Applying RCTD and MCube to the 10x Visium HD CRC dataset

set.seed(20250502)

library(Matrix)
library(ggplot2)

library(spacexr)
library(MCube)

RAW_DATA_PATH <- "/import/home/share/zw/data/CRC"
DATA_PATH <- "/import/home/share/zw/pql/data/CRC"
RESULT_PATH <- "/import/home/share/zw/pql/results/CRC"

if (!dir.exists(file.path(RESULT_PATH, "VisiumHD"))) {
    dir.create(file.path(RESULT_PATH, "VisiumHD"), recursive = TRUE)
}

Cell type deconvolution using RCTD

# library(Seurat)

# FlexRef <- Read10X_h5(file.path(
#     RAW_DATA_PATH, "sc", "HumanColonCancer_Flex_Multiplex_count_filtered_feature_bc_matrix.h5"
# ))
# # MetaData <- readRDS(file.path(
# #     RAW_DATA_PATH, "sc", "FlexSeuratV5_MetaData.rds"
# # )) # See FlexSingleCell.R if not generated.

# meta <- read.csv(file.path(
#     RAW_DATA_PATH, "HumanColonCancer_VisiumHD/MetaData/SingleCell_MetaData.csv.gz"
# ))

# KpIdents <- names(which(table(meta$Level2) > 25))
# meta <- meta[meta$Level2 %in% KpIdents, ]
# FlexRef <- FlexRef[, meta$Barcode]

# CTRef <- meta$Level2
# CTRef <- gsub("/", "_", CTRef)
# CTRef <- as.factor(CTRef)
# names(CTRef) <- meta$Barcode

# reference <- Reference(FlexRef, CTRef, colSums(FlexRef))

# library(Seurat)
# library(arrow)

# counts <- Read10X_h5(file.path(
#     RAW_DATA_PATH, "visium_hd",
#     "binned_outputs/square_016um/filtered_feature_bc_matrix.h5"
# ))

# coords <- read_parquet(
#     file.path(
#         RAW_DATA_PATH, "visium_hd",
#         "binned_outputs/square_016um/spatial/tissue_positions.parquet"
#     )
# )
# coords <- as.data.frame(coords)
# rownames(coords) <- coords$barcode
# coords <- coords[colnames(counts), ]
# coords <- coords[, c(6, 5)]

# nUMI <- colSums(counts)

# puck <- SpatialRNA(coords, counts, nUMI)
# barcodes <- colnames(puck@counts)

# myRCTD <- create.RCTD(puck, reference, max_cores = 32)
# myRCTD <- run.RCTD(myRCTD, doublet_mode = "doublet")

# saveRDS(
#     myRCTD,
#     file = file.path(
#         RESULT_PATH, "VisiumHD", "myRCTD_16um.rds"
#     )
# )

Cell-type-specific SVG identification using MCube

Due to the high sparsity and significant noise in the Visium HD data, for the cell types of interest, we select bins that are confirmed to contain those specific cell types based on the results from RCTD (doublet mode) for further analysis.

myRCTD <- readRDS(file.path(RESULT_PATH, "VisiumHD", "myRCTD_16um.rds"))
weights_RCTD <- as.matrix(myRCTD@results$weights)
proportions_RCTD <- weights_RCTD / rowSums(weights_RCTD)
spot_effects_RCTD <- log(rowSums(weights_RCTD))
names(spot_effects_RCTD) <- rownames(weights_RCTD)
doublet_results_RCTD <- myRCTD@results$results_df

# gene_xenium <- colnames(readr::read_csv(
#     file.path(DATA_PATH, "Xenium", "xenium_p2_counts.csv")
# ))[-1]

sample_size_max <- 10000
celltype_threshold <- 100
for (celltype in colnames(proportions_RCTD)) {
    spots_used <- rownames(doublet_results_RCTD)[
        ((doublet_results_RCTD$spot_class == "singlet" |
            doublet_results_RCTD$spot_class == "doublet_uncertain") &
            doublet_results_RCTD$first_type == celltype
        ) |
            (doublet_results_RCTD$spot_class == "doublet_certain" &
                (doublet_results_RCTD$first_type == celltype |
                    doublet_results_RCTD$second_type == celltype))
    ]

    if (length(spots_used) > 0 & sum(proportions_RCTD[spots_used, celltype]) > celltype_threshold) {
        if (length(spots_used) > sample_size_max) {
            spots_used <- sample(spots_used, size = sample_size_max, replace = FALSE)
        }

