n",
"\n",
"The following objects are masked from ‘package:base’:\n",
"\n",
" intersect, t\n",
"\n",
"\n",
"Warning message in check_UMI(nUMI, \"Reference\", require_2d = T, require_int = require_int, :\n",
"“Reference: some nUMI values are less than min_UMI = 100, and these cells will be removed. Optionally, you may lower the min_UMI parameter.”\n",
"Warning message in Reference(FlexRef, CTRef, colSums(FlexRef)):\n",
"“Reference: number of cells per cell type is 34844, larger than maximum allowable of 10000. Downsampling number of cells to: 10000”\n"
]
}
],
"source": [
"library(Seurat)\n",
"\n",
"FlexRef <- Read10X_h5(file.path(\n",
" RAW_DATA_PATH, \"sc\", \"HumanColonCancer_Flex_Multiplex_count_filtered_feature_bc_matrix.h5\"\n",
"))\n",
"# MetaData <- readRDS(file.path(\n",
"# RAW_DATA_PATH, \"sc\", \"FlexSeuratV5_MetaData.rds\"\n",
"# )) # See FlexSingleCell.R if not generated.\n",
"\n",
"meta <- read.csv(file.path(\n",
" RAW_DATA_PATH, \"HumanColonCancer_VisiumHD/MetaData/SingleCell_MetaData.csv.gz\"\n",
"))\n",
"\n",
"KpIdents <- names(which(table(meta$Level2) > 25))\n",
"meta <- meta[meta$Level2 %in% KpIdents, ]\n",
"FlexRef <- FlexRef[, meta$Barcode]\n",
"\n",
"CTRef <- meta$Level2\n",
"CTRef <- gsub(\"/\", \"_\", CTRef)\n",
"CTRef <- as.factor(CTRef)\n",
"names(CTRef) <- meta$Barcode\n",
"\n",
"reference <- Reference(FlexRef, CTRef, colSums(FlexRef))"
]
},
{
"cell_type": "markdown",
"id": "3075da8e",
"metadata": {},
"source": [
"We then compare the statistical power of `MCube` and `CELINA` on ST CRC datasets.\n",
"We focus on the cancer associated fibroblasts (CAFs) and macrophages to study the tumor microenvironment (TME).\n",
"In the tumor boundary region, CAFs are the most prominent cell type, while macrophages are consistently identified as the most abundant immune cell type."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "88517a92",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"celltypes_tme <- c(\"CAF\", \"Macrophage\")"
]
},
{
"cell_type": "markdown",
"id": "083de5f8",
"metadata": {},
"source": [
"Since there is no ground truth on cell-type-specific SVGs, we used the identified SVGs in the paired Visium data for verification."
]
},
{
"cell_type": "markdown",
"id": "d31d968a",
"metadata": {},
"source": [
"## 10x Visium"
]
},
{
"cell_type": "markdown",
"id": "64734933",
"metadata": {},
"source": [
"### Cell type deconvolution using `RCTD`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af4665dc",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"# counts <- Read10X_h5(file.path(\n",
"# RAW_DATA_PATH, \"visium\",\n",
"# \"Visium_V2_Human_Colon_Cancer_P2_filtered_feature_bc_matrix.h5\"\n",
"# ))\n",
"\n",
"# coordinates <- as.data.frame(readr::read_csv(file.path(\n",
"# RAW_DATA_PATH, \"visium\",\n",
"# \"spatial/tissue_positions.csv\"\n",
"# )))\n",
"# rownames(coordinates)<-coordinates$barcode\n",
"# coordinates<-coordinates[colnames(counts), ]\n",
"# coordinates <- coordinates[, c(6, 5)]\n",
"\n",
"# puck <- SpatialRNA(coordinates, counts, colSums(counts))\n",
"\n",
"# myRCTD <- create.RCTD(puck, reference, max_cores = 32)\n",
"# myRCTD <- run.RCTD(myRCTD, doublet_mode = \"full\")\n",
"# saveRDS(myRCTD, file.path(RESULT_PATH, \"Visium\", \"myRCTD.rds\"))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "412a6cec",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"myRCTD <- readRDS(file.path(RESULT_PATH, \"Visium\", \"myRCTD.rds\"))"
]
},
{
"cell_type": "markdown",
"id": "4c78e14e",
"metadata": {},
"source": [
"### Cell-type-specific SVG identification using `MCube`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2596cf05",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"spot_names <- colnames(myRCTD@spatialRNA@counts)\n",
"weights_RCTD <- as.matrix(myRCTD@results$weights)\n",
"proportions_RCTD <- weights_RCTD / rowSums(weights_RCTD)\n",
"spot_effects_RCTD <- log(rowSums(weights_RCTD))\n",
"names(spot_effects_RCTD) <- rownames(weights_RCTD)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9d21c6f",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"mcube_object <- createMCube(\n",
" counts = t(as.matrix(myRCTD@originalSpatialRNA@counts[, spot_names])),\n",
" coordinates = as.matrix(myRCTD@spatialRNA@coords),\n",
" proportions = proportions_RCTD,\n",
" library_sizes = myRCTD@spatialRNA@nUMI,\n",
" reference = t(myRCTD@cell_type_info$info[[1]]),\n",
" used_for_deconvolution = rownames(myRCTD@spatialRNA@counts),\n",
" spot_effects = spot_effects_RCTD,\n",
" celltype_test = celltypes_tme,\n",
" celltype_threshold = 50\n",
")\n",
"mcube_object <- mcubeFitNull(\n",
" mcube_object,\n",
" num_workers = 36, num_threads = 1\n",
")\n",
"mcube_object <- mcubeTest(\n",
" mcube_object,\n",
" num_workers = 36, num_threads = 1, shared_memory = TRUE\n",
")\n",
"\n",
"saveRDS(\n",
" mcube_object@pvalues,\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"mcube_pvalues_visium\", \".rds\")\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c9faafc7",
"metadata": {},
"source": [
