## ----include=FALSE------------------------------------------------------------
# Set path to plotly screenshot. We don't run the plotly code chunk as most servers do not have javascript libraries needed for interactive plotting
screenshot <- "../man/figures/plotly.png"

# The chunk below uses Rmd in man/fragments to avoid duplication, as the content is shared with the vignette and README. As suggested here: https://www.garrickadenbuie.com/blog/dry-vignette-and-readme/

visual_cue <- "../man/figures/logo_interaction-01.png"


## ----eval=FALSE---------------------------------------------------------------
#  # 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")

## ----eval=FALSE---------------------------------------------------------------
#  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)

## ----eval=FALSE---------------------------------------------------------------
#  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")

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

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

## ----eval=FALSE---------------------------------------------------------------
#  p5 =
#    PBMC_clean_scaled_UMAP_cluster_cell_type %>%
#    sample_n(1000) %>%
#    join_transcripts(transcripts=markers$gene) %>%
#    mutate(seurat_clusters = as.integer(seurat_clusters)) %>%
#    filter(seurat_clusters<10) %>%
#    group_by(seurat_clusters) %>%
#  
#    # Plot heatmap
#    heatmap(
#      .row = transcript,
#      .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)

## ----eval=FALSE---------------------------------------------------------------
#  p6 =
#    PBMC_clean_scaled_UMAP_cluster_cell_type %>%
#    tidyseurat::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)
#    )