        mcube_object <- createMCube(
            counts = t(as.matrix(myRCTD@originalSpatialRNA@counts[, spots_used])),
            coordinates = as.matrix(myRCTD@spatialRNA@coords[spots_used, ]),
            proportions = proportions_RCTD[spots_used, ],
            library_sizes = myRCTD@spatialRNA@nUMI[spots_used],
            reference = t(myRCTD@cell_type_info$info[[1]]),
            used_for_deconvolution = rownames(myRCTD@spatialRNA@counts),
            spot_effects = spot_effects_RCTD[spots_used],
            celltype_test = celltype,
            # gene_test = gene_xenium,
            proportion_threshold = 0.01
        )
        mcube_object <- mcubeFitNull(
            mcube_object,
            num_workers = 70, num_threads = 1
        )
        mcube_object <- mcubeTest(
            mcube_object,
            num_workers = 70, num_threads = 1, shared_memory = TRUE
        )

        saveRDS(
            mcube_object,
            file = file.path(
                RESULT_PATH, "VisiumHD",
                paste0("mcube_", celltype, ".rds")
            )
        )
    }
}

Warning message in asMethod(object):
“sparse->dense coercion: allocating vector of size 1.3 GiB”
The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD4 T cell, CD8 Cytotoxic T cell, Endothelial, Fibroblast, Lymphatic Endothelial, Macrophage, Myofibroblast, Pericytes, Plasma, Proliferating Immune II, Tumor III, vSM pass the threshold.

Cell type(s) CAF will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 4898 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 1738 genes to analyze for CAF.

Preprocessed data description: 10000 spots and 32 cell types in total. 10000 spots, 1738 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0242776438178797 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0343337731497088 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0242776438178797 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0343337731497088 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD4 T cell, Macrophage, Tumor III pass the threshold.

Cell type(s) CD4 T cell will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5461 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 3740 genes to analyze for CD4 T cell.

Preprocessed data description: 2510 spots and 32 cell types in total. 2510 spots, 3740 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.024317191296763 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0343897017307033 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.024317191296763 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0343897017307033 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD8 Cytotoxic T cell, Macrophage, Tumor III, vSM pass the threshold.

Cell type(s) CD8 Cytotoxic T cell will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5415 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 2304 genes to analyze for CD8 Cytotoxic T cell.

Preprocessed data description: 1693 spots and 32 cell types in total. 1693 spots, 2304 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0454713221227028 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.064306160444962 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0454713221227028 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.064306160444962 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

Warning message in asMethod(object):
“sparse->dense coercion: allocating vector of size 1.3 GiB”
The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD4 T cell, CD8 Cytotoxic T cell, Endothelial, Fibroblast, Goblet, Lymphatic Endothelial, Macrophage, Myofibroblast, Neutrophil, Pericytes, Plasma, Proliferating Immune II, Tumor III, vSM pass the threshold.

Cell type(s) Endothelial will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5412 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 2839 genes to analyze for Endothelial.

Preprocessed data description: 9881 spots and 32 cell types in total. 9881 spots, 2839 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0182651348293594 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0258308013942533 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0182651348293594 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0258308013942533 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) Enteric Glial pass the threshold.

Cell type(s) Enteric Glial will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5121 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 1526 genes to analyze for Enteric Glial.

Preprocessed data description: 427 spots and 32 cell types in total. 427 spots, 1526 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0318398464587105 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0450283426857854 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0318398464587105 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0450283426857854 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) Enterocyte, Goblet pass the threshold.

Cell type(s) Enterocyte will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 4501 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 1399 genes to analyze for Enterocyte.

Preprocessed data description: 1089 spots and 32 cell types in total. 1089 spots, 1399 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0484181739724365 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0684736382971597 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0484181739724365 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0684736382971597 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, Fibroblast, Macrophage, Plasma pass the threshold.

Cell type(s) Fibroblast will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 4754 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 2007 genes to analyze for Fibroblast.

Preprocessed data description: 1902 spots and 32 cell types in total. 1902 spots, 2007 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.038046065144733 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0538052613226117 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.038046065144733 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0538052613226117 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

Warning message in asMethod(object):
“sparse->dense coercion: allocating vector of size 1.3 GiB”
The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) Enterocyte, Goblet, Plasma, Tumor II, Tumor III, Tumor V pass the threshold.

Cell type(s) Goblet will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 4624 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 3731 genes to analyze for Goblet.

Preprocessed data description: 10000 spots and 32 cell types in total. 10000 spots, 3731 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.046925761556966 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0663630484185473 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.046925761556966 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0663630484185473 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, Lymphatic Endothelial pass the threshold.

Cell type(s) Lymphatic Endothelial will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5234 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 3090 genes to analyze for Lymphatic Endothelial.

Preprocessed data description: 1038 spots and 32 cell types in total. 1038 spots, 3090 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0269346909146216 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0380914051897853 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0269346909146216 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0380914051897853 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

Warning message in asMethod(object):
“sparse->dense coercion: allocating vector of size 1.3 GiB”
The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD4 T cell, CD8 Cytotoxic T cell, Endothelial, Fibroblast, Goblet, Macrophage, Myofibroblast, Neutrophil, Pericytes, Plasma, Proliferating Immune II, Tumor II, Tumor III, Tumor V, vSM pass the threshold.

