# Code for producing the figures in the article

## Stefano Mangiola

### 2025-11-10

    # Article workflow

    library(tidyverse)
    library(Seurat)
    library(SingleR)
    library(plotly)
    library(tidyHeatmap)
    library(ggalluvial)
    library(ggplot2)
    library(tidyseurat)
    options(future.globals.maxSize = 50068 * 1024^2)

    # Use colourblind-friendly colours
    friendly_cols <- dittoSeq::dittoColors()

    # Set theme
    custom_theme <-
      list(
        scale_fill_manual(values = friendly_cols),
        scale_color_manual(values = friendly_cols),
        theme_bw() +
          theme(
            panel.border = element_blank(),
            axis.line = element_line(),
            panel.grid.major = element_line(size = 0.2),
            panel.grid.minor = element_line(size = 0.1),
            text = element_text(size = 9),
            legend.position = "bottom",
            strip.background = element_blank(),
            axis.title.x = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)),
            axis.title.y = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)),
            axis.text.x = element_text(angle = 30, hjust = 1, vjust = 1)
          )
      )

    PBMC_clean_scaled_UMAP_cluster_cell_type <- readRDS("dev/PBMC_clean_scaled_UMAP_cluster_cell_type.rds")

    p1 =
      PBMC_clean_scaled_UMAP_cluster_cell_type %>%
      pivot_longer(
        c(mito.fraction, S.Score, G2M.Score),
        names_to="property",
        values_to="Value"
      ) %>%
      mutate(property =  factor(property, levels = c("mito.fraction", "G2M.Score", "S.Score"))) %>%
      ggplot(aes(sample, Value)) +
      geom_boxplot(outlier.size = 0.5 ) +
      facet_wrap(~property, scales = "free_y" ) +
      custom_theme +
      theme(aspect.ratio=1)

    p2 =
      PBMC_clean_scaled_UMAP_cluster_cell_type %>%
      sample_n(20000) %>%
      ggplot(aes(UMAP_1, UMAP_2, color=seurat_clusters)) +
      geom_point(size=0.05, alpha=0.2) +
      custom_theme +
      theme(aspect.ratio=1)

    PBMC_clean_scaled_UMAP_cluster_cell_type %>%
      sample_n(20000) %>%
      plot_ly(
        x = ~`UMAP_1`,
        y = ~`UMAP_2`,
        z = ~`UMAP_3`,
        color = ~seurat_clusters,
        colors = friendly_cols[1:24],sizes = 50, size = 1
      )

    markers = readRDS("dev/PBMC_marker_df.rds")

    p3 =
      PBMC_clean_scaled_UMAP_cluster_cell_type %>%
      arrange(first.labels) %>%
      mutate(seurat_clusters = fct_inorder(seurat_clusters)) %>%
      join_features(features=c("CD3D", "HLA-DRB1")) %>%
      ggplot(aes(y=seurat_clusters , x=.abundance_SCT, fill=first.labels)) +
      geom_density_ridges(bandwidth = 0.2) +
      facet_wrap(~ .feature, nrow = 2) +
      coord_flip() +
      custom_theme

    # Plot heatmap
    p4 =
      PBMC_clean_scaled_UMAP_cluster_cell_type %>%
      sample_n(2000) %>%
      DoHeatmap(
        features = markers$gene,
        group.colors = friendly_cols
      )

    p5 =
      PBMC_clean_scaled_UMAP_cluster_cell_type %>%
      sample_n(1000) %>%
      join_features(features=markers$gene) %>%
      mutate(seurat_clusters = as.integer(seurat_clusters)) %>%
      filter(seurat_clusters<10) %>%
      group_by(seurat_clusters) %>%

      # Plot heatmap
      heatmap(
        .row = .feature,
        .column = .cell,
        .value = .abundance_SCT,
        palette_grouping = list(rep("black",9)),
        palette_value = circlize::colorRamp2(c(-1.5, 0, 1.5), c("purple", "black", "yellow")),

        # ComplexHeatmap parameters
        row_gap = unit(0.1, "mm"), column_gap = unit(0.1, "mm")
      ) %>%

      # Add annotation
      add_tile(sample, palette = friendly_cols[1:7]) %>%
      add_point(PC_1)

    p6 =
      PBMC_clean_scaled_UMAP_cluster_cell_type %>%
      unite("cluster_cell_type", c(first.labels, seurat_clusters), remove=FALSE) %>%
      pivot_longer(
        c(seurat_clusters, first.labels_single),
        names_to = "classification", values_to = "value"
      ) %>%

      ggplot(aes(x = classification, stratum = value, alluvium = cell,
               fill = first.labels, label = value)) +
      scale_x_discrete(expand = c(1, 1)) +
      geom_flow() +
      geom_stratum(alpha = .5) +
      # geom_text(stat = "stratum", size = 3) +
      geom_text_repel(stat = "stratum", size = 3,
                 nudge_x      = 0.05,
                 direction    = "y",
                 angle        = 0,
                 vjust        = 0,
                 segment.size = 0.2
             ) +
      scale_fill_manual(values = friendly_cols) +
      #guides(fill = FALSE) +
      coord_flip() +
      theme_bw() +
      theme(
        panel.border = element_blank(),
        axis.line = element_line(),
        panel.grid.major = element_line(size = 0.2),
        panel.grid.minor = element_line(size = 0.1),
        text = element_text(size = 9),
        legend.position = "bottom",
        strip.background = element_blank(),
        axis.title.x = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)),
        axis.title.y = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)),
        axis.text.x = element_text(angle = 30, hjust = 1, vjust = 1)
      )