"### Cell-type-specific SVG identification using `C-SIDE`"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "72149b32",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"CD4 T cell\", \"CD8 Cytotoxic T cell\", \"Endothelial\", \"Enteric Glial\", \"Enterocyte\", \"Fibroblast\", \"Goblet\", \"Lymphatic Endothelial\", \"Macrophage\", \"Myofibroblast\", \"Neutrophil\", \"Pericytes\", \"Plasma\", \"Proliferating Immune II\", \"Smooth Muscle\", \"Tumor I\", \"Tumor III\", \"Tumor V\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: Enteric Glial, Enterocyte, Fibroblast, Goblet, Lymphatic Endothelial, Myofibroblast, Neutrophil, Plasma, Proliferating Immune II, Smooth Muscle, Tumor I, Tumor V, vSM, \n",
"\n",
"Warning message in choose_cell_types(myRCTD, barcodes, doublet_mode, cell_type_threshold, :\n",
"“choose_cell_types: removing cell types: CD8 Cytotoxic T cell because these cell types did not contain sufficient pixels passing total cell type weight of weight_threshold = 0.8. Consider removing this cell type or lowering weight_threshold”\n",
"run.CSIDE.general: running CSIDE with cell types CAF, CD4 T cell, Endothelial, Macrophage, Pericytes, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n"
]
}
],
"source": [
"myCSIDE <- run.CSIDE.nonparam(\n",
" myRCTD,\n",
" df = 15,\n",
" weight_threshold = 0.8, cell_type_threshold = 20,\n",
" doublet_mode = FALSE\n",
")\n",
"saveRDS(\n",
" myCSIDE@de_results$all_gene_list,\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"cside_pvalues_visium\", \".rds\")\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "33c06b23",
"metadata": {},
"source": [
"### Cell-type-specific SVG identification using `CELINA`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6bc0d151",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message in asMethod(object):\n",
"“sparse->dense coercion: allocating vector of size 37.7 GiB”\n"
]
}
],
"source": [
"RhpcBLASctl::blas_set_num_threads(1)\n",
"\n",
"celina_object <- Create_Celina_Object(\n",
" celltype_mat = t(proportions_RCTD),\n",
" gene_expression_mat = myRCTD@originalSpatialRNA@counts[, spot_names],\n",
" location = as.matrix(myRCTD@spatialRNA@coords),\n",
" covariates = NULL\n",
")\n",
"celina_object <- preprocess_input(\n",
" celina_object, # Celina object\n",
" cell_types_to_test = celltypes_tme, # a vector of cell types to be used for testing\n",
" scRNA_count = FlexRef, # a gene x cell expression matrix of reference scRNA-seq data\n",
" sc_cell_type_labels = CTRef, # a vector of cell type labels for each cell in scRNA_count\n",
" threshold = 5e-5\n",
")\n",
"celina_object <- Calculate_Kernel(\n",
" celina_object,\n",
" approximation = FALSE\n",
")\n",
"celina_object <- Testing_interaction_all(\n",
" celina_object,\n",
" celltype_to_test = celltypes_tme,\n",
" method = \"MM\",\n",
" num_cores = 36\n",
")\n",
"\n",
"pvalues <- celina_object@result[\n",
" sapply(celina_object@result, function(x) ncol(x) > 1, simplify = TRUE)\n",
"]\n",
"rm(celina_object)\n",
"\n",
"saveRDS(\n",
" pvalues,\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"celina_pvalues_visium\", \".rds\")\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "0204c276",
"metadata": {},
"source": [
"## 10x Visium HD"
]
},
{
"cell_type": "markdown",
"id": "18d86fd4",
"metadata": {},
"source": [
"### Cell type deconvolution using `RCTD`"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5d4937f",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"# library(Seurat)\n",
"# library(arrow)\n",
"\n",
"# counts <- Read10X_h5(file.path(\n",
"# RAW_DATA_PATH, \"visium_hd\",\n",
"# \"binned_outputs/square_016um/filtered_feature_bc_matrix.h5\"\n",
"# ))\n",
"\n",
"# coords <- read_parquet(\n",
"# file.path(\n",
"# RAW_DATA_PATH, \"visium_hd\",\n",
"# \"binned_outputs/square_016um/spatial/tissue_positions.parquet\"\n",
"# )\n",
"# )\n",
"# coords <- as.data.frame(coords)\n",
"# rownames(coords) <- coords$barcode\n",
"# coords <- coords[colnames(counts), ]\n",
"# coords <- coords[, c(6, 5)]\n",
"\n",
"# nUMI <- colSums(counts)\n",
"\n",
"# puck <- SpatialRNA(coords, counts, nUMI)\n",
"# barcodes <- colnames(puck@counts)\n",
"\n",
"# myRCTDhd <- create.RCTD(puck, reference, max_cores = 32)\n",
"# myRCTD <- run.RCTD(myRCTD, doublet_mode = \"doublet\")\n",
"\n",
"# saveRDS(\n",
"# myRCTD,\n",
"# file = file.path(\n",
"# RESULT_PATH, \"VisiumHD\", \"myRCTD_16um.rds\"\n",
"# )\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "c3dcf87e",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"myRCTD <- readRDS(file.path(RESULT_PATH, \"VisiumHD\", \"myRCTD_16um.rds\"))"
]
},
{
"cell_type": "markdown",
"id": "9217346c",
"metadata": {},
"source": [
"### Cell-type-specific SVG identification using `MCube` with different sample sizes"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "da2c6a5a",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"weights_RCTD <- as.matrix(myRCTD@results$weights)\n",
"proportions_RCTD <- weights_RCTD / rowSums(weights_RCTD)\n",
"spot_effects_RCTD <- log(rowSums(weights_RCTD))\n",
"names(spot_effects_RCTD) <- rownames(weights_RCTD)\n",
"doublet_results_RCTD <- myRCTD@results$results_df"
]
},
{
"cell_type": "markdown",
"id": "11d00d77",
"metadata": {},
"source": [
"We compare the performances of `MCube` with different sample sizes on Visium HD data."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b0f0a88e",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"sample_size_seq <- c(3000L, 4000L, 5000L, 6000L, 7000L)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f4797fb",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"CD4 T cell\", \"Endothelial\", \"Macrophage\", \"Tumor III\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: CD4 T cell, \n",
