figures_article.R
In tidyseurat: Brings Seurat to the Tidyverse

## ----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_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

## ----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_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)

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