Comparison of MCube results across multiple CRC datasets focusing on CAF and Macrophage

[1]:

set.seed(20250502)

library(Matrix)
library(spacexr)
library(MCube)
library(ggplot2)
palettes <- c("#32CD32", "#FF69B4")

[2]:

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

We compare the results from MCube on different ST CRC datasets. We focus on the cancer associated fibroblasts (CAFs) and macrophages to study the tumor microenvironment (TME). In the tumor boundary region, CAFs are the most prominent cell type, while macrophages are consistently identified as the most abundant immune cell type.

[3]:

celltypes_tme <- c("CAF", "Macrophage")

10x Visium

[4]:

tech <- "Visium"

Cell type deconvolution using RCTD

[5]:

myRCTD <- readRDS(file.path(RESULT_PATH, tech, "myRCTD.rds"))
weights_RCTD <- as.matrix(myRCTD@results$weights)
proportions_RCTD <- weights_RCTD / rowSums(weights_RCTD)

[6]:

for (celltype in celltypes_tme) {
    p <- mcubePlotPropCellType(
        proportions_RCTD, as.matrix(myRCTD@spatialRNA@coords), celltype
    ) + labs(title = NULL, x = NULL, y = NULL) +
        guides(color = guide_colorbar(barheight = 15)) +
        theme(
            text = element_text(family = "Helvetica"),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
    ggsave(
        filename = file.path(
            RESULT_PATH,
            paste0(tech, "_proportion_", celltype, ".pdf")
        ),
        plot = p, width = 7, height = 5
    )
    ggsave(
        filename = file.path(
            RESULT_PATH,
            paste0(tech, "_proportion_", celltype, ".png")
        ),
        plot = p, width = 7, height = 5
    )
}
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_9_0.png

Cell-type-specific SVG identification using MCube

[7]:

spot_names <- colnames(myRCTD@spatialRNA@counts)
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)

[8]:

# mcube_object <- createMCube(
#     counts = t(as.matrix(myRCTD@originalSpatialRNA@counts[, spot_names])),
#     coordinates = as.matrix(myRCTD@spatialRNA@coords),
#     proportions = proportions_RCTD,
#     library_sizes = myRCTD@spatialRNA@nUMI,
#     reference = t(myRCTD@cell_type_info$info[[1]]),
#     used_for_deconvolution = rownames(myRCTD@spatialRNA@counts),
#     spot_effects = spot_effects_RCTD,
#     celltype_threshold = 50
# )
# mcube_object <- mcubeFitNull(
#     mcube_object,
#     num_workers = 36, num_threads = 1
# )
# mcube_object <- mcubeTest(
#     mcube_object,
#     num_workers = 36, num_threads = 1, shared_memory = TRUE
# )

# saveRDS(
#     mcube_object,
#     file = file.path(
#         RESULT_PATH, "Visium",
#         paste0("mcube", ".rds")
#     )
# )

[9]:

mcube_object <- readRDS(
    file.path(
        RESULT_PATH, tech,
        paste0("mcube", ".rds")
    )
)

SPP1 in Macrophage

[10]:

celltype <- "Macrophage"
gene <- "SPP1"
mcube_object@pvalues[[celltype]][gene, ]
A data.frame: 1 × 6
linearGaussian_1Gaussian_2Gaussian_transformed_1Gaussian_transformed_2combined_pvalue
<dbl><dbl><dbl><dbl><dbl><dbl>
SPP12.313769e-131.129061e-608.640312e-562.370429e-065.905008e-111.129061e-60
 
[11]:

null_model_results <- mcube_object@null_models[[paste0(celltype, "_", gene)]]

spots_target <- null_model_results$spots
expr_level <- null_model_results$u[spots_target, celltype]
expr_level <- factor(ifelse(expr_level >= 0, "Higher", "Lower"))
target_df <- data.frame(
    x = mcube_object@coordinates[spots_target, 1],
    y = mcube_object@coordinates[spots_target, 2],
    expr_level = expr_level
)

spots_background <- setdiff(rownames(proportions_RCTD), spots_target)
tumor_df <- cbind(
    myRCTD@spatialRNA@coords[spots_background, ],
    Tumor = proportions_RCTD[spots_background, "Tumor III"]
)