"\n",
"Warning message in choose_cell_types(myRCTD, barcodes, doublet_mode, cell_type_threshold, :\n",
"“choose_cell_types: removing cell types: Endothelial because these cell types did not contain sufficient pixels passing total cell type weight of weight_threshold = 0.8. Consider removing this cell type or lowering weight_threshold”\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"CD4 T cell\", \"Endothelial\", \"Macrophage\", \"Plasma\", \"Proliferating Immune II\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: Plasma, Proliferating Immune II, vSM, \n",
"\n",
"Warning message in choose_cell_types(myRCTD, barcodes, doublet_mode, cell_type_threshold, :\n",
"“choose_cell_types: removing cell types: CD4 T cell because these cell types did not contain sufficient pixels passing total cell type weight of weight_threshold = 0.8. Consider removing this cell type or lowering weight_thresholdchoose_cell_types: removing cell types: Endothelial because these cell types did not contain sufficient pixels passing total cell type weight of weight_threshold = 0.8. Consider removing this cell type or lowering weight_threshold”\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"CD4 T cell\", \"Endothelial\", \"Macrophage\", \"Plasma\", \"Proliferating Immune II\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: CD4 T cell, Plasma, Proliferating Immune II, vSM, \n",
"\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Endothelial, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"CD4 T cell\", \"CD8 Cytotoxic T cell\", \"Endothelial\", \"Macrophage\", \"Plasma\", \"Proliferating Immune II\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: CD8 Cytotoxic T cell, Plasma, Proliferating Immune II, vSM, \n",
"\n",
"Warning message in choose_cell_types(myRCTD, barcodes, doublet_mode, cell_type_threshold, :\n",
"“choose_cell_types: removing cell types: CD4 T cell because these cell types did not contain sufficient pixels passing total cell type weight of weight_threshold = 0.8. Consider removing this cell type or lowering weight_threshold”\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Endothelial, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"CD4 T cell\", \"CD8 Cytotoxic T cell\", \"Endothelial\", \"Macrophage\", \"Plasma\", \"Proliferating Immune II\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: CD8 Cytotoxic T cell, Plasma, Proliferating Immune II, vSM, \n",
"\n",
"Warning message in choose_cell_types(myRCTD, barcodes, doublet_mode, cell_type_threshold, :\n",
"“choose_cell_types: removing cell types: CD4 T cell because these cell types did not contain sufficient pixels passing total cell type weight of weight_threshold = 0.8. Consider removing this cell type or lowering weight_threshold”\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Endothelial, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"Endothelial\", \"Macrophage\", \"Plasma\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: Plasma, vSM, \n",
"\n",
"Warning message in choose_cell_types(myRCTD, barcodes, doublet_mode, cell_type_threshold, :\n",
"“choose_cell_types: removing cell types: Endothelial because these cell types did not contain sufficient pixels passing total cell type weight of weight_threshold = 0.8. Consider removing this cell type or lowering weight_threshold”\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"Endothelial\", \"Macrophage\", \"Pericytes\", \"Plasma\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: Pericytes, Plasma, vSM, \n",
"\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Endothelial, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"Endothelial\", \"Macrophage\", \"Pericytes\", \"Plasma\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: Pericytes, Plasma, vSM, \n",
"\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Endothelial, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n",
"choose_cell_types: detected the following cell types occuring at least cell_type_threshold times: c(\"CAF\", \"CD4 T cell\", \"Endothelial\", \"Fibroblast\", \"Macrophage\", \"Pericytes\", \"Plasma\", \"Proliferating Immune II\", \"Tumor III\", \"vSM\")\n",
"\n",
"\n",
"Warning: run.CSIDE.general: removing the following cell types due to insufficient counts per region. Consider lowering cell_type_threshold or proceeding with removed cell types. Cell types: CD4 T cell, Fibroblast, Pericytes, Plasma, Proliferating Immune II, vSM, \n",
"\n",
"run.CSIDE.general: running CSIDE with cell types CAF, Endothelial, Macrophage, Tumor III\n",
"\n",
"run.CSIDE.general: configure params_to_test = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, \n",
"\n",
"filter_genes: filtering genes based on threshold = 5e-05\n",
"\n",
"set_global_Q_all: begin\n",
"\n",
"set_global_Q_all: finished\n",
"\n"
]
}
],
"source": [
"for (celltype in celltypes_tme) {\n",
" spots_used <- rownames(doublet_results_RCTD)[\n",
" ((doublet_results_RCTD$spot_class == \"singlet\" |\n",
" doublet_results_RCTD$spot_class == \"doublet_uncertain\") &\n",
" doublet_results_RCTD$first_type == celltype\n",
" ) |\n",
" (doublet_results_RCTD$spot_class == \"doublet_certain\" &\n",
" (doublet_results_RCTD$first_type == celltype |\n",
" doublet_results_RCTD$second_type == celltype))\n",
" ]\n",
" length(spots_used)\n",
" \n",