Cell type(s) Macrophage will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5217 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 2118 genes to analyze for Macrophage.

Preprocessed data description: 10000 spots and 32 cell types in total. 10000 spots, 2118 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0164824525447589 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.023309707929969 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0164824525447589 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.023309707929969 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, Endothelial, Neutrophil, Tumor III, Tumor V pass the threshold.

Cell type(s) Neutrophil will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5046 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 2683 genes to analyze for Neutrophil.

Preprocessed data description: 3284 spots and 32 cell types in total. 3284 spots, 2683 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.017751449728357 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0251043409576267 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.017751449728357 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0251043409576267 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, Endothelial, Macrophage, Pericytes, Tumor III, vSM pass the threshold.

Cell type(s) Pericytes will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5358 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 3355 genes to analyze for Pericytes.

Preprocessed data description: 3210 spots and 32 cell types in total. 3210 spots, 3355 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0172066214140663 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0243338373663919 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0172066214140663 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0243338373663919 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD4 T cell, Endothelial, Fibroblast, Goblet, Macrophage, Plasma pass the threshold.

Cell type(s) Plasma will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 3035 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 891 genes to analyze for Plasma.

Preprocessed data description: 5349 spots and 32 cell types in total. 5349 spots, 891 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0281847531997345 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0398592602272031 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0281847531997345 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0398592602272031 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD4 T cell, Endothelial, Macrophage, Plasma, Proliferating Immune II pass the threshold.

Cell type(s) Proliferating Immune II will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 4900 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 3412 genes to analyze for Proliferating Immune II.

Preprocessed data description: 2218 spots and 32 cell types in total. 2218 spots, 3412 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0161268898278666 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0228068663134656 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0161268898278666 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0228068663134656 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) Tumor II, Tumor III pass the threshold.

Cell type(s) Tumor II will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5008 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 235 genes to analyze for Tumor II.

Preprocessed data description: 559 spots and 32 cell types in total. 559 spots, 235 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0286043653326928 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.040452681396569 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0286043653326928 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.040452681396569 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

Warning message in asMethod(object):
“sparse->dense coercion: allocating vector of size 1.3 GiB”
The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, Macrophage, Tumor III, Tumor V pass the threshold.

Cell type(s) Tumor III will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5480 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 4837 genes to analyze for Tumor III.

Preprocessed data description: 10000 spots and 32 cell types in total. 10000 spots, 4837 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0229406616346267 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0324429948135012 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0229406616346267 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0324429948135012 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) Goblet, Tumor III, Tumor V pass the threshold.

Cell type(s) Tumor V will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 4831 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 1049 genes to analyze for Tumor V.

Preprocessed data description: 475 spots and 32 cell types in total. 475 spots, 1049 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0235365220297113 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0332856686655108 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0235365220297113 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0332856686655108 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) Unknown III (SM) pass the threshold.

Cell type(s) Unknown III (SM) will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 5050 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 1247 genes to analyze for Unknown III (SM).

Preprocessed data description: 514 spots and 32 cell types in total. 514 spots, 1247 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0469775856808376 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0664363387973846 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0469775856808376 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0664363387973846 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

Warning message in asMethod(object):
“sparse->dense coercion: allocating vector of size 1.2 GiB”
The batch_id is not provided!
All spots are assumed to be from the same batch and share the same gene platform effects.

Select high-abundance cell types to analyze with proportion_threshold = 0.01 and celltype_threshold = 100.

mcubeFilterCellTypes: Cell type(s) CAF, CD4 T cell, CD8 Cytotoxic T cell, Endothelial, Enteric Glial, Fibroblast, Macrophage, Pericytes, Plasma, Smooth Muscle, Tumor III, Unknown III (SM), vSM pass the threshold.

Cell type(s) vSM will be analyzed.

Filter out lowly-expressed genes with gene_threshold = 5e-05.

mcubeFilterGenes: 4625 genes pass the threshold.

The platform effects are not provided and need to be estimated from data!

Select highly-expressed genes to analyze for each specific cell type with reference_threshold = 0.5.

mcubeFilterGenesCellType: Select 2158 genes to analyze for vSM.

Preprocessed data description: 9142 spots and 32 cell types in total. 9142 spots, 2158 genes, and 1 cell type(s) to analyze.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.

mcubeKernel: length_scale is set as 0.0446385383593204 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0631284263522625 for the Gaussian kernel.

mcubeKernel: length_scale is set as 0.0446385383593204 for the Gaussian_transformed kernel.

mcubeKernel: length_scale is set as 0.0631284263522625 for the Gaussian_transformed kernel.

Number of physical cores: 72.

Number of workers: 70.

Number of thread(s) on BLAS per worker: 1.