p <- ggplot() +
    geom_point(
        data = tumor_df,
        aes(x = x, y = y, alpha = Tumor),
        color = "bisque", size = 1
    ) +
    geom_point(
        data = target_df,
        aes(x = x, y = y, color = expr_level),
        size = 1, alpha = 0.8
    ) +
    scale_color_manual(
      name = bquote(italic(.(gene))),
      values = c("Lower" = palettes[1], "Higher" = palettes[2])
    ) +
    scale_alpha_continuous(
        name = "Tumor",
        range = c(0, 1),
        breaks = c(0.5, 0.7, 0.9)
    ) +
    guides(
        color = guide_legend(order = 1, override.aes = list(size = 5)),
        alpha = guide_legend(order = 2, override.aes = list(size = 5))
    ) +
    scale_y_continuous(trans = "reverse") +
        coord_fixed(ratio = 1) +
        labs(title = NULL, x = NULL, y = NULL) +
        theme_classic() +
        theme(
            text = element_text(family = "Helvetica"),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.title = element_text(size = 24),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".pdf")
    ),
    plot = p, width = 7, height = 5
)
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".png")
    ),
    plot = p, width = 7, height = 5
)
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_16_0.png

MMP11 in CAF

[12]:

celltype <- "CAF"
gene <- "MMP11"
mcube_object@pvalues[[celltype]][gene, ]
A data.frame: 1 × 6
linearGaussian_1Gaussian_2Gaussian_transformed_1Gaussian_transformed_2combined_pvalue
<dbl><dbl><dbl><dbl><dbl><dbl>
MMP112.434843e-273.495729e-1362.424947e-1252.446646e-197.566108e-293.495729e-136
 
[13]:

null_model_results <- mcube_object@null_models[[paste0(celltype, "_", gene)]]

spots_target <- null_model_results$spots
expr_level <- null_model_results$u[spots_target, celltype]
expr_level <- factor(ifelse(expr_level >= 0, "Higher", "Lower"))
target_df <- data.frame(
    x = mcube_object@coordinates[spots_target, 1],
    y = mcube_object@coordinates[spots_target, 2],
    expr_level = expr_level
)

spots_background <- setdiff(rownames(proportions_RCTD), spots_target)
tumor_df <- cbind(
    myRCTD@spatialRNA@coords[spots_background, ],
    Tumor = proportions_RCTD[spots_background, "Tumor III"]
)

p <- ggplot() +
    geom_point(
        data = tumor_df,
        aes(x = x, y = y, alpha = Tumor),
        color = "bisque", size = 1
    ) +
    geom_point(
        data = target_df,
        aes(x = x, y = y, color = expr_level),
        size = 1, alpha = 0.8
    ) +
    scale_color_manual(
      name = bquote(italic(.(gene))),
      values = c("Lower" = palettes[1], "Higher" = palettes[2])
    ) +
    scale_alpha_continuous(
        name = "Tumor",
        range = c(0, 1),
        breaks = c(0.5, 0.7, 0.9)
    ) +
    guides(
        color = guide_legend(order = 1, override.aes = list(size = 5)),
        alpha = guide_legend(order = 2, override.aes = list(size = 5))
    ) +
    scale_y_continuous(trans = "reverse") +
        coord_fixed(ratio = 1) +
        labs(title = NULL, x = NULL, y = NULL) +
        theme_classic() +
        theme(
            text = element_text(family = "Helvetica"),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.title = element_text(size = 24),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".pdf")
    ),
    plot = p, width = 7, height = 5
)
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".png")
    ),
    plot = p, width = 7, height = 5
)
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_19_0.png

10x Visium HD

[14]:

tech <- "VisiumHD"

Cell type deconvolution using RCTD

[15]:

myRCTD <- readRDS(file.path(RESULT_PATH, tech, "myRCTD_16um.rds"))
weights_RCTD <- as.matrix(myRCTD@results$weights)
proportions_RCTD <- weights_RCTD / rowSums(weights_RCTD)