" for (sample_size in sample_size_seq) {\n",
" spots_sample <- sample(spots_used, size = sample_size, replace = FALSE)\n",
"\n",
" # MCube\n",
" mcube_object <- createMCube(\n",
" counts = t(as.matrix(myRCTD@originalSpatialRNA@counts[, spots_sample])),\n",
" coordinates = as.matrix(myRCTD@spatialRNA@coords[spots_sample, ]),\n",
" proportions = proportions_RCTD[spots_sample, ],\n",
" library_sizes = myRCTD@spatialRNA@nUMI[spots_sample],\n",
" reference = t(myRCTD@cell_type_info$info[[1]]),\n",
" used_for_deconvolution = rownames(myRCTD@spatialRNA@counts),\n",
" spot_effects = spot_effects_RCTD[spots_sample],\n",
" celltype_test = celltype,\n",
" proportion_threshold = 0.01\n",
" )\n",
" mcube_object <- mcubeFitNull(\n",
" mcube_object,\n",
" num_workers = 36, num_threads = 1\n",
" )\n",
" mcube_object <- mcubeTest(\n",
" mcube_object,\n",
" num_workers = 36, num_threads = 1, shared_memory = TRUE\n",
" )\n",
" saveRDS(\n",
" mcube_object@pvalues,\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"mcube_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
" )\n",
" )\n",
" rm(mcube_object)\n",
"\n",
" # CELINA\n",
" celina_object <- Create_Celina_Object(\n",
" celltype_mat = t(proportions_RCTD[spots_sample, ]),\n",
" gene_expression_mat = myRCTD@originalSpatialRNA@counts[, spots_sample],\n",
" location = as.matrix(myRCTD@spatialRNA@coords[spots_sample, ]),\n",
" covariates = NULL\n",
" )\n",
" celina_object <- preprocess_input(\n",
" celina_object, # Celina object\n",
" cell_types_to_test = celltypes_tme, # a vector of cell types to be used for testing\n",
" scRNA_count = FlexRef, # a gene x cell expression matrix of reference scRNA-seq data\n",
" sc_cell_type_labels = CTRef, # a vector of cell type labels for each cell in scRNA_count\n",
" threshold = 5e-5\n",
" )\n",
" celina_object <- Calculate_Kernel(\n",
" celina_object,\n",
" approximation = FALSE\n",
" )\n",
" celina_object <- Testing_interaction_all(\n",
" celina_object,\n",
" celltype_to_test = celltypes_tme,\n",
" method = \"MM\",\n",
" num_cores = 36\n",
" )\n",
" pvalues <- celina_object@result[\n",
" sapply(celina_object@result, function(x) ncol(x) > 1, simplify = TRUE)\n",
" ]\n",
" rm(celina_object)\n",
" saveRDS(\n",
" pvalues,\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"celina_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
" )\n",
" )\n",
"\n",
" # C-SIDE\n",
" myCSIDE <- run.CSIDE.nonparam(\n",
" myRCTD,\n",
" df = 15, barcodes = spots_sample,\n",
" weight_threshold = 0.8, doublet_mode = TRUE\n",
" )\n",
" saveRDS(\n",
" myCSIDE@de_results$all_gene_list,\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"cside_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
" )\n",
" )\n",
" rm(myCSIDE)\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "984b594c",
"metadata": {},
"source": [
"## Visualization of the comparison results"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8002bafb",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"celltypes_tme <- c(\"CAF\", \"Macrophage\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e16a6d12",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"data": {
"text/html": [
"- CAF
- 2185
- Macrophage
- 2683
\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[CAF] 2185\n",
"\\item[Macrophage] 2683\n",
"\\end{description*}\n"
],
"text/markdown": [
"CAF\n",
": 2185Macrophage\n",
": 2683\n",
"\n"
],
"text/plain": [
" CAF Macrophage \n",
" 2185 2683 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"- CAF
- 5009
- Macrophage
- 4770
\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[CAF] 5009\n",
"\\item[Macrophage] 4770\n",
"\\end{description*}\n"
],
"text/markdown": [
"CAF\n",
": 5009Macrophage\n",
": 4770\n",
"\n"
],
"text/plain": [
" CAF Macrophage \n",
" 5009 4770 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"- CAF
- 1901
- Macrophage
- 1908
\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[CAF] 1901\n",
"\\item[Macrophage] 1908\n",
"\\end{description*}\n"
],
"text/markdown": [
"CAF\n",
": 1901Macrophage\n",
": 1908\n",
"\n"
],
"text/plain": [
" CAF Macrophage \n",
" 1901 1908 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mcube_pvalues_visium <- readRDS(\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"mcube_pvalues_visium\", \".rds\")\n",
" )\n",
")[celltypes_tme]\n",
"sapply(mcube_pvalues_visium, nrow)\n",
"\n",
"celina_pvalues_visium <- readRDS(\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"celina_pvalues_visium\", \".rds\")\n",
" )\n",
")[celltypes_tme]\n",
"sapply(celina_pvalues_visium, nrow)\n",
"\n",
"cside_pvalues_visium <- readRDS(\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"cside_pvalues_visium\", \".rds\")\n",
" )\n",
")[celltypes_tme]\n",
"sapply(cside_pvalues_visium, nrow)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3c35ef77",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": [
"# sample_size_seq <- c(3000L, 4000L, 5000L, 6000, 7000L)\n",
"# for (celltype in celltypes_tme) {\n",
"# mcube_pvalues_df <- data.frame(\n",
"# gene = rownames(mcube_pvalues_visium[[celltype]]),\n",
"# Visium = mcube_pvalues_visium[[celltype]]$combined_pvalue\n",
"# )\n",
"# for (sample_size in sample_size_seq) {\n",
"# mcube_pvalues_visiumhd <- readRDS(\n",
"# file = file.path(\n",
"# RESULT_PATH, \"comparison\",\n",
"# paste0(\"mcube_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