[16]:

for (celltype in celltypes_tme) {
    p <- mcubePlotPropCellType(
        proportions_RCTD, as.matrix(myRCTD@spatialRNA@coords),
        celltype,
        spot_size = 0.01
    ) + labs(title = NULL, x = NULL, y = NULL) +
        guides(color = guide_colorbar(barheight = 15)) +
        theme(
            text = element_text(family = "Helvetica"),
            plot.title = element_text(size = 24, hjust = 0.5),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
    ggsave(
        filename = file.path(
            RESULT_PATH,
            paste0(tech, "_proportion_", celltype, ".pdf")
        ),
        plot = p, width = 7, height = 5
    )
    ggsave(
        filename = file.path(
            RESULT_PATH,
            paste0(tech, "_proportion_", celltype, ".png")
        ),
        plot = p, width = 7, height = 5
    )
}
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_24_0.png

Cell-type-specific SVG identification using MCube

[17]:

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

SPP1 in Macrophage

[18]:

celltype <- "Macrophage"
gene <- "SPP1"

[19]:

mcube_object <- readRDS(
    file.path(
        RESULT_PATH, tech,
        paste0("mcube_", celltype, ".rds")
    )
)
mcube_object@pvalues[[celltype]][gene, ]
A data.frame: 1 × 6
linearGaussian_1Gaussian_2Gaussian_transformed_1Gaussian_transformed_2combined_pvalue
<dbl><dbl><dbl><dbl><dbl><dbl>
SPP14.492731e-1132.225074e-3082.225074e-3081.309304e-102.372605e-122.225074e-308
 
[20]:

null_model_results <- mcube_object@null_models[[paste0(celltype, "_", gene)]]

spots_target <- null_model_results$spots
expr_level <- null_model_results$u[spots_target, celltype]
expr_level <- factor(ifelse(expr_level >= 0, "Higher", "Lower"))
target_df <- data.frame(
    x = mcube_object@coordinates[spots_target, 1],
    y = mcube_object@coordinates[spots_target, 2],
    expr_level = expr_level
)

spots_background <- setdiff(rownames(proportions_RCTD), spots_target)
tumor_df <- cbind(
    myRCTD@spatialRNA@coords[spots_background, ],
    Tumor = proportions_RCTD[spots_background, "Tumor III"]
)
tumor_df <- tumor_df[sample(1:nrow(tumor_df), 30000), ]

p <- ggplot() +
    geom_point(
        data = tumor_df,
        aes(x = x, y = y, alpha = Tumor),
        color = "bisque", size = 0.1
    ) +
    geom_point(
        data = target_df,
        aes(x = x, y = y, color = expr_level),
        size = 0.1, alpha = 0.7
    ) +
    scale_color_manual(
      name = bquote(italic(.(gene))),
      values = c("Lower" = palettes[1], "Higher" = palettes[2])
    ) +
    scale_alpha_continuous(
        name = "Tumor",
        range = c(0, 1),
        breaks = c(0.5, 0.7, 0.9)
    ) +
    guides(
        color = guide_legend(order = 1, override.aes = list(size = 5)),
        alpha = guide_legend(order = 2, override.aes = list(size = 5))
    ) +
    scale_y_continuous(trans = "reverse") +
        coord_fixed(ratio = 1) +
        labs(title = NULL, x = NULL, y = NULL) +
        theme_classic() +
        theme(
            text = element_text(family = "Helvetica"),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.title = element_text(size = 24),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".pdf")
    ),
    plot = p, width = 7, height = 5
)
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".png")
    ),
    plot = p, width = 7, height = 5
)
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_30_0.png

MMP11 in CAF

[21]:

celltype <- "CAF"
gene <- "MMP11"

[22]:

mcube_object <- readRDS(
    file.path(
        RESULT_PATH, tech,
        paste0("mcube_", celltype, ".rds")
    )
)
mcube_object@pvalues[[celltype]][gene, ]
A data.frame: 1 × 6
linearGaussian_1Gaussian_2Gaussian_transformed_1Gaussian_transformed_2combined_pvalue
<dbl><dbl><dbl><dbl><dbl><dbl>
MMP111.695596e-162.225074e-3082.225074e-3080.38937250.32225632.225074e-308
 