"# )\n",
"# )[[celltype]]\n",
"# mcube_pvalues_df <- merge(\n",
"# mcube_pvalues_df,\n",
"# data.frame(\n",
"# gene = rownames(mcube_pvalues_visiumhd),\n",
"# pvalue = mcube_pvalues_visiumhd$combined_pvalue\n",
"# ),\n",
"# by = \"gene\"\n",
"# )\n",
"# colnames(mcube_pvalues_df)[ncol(mcube_pvalues_df)] <- paste0(\"Visium HD \", sample_size)\n",
"# }\n",
"\n",
"# celina_pvalues_df <- data.frame(\n",
"# gene = rownames(celina_pvalues_visium[[celltype]]),\n",
"# Visium = celina_pvalues_visium[[celltype]]$CombinedPvals\n",
"# )\n",
"# for (sample_size in sample_size_seq) {\n",
"# celina_pvalues_visiumhd <- readRDS(\n",
"# file = file.path(\n",
"# RESULT_PATH, \"comparison\",\n",
"# paste0(\"celina_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
"# )\n",
"# )[[celltype]]\n",
"# celina_pvalues_df <- merge(\n",
"# celina_pvalues_df,\n",
"# data.frame(\n",
"# gene = rownames(celina_pvalues_visiumhd),\n",
"# pvalue = celina_pvalues_visiumhd$CombinedPvals\n",
"# ),\n",
"# by = \"gene\"\n",
"# )\n",
"# colnames(celina_pvalues_df)[ncol(celina_pvalues_df)] <- paste0(\"Visium HD \", sample_size)\n",
"# }\n",
"\n",
"# overlapped_genes <- intersect(mcube_pvalues_df$gene, celina_pvalues_df$gene)\n",
"# length(overlapped_genes)\n",
"\n",
"# mcube_pvalues_df <- mcube_pvalues_df[\n",
"# mcube_pvalues_df$gene %in% overlapped_genes,\n",
"# ]\n",
"# celina_pvalues_df <- celina_pvalues_df[\n",
"# celina_pvalues_df$gene %in% overlapped_genes,\n",
"# ]\n",
"\n",
"# mcube_svgs_list <- lapply(\n",
"# mcube_pvalues_df[, -1],\n",
"# FUN = function(x) {\n",
"# x <- p.adjust(x, method = \"BH\")\n",
"# mcube_pvalues_df$gene[x < 0.05]\n",
"# }\n",
"# )\n",
"# names(mcube_svgs_list) <- c(\n",
"# \"Visium\", paste0(\"Visium HD \", sample_size_seq)\n",
"# )\n",
"# mcube_overlapped_svgs <- sapply(\n",
"# mcube_svgs_list,\n",
"# FUN = function(x) {\n",
"# length(intersect(x, mcube_svgs_list[[\"Visium\"]]))\n",
"# },\n",
"# simplify = TRUE\n",
"# )\n",
"# mcube_overlapped_svgs <- data.frame(\n",
"# setting = names(mcube_overlapped_svgs),\n",
"# number = mcube_overlapped_svgs\n",
"# )\n",
"# mcube_overlapped_svgs$proportion <-\n",
"# mcube_overlapped_svgs$number / mcube_overlapped_svgs$number[1] * 100\n",
"# mcube_overlapped_svgs$proportion[1] <- NA\n",
"# mcube_overlapped_svgs$setting <- factor(\n",
"# mcube_overlapped_svgs$setting,\n",
"# levels = c(paste0(\"Visium HD \", sample_size_seq), \"Visium\")\n",
"# )\n",
"\n",
"# celina_svgs_list <- lapply(\n",
"# celina_pvalues_df[, -1],\n",
"# FUN = function(x) {\n",
"# x <- p.adjust(x, method = \"BH\")\n",
"# celina_pvalues_df$gene[x < 0.05]\n",
"# }\n",
"# )\n",
"# names(celina_svgs_list) <- c(\n",
"# \"Visium\", paste0(\"Visium HD \", sample_size_seq)\n",
"# )\n",
"# celina_overlapped_svgs <- sapply(\n",
"# celina_svgs_list,\n",
"# FUN = function(x) {\n",
"# length(intersect(x, celina_svgs_list[[\"Visium\"]]))\n",
"# },\n",
"# simplify = TRUE\n",
"# )\n",
"# celina_overlapped_svgs <- data.frame(\n",
"# setting = names(celina_overlapped_svgs),\n",
"# number = celina_overlapped_svgs\n",
"# )\n",
"# celina_overlapped_svgs$proportion <-\n",
"# celina_overlapped_svgs$number / celina_overlapped_svgs$number[1] * 100\n",
"# celina_overlapped_svgs$proportion[1] <- NA\n",
"# celina_overlapped_svgs$setting <- factor(\n",
"# celina_overlapped_svgs$setting,\n",
"# levels = c(paste0(\"Visium HD \", sample_size_seq), \"Visium\")\n",
"# )\n",
"\n",
"# overlapped_svgs_comparison <- rbind(\n",
"# data.frame(\n",
"# mcube_overlapped_svgs,\n",
"# method = \"MMM\"\n",
"# ),\n",
"# data.frame(\n",
"# celina_overlapped_svgs,\n",
"# method = \"Celina\"\n",
"# )\n",
"# )\n",
"# overlapped_svgs_comparison$method <- factor(\n",
"# overlapped_svgs_comparison$method,\n",
"# levels = c(\"MMM\", \"Celina\")\n",
"# )\n",
"\n",
"# p <- ggplot(data = overlapped_svgs_comparison, aes(x = setting, y = number, group = method)) +\n",
"# geom_bar(aes(fill = method), stat = \"identity\", position = \"dodge\") +\n",
"# geom_text(\n",
"# aes(label = ifelse(!is.na(proportion), paste0(round(proportion), \"%\"), \"\")),\n",
"# position = position_dodge(width = 0.9), vjust = -0.5, size = 5\n",
"# ) +\n",
"# theme_classic() +\n",
"# labs(\n",
"# title = paste(\"Number of overlapped SVGs of\", celltype),\n",
"# x = NULL, y = NULL\n",
"# ) +\n",
"# theme(\n",
"# plot.title = element_text(size = 20),\n",
"# axis.text.x = element_text(size = 16,angle = 45, vjust = 0.5),\n",
"# # axis.ticks.x = element_blank(),\n",
"# axis.text.y = element_text(size = 16),\n",
"# legend.title = element_blank(),\n",
"# legend.text = element_text(size = 16),\n",
"# legend.position = \"bottom\"\n",
"# )\n",
"# ggsave(\n",
"# filename = file.path(\n",
"# RESULT_PATH, \"comparison\",\n",
"# paste0(\"comparison_overlapped_svgs_\", celltype, \".pdf\")\n",
"# ),\n",
"# plot = p\n",
"# )\n",
"# }"
]
},
{
"cell_type": "markdown",
"id": "5e0955e5",
"metadata": {},
"source": [
"### Comparison beween `MCube` and `CELINA`"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6517ba42",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"CAF, overlapped genes: 1273\n",
"\n",
"CAF, MCube, 797\n",
"\n",
"CAF, CELINA, 498\n",
"\n",
"CAF, Visium, SVGs identified by both methods, 412\n",
"\n",
"Macrophage, overlapped genes: 648\n",
"\n",
"Macrophage, MCube, 315\n",
"\n",
"Macrophage, CELINA, 223\n",
"\n",