[23]:

null_model_results <- mcube_object@null_models[[paste0(celltype, "_", gene)]]

spots_target <- null_model_results$spots
expr_level <- null_model_results$u[spots_target, celltype]
expr_level <- factor(ifelse(expr_level >= 0, "Higher", "Lower"))
target_df <- data.frame(
    x = mcube_object@coordinates[spots_target, 1],
    y = mcube_object@coordinates[spots_target, 2],
    expr_level = expr_level
)

spots_background <- setdiff(rownames(proportions_RCTD), spots_target)
tumor_df <- cbind(
    myRCTD@spatialRNA@coords[spots_background, ],
    Tumor = proportions_RCTD[spots_background, "Tumor III"]
)
tumor_df <- tumor_df[sample(1:nrow(tumor_df), 30000), ]

p <- ggplot() +
    geom_point(
        data = tumor_df,
        aes(x = x, y = y, alpha = Tumor),
        color = "bisque", size = 0.1
    ) +
    geom_point(
        data = target_df,
        aes(x = x, y = y, color = expr_level),
        size = 0.1, alpha = 0.7
    ) +
    scale_color_manual(
      name = bquote(italic(.(gene))),
      values = c("Lower" = palettes[1], "Higher" = palettes[2])
    ) +
    scale_alpha_continuous(
        name = "Tumor",
        range = c(0, 1),
        breaks = c(0.5, 0.7, 0.9)
    ) +
    guides(
        color = guide_legend(order = 1, override.aes = list(size = 5)),
        alpha = guide_legend(order = 2, override.aes = list(size = 5))
    ) +
    scale_y_continuous(trans = "reverse") +
        coord_fixed(ratio = 1) +
        labs(title = NULL, x = NULL, y = NULL) +
        theme_classic() +
        theme(
            text = element_text(family = "Helvetica"),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.title = element_text(size = 24),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".pdf")
    ),
    plot = p, width = 7, height = 5
)
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".png")
    ),
    plot = p, width = 7, height = 5
)
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_34_0.png

10x Xenium

[24]:

tech <- "Xenium"

Cell type deconvolution using RCTD

[25]:

myRCTD <- readRDS(file.path(RESULT_PATH, tech, "myRCTD.rds"))
weights_RCTD <- as.matrix(myRCTD@results$weights)
proportions_RCTD <- weights_RCTD / rowSums(weights_RCTD)

[26]:

for (celltype in celltypes_tme) {
    p <- mcubePlotPropCellType(
        proportions_RCTD, as.matrix(myRCTD@spatialRNA@coords),
        celltype,
        spot_size = 0.01
    ) + labs(title = NULL, x = NULL, y = NULL) +
        guides(color = guide_colorbar(barheight = 15)) +
        theme(
            text = element_text(family = "Helvetica"),
            plot.title = element_text(size = 24, hjust = 0.5),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
    ggsave(
        filename = file.path(
            RESULT_PATH,
            paste0(tech, "_proportion_", celltype, ".pdf")
        ),
        plot = p, width = 7, height = 5
    )
    ggsave(
        filename = file.path(
            RESULT_PATH,
            paste0(tech, "_proportion_", celltype, ".png")
        ),
        plot = p, width = 7, height = 5
    )
}
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_39_0.png

Cell-type-specific SVG identification using MCube

[27]:

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

SPP1 in Macrophage

[28]:

celltype <- "Macrophage"
gene <- "SPP1"

[29]:

mcube_object <- readRDS(
    file.path(
        RESULT_PATH, tech,
        paste0("mcube_", celltype, ".rds")
    )
)
mcube_object@pvalues[[celltype]][gene, ]
A data.frame: 1 × 6
linearGaussian_1Gaussian_2Gaussian_transformed_1Gaussian_transformed_2combined_pvalue
<dbl><dbl><dbl><dbl><dbl><dbl>
SPP16.962033e-1292.225074e-3082.225074e-3080.082962630.022580162.225074e-308
 
[30]:

null_model_results <- mcube_object@null_models[[paste0(celltype, "_", gene)]]

spots_target <- null_model_results$spots
expr_level <- null_model_results$u[spots_target, celltype]
expr_level <- factor(ifelse(expr_level >= 0, "Higher", "Lower"))
target_df <- data.frame(
    x = mcube_object@coordinates[spots_target, 1],
    y = mcube_object@coordinates[spots_target, 2],
    expr_level = expr_level
)

spots_background <- setdiff(rownames(proportions_RCTD), spots_target)
tumor_df <- cbind(
    myRCTD@spatialRNA@coords[spots_background, ],
    Tumor = proportions_RCTD[spots_background, "Tumor III"]
)
tumor_df <- tumor_df[sample(1:nrow(tumor_df), 30000), ]