"Macrophage, Visium, SVGs identified by both methods, 150\n",
"\n"
]
},
{
"data": {
"text/html": [
"\n",
"A data.frame: 6 × 4\n",
"\n",
"\t | sample_size | proportion | method | celltype |
|---|
\n",
"\t | <int> | <dbl> | <fct> | <chr> |
|---|
\n",
"\n",
"\n",
"\t| Visium HD 3000 | 3000 | 0.2710163 | MMM | CAF |
|---|
\n",
"\t| Visium HD 4000 | 4000 | 0.3161857 | MMM | CAF |
|---|
\n",
"\t| Visium HD 5000 | 5000 | 0.3676286 | MMM | CAF |
|---|
\n",
"\t| Visium HD 6000 | 6000 | 0.3776662 | MMM | CAF |
|---|
\n",
"\t| Visium HD 7000 | 7000 | 0.4291092 | MMM | CAF |
|---|
\n",
"\t| Visium HD 30001 | 3000 | 0.1445783 | Celina | CAF |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"A data.frame: 6 × 4\n",
"\\begin{tabular}{r|llll}\n",
" & sample\\_size & proportion & method & celltype\\\\\n",
" & & & & \\\\\n",
"\\hline\n",
"\tVisium HD 3000 & 3000 & 0.2710163 & MMM & CAF\\\\\n",
"\tVisium HD 4000 & 4000 & 0.3161857 & MMM & CAF\\\\\n",
"\tVisium HD 5000 & 5000 & 0.3676286 & MMM & CAF\\\\\n",
"\tVisium HD 6000 & 6000 & 0.3776662 & MMM & CAF\\\\\n",
"\tVisium HD 7000 & 7000 & 0.4291092 & MMM & CAF\\\\\n",
"\tVisium HD 30001 & 3000 & 0.1445783 & Celina & CAF\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A data.frame: 6 × 4\n",
"\n",
"| | sample_size <int> | proportion <dbl> | method <fct> | celltype <chr> |\n",
"|---|---|---|---|---|\n",
"| Visium HD 3000 | 3000 | 0.2710163 | MMM | CAF |\n",
"| Visium HD 4000 | 4000 | 0.3161857 | MMM | CAF |\n",
"| Visium HD 5000 | 5000 | 0.3676286 | MMM | CAF |\n",
"| Visium HD 6000 | 6000 | 0.3776662 | MMM | CAF |\n",
"| Visium HD 7000 | 7000 | 0.4291092 | MMM | CAF |\n",
"| Visium HD 30001 | 3000 | 0.1445783 | Celina | CAF |\n",
"\n"
],
"text/plain": [
" sample_size proportion method celltype\n",
"Visium HD 3000 3000 0.2710163 MMM CAF \n",
"Visium HD 4000 4000 0.3161857 MMM CAF \n",
"Visium HD 5000 5000 0.3676286 MMM CAF \n",
"Visium HD 6000 6000 0.3776662 MMM CAF \n",
"Visium HD 7000 7000 0.4291092 MMM CAF \n",
"Visium HD 30001 3000 0.1445783 Celina CAF "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size_seq <- c(3000L, 4000L, 5000L, 6000L, 7000L)\n",
"overlapped_svgs_comparison <- data.frame()\n",
"for (celltype in celltypes_tme) {\n",
" mcube_pvalues_df <- data.frame(\n",
" gene = rownames(mcube_pvalues_visium[[celltype]]),\n",
" Visium = mcube_pvalues_visium[[celltype]]$combined_pvalue\n",
" )\n",
" for (sample_size in sample_size_seq) {\n",
" mcube_pvalues_visiumhd <- readRDS(\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"mcube_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
" )\n",
" )[[celltype]]\n",
" mcube_pvalues_df <- merge(\n",
" mcube_pvalues_df,\n",
" data.frame(\n",
" gene = rownames(mcube_pvalues_visiumhd),\n",
" pvalue = mcube_pvalues_visiumhd$combined_pvalue\n",
" ),\n",
" by = \"gene\"\n",
" )\n",
" colnames(mcube_pvalues_df)[ncol(mcube_pvalues_df)] <- paste0(\"Visium HD \", sample_size)\n",
" }\n",
"\n",
" celina_pvalues_df <- data.frame(\n",
" gene = rownames(celina_pvalues_visium[[celltype]]),\n",
" Visium = celina_pvalues_visium[[celltype]]$CombinedPvals\n",
" )\n",
" for (sample_size in sample_size_seq) {\n",
" celina_pvalues_visiumhd <- readRDS(\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"celina_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
" )\n",
" )[[celltype]]\n",
" celina_pvalues_df <- merge(\n",
" celina_pvalues_df,\n",
" data.frame(\n",
" gene = rownames(celina_pvalues_visiumhd),\n",
" pvalue = celina_pvalues_visiumhd$CombinedPvals\n",
" ),\n",
" by = \"gene\"\n",
" )\n",
" colnames(celina_pvalues_df)[ncol(celina_pvalues_df)] <- paste0(\"Visium HD \", sample_size)\n",
" }\n",
"\n",
" overlapped_genes <- intersect(mcube_pvalues_df$gene, celina_pvalues_df$gene)\n",
" message(celltype, \", overlapped genes: \", length(overlapped_genes))\n",
"\n",
" mcube_pvalues_df <- mcube_pvalues_df[\n",
" mcube_pvalues_df$gene %in% overlapped_genes,\n",
" ]\n",
" celina_pvalues_df <- celina_pvalues_df[\n",
" celina_pvalues_df$gene %in% overlapped_genes,\n",
" ]\n",
"\n",
" mcube_svgs_list <- lapply(\n",
" mcube_pvalues_df[, -1],\n",
" FUN = function(x) {\n",
" x <- p.adjust(x, method = \"BH\")\n",
" mcube_pvalues_df$gene[x < 0.05]\n",
" }\n",
" )\n",
" names(mcube_svgs_list) <- c(\n",
" \"Visium\", paste0(\"Visium HD \", sample_size_seq)\n",
" )\n",
" message(celltype, \", MCube, \", length(mcube_svgs_list$Visium))\n",
"\n",
" mcube_overlapped_svgs <- sapply(\n",
" mcube_svgs_list,\n",
" FUN = function(x) {\n",
" length(intersect(x, mcube_svgs_list[[\"Visium\"]]))\n",
" },\n",
" simplify = TRUE\n",
" )\n",
" mcube_overlapped_svgs <- data.frame(\n",
" sample_size = sample_size_seq,\n",
" proportion = mcube_overlapped_svgs[-1] / mcube_overlapped_svgs[1]\n",
" )\n",
"\n",
" celina_svgs_list <- lapply(\n",
" celina_pvalues_df[, -1],\n",
" FUN = function(x) {\n",
" x <- p.adjust(x, method = \"BH\")\n",
" celina_pvalues_df$gene[x < 0.05]\n",
" }\n",
" )\n",
" names(celina_svgs_list) <- c(\n",
" \"Visium\", paste0(\"Visium HD \", sample_size_seq)\n",
" )\n",
" message(celltype, \", CELINA, \", length(celina_svgs_list$Visium))\n",
"\n",
" celina_overlapped_svgs <- sapply(\n",
" celina_svgs_list,\n",
" FUN = function(x) {\n",
" length(intersect(x, celina_svgs_list[[\"Visium\"]]))\n",
" },\n",
" simplify = TRUE\n",
" )\n",
" celina_overlapped_svgs <- data.frame(\n",
" sample_size = sample_size_seq,\n",
" proportion = celina_overlapped_svgs[-1] / celina_overlapped_svgs[1]\n",
" )\n",
"\n",
" message(\n",
" celltype, \", Visium, \", \"SVGs identified by both methods, \",\n",
" length(intersect(\n",
" mcube_svgs_list[[\"Visium\"]], celina_svgs_list[[\"Visium\"]]\n",
" ))\n",
" )\n",
"\n",
" overlapped_svgs_comparison <- rbind(\n",
" overlapped_svgs_comparison,\n",
" data.frame(\n",
" mcube_overlapped_svgs,\n",
" method = \"MMM\",\n",
" celltype = celltype\n",
" ),\n",
" data.frame(\n",
" celina_overlapped_svgs,\n",
" method = \"Celina\",\n",
" celltype = celltype\n",
" )\n",
" )\n",
"}\n",
"overlapped_svgs_comparison$method <- factor(\n",
" overlapped_svgs_comparison$method,\n",
" levels = c(\"MMM\", \"Celina\")\n",
")\n",
"head(overlapped_svgs_comparison)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4addafc4",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"p <- ggplot(data = overlapped_svgs_comparison, aes(x = sample_size, y = proportion, group = method)) +\n",
" geom_point(aes(color = method, shape = method), size = 6) +\n",
" geom_line(aes(color = method), linewidth = 1.5) +\n",
" scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +\n",
" theme_classic() +\n",
" labs(\n",
" title = paste(\"Identified cell-type-specific SVGs in the Visium HD vs. Visium data\"),\n",
" x = \"Number of sample bins in the Visium HD data\", y = \"Overlapped proportion\"\n",
" ) +\n",
" facet_wrap(. ~ celltype, scales = \"free_y\") +\n",
" theme(\n",
" text = element_text(family = \"Helvetica\"),\n",
" # plot.title = element_text(size = 20, hjust = 0.5),\n",
" plot.title = element_blank(),\n",
" axis.title = element_text(size = 20),\n",
" axis.text = element_text(size = 16),\n",
" strip.text = element_text(size = 16),\n",
" legend.title = element_blank(),\n",
" legend.text = element_text(size = 16),\n",
" legend.position = \"bottom\",\n",
" panel.spacing = unit(1, \"lines\")\n",
" )\n",
"ggsave(\n",
" filename = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"comparison_MMM_Celina_overlapped_svgs\", \".pdf\")\n",
" ),\n",
" plot = p, width = 9, height = 5\n",
")\n",
"ggsave(\n",
" filename = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"comparison_MMM_Celina_overlapped_svgs\", \".png\")\n",
" ),\n",
" plot = p, width = 9, height = 5\n",
")\n",
"p"
]
},
{
"cell_type": "markdown",
"id": "26ac1a84",
"metadata": {},
"source": [
"### Comparison beween `MCube` and `C-SIDE`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "974d968d",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"CAF, overlapped genes: 888\n",
"\n",
"CAF, MCube, 601\n",
"\n",
"CAF, C-SIDE, 214\n",
"\n",
"CAF, Visium, SVGs identified by both methods, 40\n",
"\n",
"Macrophage, overlapped genes: 997\n",
"\n",
"Macrophage, MCube, 329\n",
"\n",
"Macrophage, C-SIDE, 305\n",
"\n",
"Macrophage, Visium, SVGs identified by both methods, 129\n",
"\n"
]
},
{
"data": {
"text/html": [
"\n",
"A data.frame: 6 × 4\n",
"\n",
"\t | sample_size | proportion | method | celltype |
|---|
\n",
"\t | <int> | <dbl> | <fct> | <chr> |
|---|
\n",
"\n",
"\n",
"\t| Visium HD 3000 | 3000 | 0.3227953 | MMM | CAF |
|---|
\n",
"\t| Visium HD 4000 | 4000 | 0.3643927 | MMM | CAF |
|---|
\n",
"\t| Visium HD 5000 | 5000 | 0.4159734 | MMM | CAF |
|---|
\n",
"\t| Visium HD 6000 | 6000 | 0.4509151 | MMM | CAF |
|---|
\n",
"\t| Visium HD 7000 | 7000 | 0.4941764 | MMM | CAF |
|---|
\n",
"\t| Visium HD 30001 | 3000 | 0.1635514 | C-SIDE | CAF |
|---|
\n",
"\n",
"
\n"
],
"text/latex": [
"A data.frame: 6 × 4\n",
"\\begin{tabular}{r|llll}\n",
" & sample\\_size & proportion & method & celltype\\\\\n",
" & & & & \\\\\n",
"\\hline\n",
"\tVisium HD 3000 & 3000 & 0.3227953 & MMM & CAF\\\\\n",
"\tVisium HD 4000 & 4000 & 0.3643927 & MMM & CAF\\\\\n",
"\tVisium HD 5000 & 5000 & 0.4159734 & MMM & CAF\\\\\n",
"\tVisium HD 6000 & 6000 & 0.4509151 & MMM & CAF\\\\\n",
"\tVisium HD 7000 & 7000 & 0.4941764 & MMM & CAF\\\\\n",
"\tVisium HD 30001 & 3000 & 0.1635514 & C-SIDE & CAF\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A data.frame: 6 × 4\n",
"\n",
"| | sample_size <int> | proportion <dbl> | method <fct> | celltype <chr> |\n",
"|---|---|---|---|---|\n",
"| Visium HD 3000 | 3000 | 0.3227953 | MMM | CAF |\n",
"| Visium HD 4000 | 4000 | 0.3643927 | MMM | CAF |\n",
"| Visium HD 5000 | 5000 | 0.4159734 | MMM | CAF |\n",
"| Visium HD 6000 | 6000 | 0.4509151 | MMM | CAF |\n",
"| Visium HD 7000 | 7000 | 0.4941764 | MMM | CAF |\n",
"| Visium HD 30001 | 3000 | 0.1635514 | C-SIDE | CAF |\n",
"\n"
],
"text/plain": [
" sample_size proportion method celltype\n",
"Visium HD 3000 3000 0.3227953 MMM CAF \n",
"Visium HD 4000 4000 0.3643927 MMM CAF \n",
"Visium HD 5000 5000 0.4159734 MMM CAF \n",
"Visium HD 6000 6000 0.4509151 MMM CAF \n",
"Visium HD 7000 7000 0.4941764 MMM CAF \n",
"Visium HD 30001 3000 0.1635514 C-SIDE CAF "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size_seq <- c(3000L, 4000L, 5000L, 6000L, 7000L)\n",
"overlapped_svgs_comparison <- data.frame()\n",
"for (celltype in celltypes_tme) {\n",
" mcube_pvalues_df <- data.frame(\n",
" gene = rownames(mcube_pvalues_visium[[celltype]]),\n",
" Visium = mcube_pvalues_visium[[celltype]]$combined_pvalue\n",
" )\n",
" for (sample_size in sample_size_seq) {\n",
" mcube_pvalues_visiumhd <- readRDS(\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"mcube_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