p <- ggplot() +
    geom_point(
        data = tumor_df,
        aes(x = x, y = y, alpha = Tumor),
        color = "bisque", size = 0.1
    ) +
    geom_point(
        data = target_df,
        aes(x = x, y = y, color = expr_level),
        size = 0.1, alpha = 0.7
    ) +
    scale_color_manual(
      name = bquote(italic(.(gene))),
      values = c("Lower" = palettes[1], "Higher" = palettes[2])
    ) +
    scale_alpha_continuous(
        name = "Tumor",
        range = c(0, 1),
        breaks = c(0.5, 0.7, 0.9)
    ) +
    guides(
        color = guide_legend(order = 1, override.aes = list(size = 5)),
        alpha = guide_legend(order = 2, override.aes = list(size = 5))
    ) +
    scale_y_continuous(trans = "reverse") +
        coord_fixed(ratio = 1) +
        labs(title = NULL, x = NULL, y = NULL) +
        theme_classic() +
        theme(
            text = element_text(family = "Helvetica"),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.title = element_text(size = 24),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".pdf")
    ),
    plot = p, width = 7, height = 5
)
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".png")
    ),
    plot = p, width = 7, height = 5
)
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_45_0.png

MMP11 in CAF

[31]:

celltype <- "CAF"
gene <- "MMP11"

[32]:

mcube_object <- readRDS(
    file.path(
        RESULT_PATH, tech,
        paste0("mcube_", celltype, ".rds")
    )
)
mcube_object@pvalues[[celltype]][gene, ]
A data.frame: 1 × 6
linearGaussian_1Gaussian_2Gaussian_transformed_1Gaussian_transformed_2combined_pvalue
<dbl><dbl><dbl><dbl><dbl><dbl>
MMP110.16629072.225074e-3082.225074e-3080.0086684990.0013173512.225074e-308
 
[33]:

null_model_results <- mcube_object@null_models[[paste0(celltype, "_", gene)]]

spots_target <- null_model_results$spots
expr_level <- null_model_results$u[spots_target, celltype]
expr_level <- factor(ifelse(expr_level >= 0, "Higher", "Lower"))
target_df <- data.frame(
    x = mcube_object@coordinates[spots_target, 1],
    y = mcube_object@coordinates[spots_target, 2],
    expr_level = expr_level
)

spots_background <- setdiff(rownames(proportions_RCTD), spots_target)
tumor_df <- cbind(
    myRCTD@spatialRNA@coords[spots_background, ],
    Tumor = proportions_RCTD[spots_background, "Tumor III"]
)
tumor_df <- tumor_df[sample(1:nrow(tumor_df), 30000), ]

p <- ggplot() +
    geom_point(
        data = tumor_df,
        aes(x = x, y = y, alpha = Tumor),
        color = "bisque", size = 0.1
    ) +
    geom_point(
        data = target_df,
        aes(x = x, y = y, color = expr_level),
        size = 0.1, alpha = 0.7
    ) +
    scale_color_manual(
      name = bquote(italic(.(gene))),
      values = c("Lower" = palettes[1], "Higher" = palettes[2])
    ) +
    scale_alpha_continuous(
        name = "Tumor",
        range = c(0, 1),
        breaks = c(0.5, 0.7, 0.9)
    ) +
    guides(
        color = guide_legend(order = 1, override.aes = list(size = 5)),
        alpha = guide_legend(order = 2, override.aes = list(size = 5))
    ) +
    scale_y_continuous(trans = "reverse") +
        coord_fixed(ratio = 1) +
        labs(title = NULL, x = NULL, y = NULL) +
        theme_classic() +
        theme(
            text = element_text(family = "Helvetica"),
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            legend.title = element_text(size = 24),
            legend.text = element_text(size = 24),
            legend.position = "right"
        )
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".pdf")
    ),
    plot = p, width = 7, height = 5
)
ggsave(
    filename = file.path(
        RESULT_PATH,
        paste0(tech, "_", celltype, "_", gene, ".png")
    ),
    plot = p, width = 7, height = 5
)
p
../../_images/analysis_CRC_CRC_MCube_CAF_Macrophage_49_0.png 
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