" )\n",
" )[[celltype]]\n",
" mcube_pvalues_df <- merge(\n",
" mcube_pvalues_df,\n",
" data.frame(\n",
" gene = rownames(mcube_pvalues_visiumhd),\n",
" pvalue = mcube_pvalues_visiumhd$combined_pvalue\n",
" ),\n",
" by = \"gene\"\n",
" )\n",
" colnames(mcube_pvalues_df)[ncol(mcube_pvalues_df)] <- paste0(\"Visium HD \", sample_size)\n",
" }\n",
"\n",
" cside_pvalues_df <- data.frame(\n",
" gene = rownames(cside_pvalues_visium[[celltype]]),\n",
" Visium = cside_pvalues_visium[[celltype]]$p_val\n",
" )\n",
" for (sample_size in sample_size_seq) {\n",
" cside_pvalues_visiumhd <- readRDS(\n",
" file = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"cside_pvalues_visiumhd_\", celltype, \"_\", sample_size, \".rds\")\n",
" )\n",
" )[[celltype]]\n",
" cside_pvalues_df <- merge(\n",
" cside_pvalues_df,\n",
" data.frame(\n",
" gene = rownames(cside_pvalues_visiumhd),\n",
" pvalue = cside_pvalues_visiumhd$p_val\n",
" ),\n",
" by = \"gene\"\n",
" )\n",
" colnames(cside_pvalues_df)[ncol(cside_pvalues_df)] <- paste0(\"Visium HD \", sample_size)\n",
" }\n",
"\n",
" overlapped_genes <- intersect(mcube_pvalues_df$gene, cside_pvalues_df$gene)\n",
" message(celltype, \", overlapped genes: \", length(overlapped_genes))\n",
"\n",
" mcube_pvalues_df <- mcube_pvalues_df[\n",
" mcube_pvalues_df$gene %in% overlapped_genes,\n",
" ]\n",
" cside_pvalues_df <- cside_pvalues_df[\n",
" cside_pvalues_df$gene %in% overlapped_genes,\n",
" ]\n",
"\n",
" mcube_svgs_list <- lapply(\n",
" mcube_pvalues_df[, -1],\n",
" FUN = function(x) {\n",
" x <- p.adjust(x, method = \"BH\")\n",
" mcube_pvalues_df$gene[x < 0.05]\n",
" }\n",
" )\n",
" names(mcube_svgs_list) <- c(\n",
" \"Visium\", paste0(\"Visium HD \", sample_size_seq)\n",
" )\n",
" message(celltype, \", MCube, \", length(mcube_svgs_list$Visium))\n",
"\n",
" mcube_overlapped_svgs <- sapply(\n",
" mcube_svgs_list,\n",
" FUN = function(x) {\n",
" length(intersect(x, mcube_svgs_list[[\"Visium\"]]))\n",
" },\n",
" simplify = TRUE\n",
" )\n",
" mcube_overlapped_svgs <- data.frame(\n",
" sample_size = sample_size_seq,\n",
" proportion = mcube_overlapped_svgs[-1] / mcube_overlapped_svgs[1]\n",
" )\n",
"\n",
" cside_svgs_list <- lapply(\n",
" cside_pvalues_df[, -1],\n",
" FUN = function(x) {\n",
" x <- p.adjust(x, method = \"BH\")\n",
" cside_pvalues_df$gene[x < 0.05]\n",
" }\n",
" )\n",
" names(cside_svgs_list) <- c(\n",
" \"Visium\", paste0(\"Visium HD \", sample_size_seq)\n",
" )\n",
" message(celltype, \", C-SIDE, \", length(cside_svgs_list$Visium))\n",
"\n",
" cside_overlapped_svgs <- sapply(\n",
" cside_svgs_list,\n",
" FUN = function(x) {\n",
" length(intersect(x, cside_svgs_list[[\"Visium\"]]))\n",
" },\n",
" simplify = TRUE\n",
" )\n",
" cside_overlapped_svgs <- data.frame(\n",
" sample_size = sample_size_seq,\n",
" proportion = cside_overlapped_svgs[-1] / cside_overlapped_svgs[1]\n",
" )\n",
"\n",
" message(\n",
" celltype, \", Visium, \", \"SVGs identified by both methods, \",\n",
" length(intersect(\n",
" mcube_svgs_list[[\"Visium\"]], celina_svgs_list[[\"Visium\"]]\n",
" ))\n",
" )\n",
"\n",
" overlapped_svgs_comparison <- rbind(\n",
" overlapped_svgs_comparison,\n",
" data.frame(\n",
" mcube_overlapped_svgs,\n",
" method = \"MMM\",\n",
" celltype = celltype\n",
" ),\n",
" data.frame(\n",
" cside_overlapped_svgs,\n",
" method = \"C-SIDE\",\n",
" celltype = celltype\n",
" )\n",
" )\n",
"}\n",
"overlapped_svgs_comparison$method <- factor(\n",
" overlapped_svgs_comparison$method,\n",
" levels = c(\"MMM\", \"C-SIDE\")\n",
")\n",
"head(overlapped_svgs_comparison)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "156bfce1",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 420,
"width": 420
}
},
"output_type": "display_data"
}
],
"source": [
"p <- ggplot(data = overlapped_svgs_comparison, aes(x = sample_size, y = proportion, group = method)) +\n",
" geom_point(aes(color = method, shape = method), size = 6) +\n",
" geom_line(aes(color = method), linewidth = 1.5) +\n",
" scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +\n",
" theme_classic() +\n",
" labs(\n",
" title = paste(\"Identified cell-type-specific SVGs in the Visium HD vs. Visium data\"),\n",
" x = \"Number of sample bins in the Visium HD data\", y = \"Overlapped proportion\"\n",
" ) +\n",
" facet_wrap(. ~ celltype, scales = \"free\") +\n",
" theme(\n",
" text = element_text(family = \"Helvetica\"),\n",
" plot.title = element_blank(),\n",
" axis.title = element_text(size = 20),\n",
" axis.text = element_text(size = 16),\n",
" strip.text = element_text(size = 16),\n",
" legend.title = element_blank(),\n",
" legend.text = element_text(size = 16),\n",
" legend.position = \"bottom\",\n",
" panel.spacing = unit(1, \"lines\")\n",
" )\n",
"ggsave(\n",
" filename = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"comparison_MMM_CSIDE_overlapped_svgs\", \".pdf\")\n",
" ),\n",
" plot = p, width = 9, height = 5\n",
")\n",
"ggsave(\n",
" filename = file.path(\n",
" RESULT_PATH, \"comparison\",\n",
" paste0(\"comparison_MMM_CSIDE_overlapped_svgs\", \".png\")\n",
" ),\n",
" plot = p, width = 9, height = 5\n",
")\n",
"p"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "08ae53da",
"metadata": {
"vscode": {
"languageId": "r"
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "4.3.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}