library(tidyverse)

data <- read_csv("data.csv") %>%
  pivot_longer(cols=c(2:length(.)), names_to = "cell_line", values_to = "log2FC")

colnames(data)[1] = "gene"

gene_order <- data |> group_by(gene) |> summarise(median = median(log2FC)) |>
  arrange(median) |> pull(gene)

data |>
  mutate(gene = factor(gene, levels=gene_order)) |>
  ggplot(aes(gene, log2FC, group=gene, color=cell_line)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "#34495e") +
  geom_point() +
  stat_summary(fun = median, fun.min = median, fun.max = median,
               geom = "crossbar", width = 0.5, show.legend = FALSE) +
  scale_color_brewer(name = "Cell line", palette = "Set1") +
  theme_bw() +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.major.x = element_blank()
  )

ggsave("5.png", width = 6, height = 4)

library(tidyverse)
library(Seurat)

# load a single cell expression data set (generated in the lab I work at)
seurat <- readRDS('seurat.rds')

# cells will be grouped by clusters that they have been assigned to
cluster_ids <- levels(seurat@meta.data$seurat_clusters)

# select a set of genes for which we want to show expression
genes_to_show <- seurat@misc$marker_genes$by_cluster %>%
  group_by(cluster) %>%
  arrange(p_val_adj) %>%
  slice(1) %>%
  pull(gene)

# for every cluster-gene combination, calculate the average expression across
# all cells and then transform the data into a data frame
expression_levels_per_cluster <- vapply(
    cluster_ids, FUN.VALUE = numeric(length(cluster_ids)), function(x) {
      cells_in_current_cluster <- which(seurat@meta.data$seurat_cluster == x)
      Matrix::rowMeans(seurat@assays$SCT@data[genes_to_show,cells_in_current_cluster])
    }
  ) %>%
  t() %>%
  as.data.frame() %>%
  mutate(cluster = rownames(.)) %>%
  select(cluster, everything()) %>%
  pivot_longer(
    cols = c(2:ncol(.)),
    names_to = 'gene'
  ) %>%
  rename(expression = value) %>%
  mutate(id_to_merge = paste0(cluster, '_', gene))

# for every cluster-gene combination, calculate the percentage of cells in the
# respective group that has at least 1 transcript (this means we consider it
# as expressing the gene) and then transform the data into a data frame
percentage_of_cells_expressing_gene <- vapply(
    cluster_ids, FUN.VALUE = numeric(length(cluster_ids)), function(x) {
      cells_in_current_cluster <- which(seurat@meta.data$seurat_cluster == x)
      Matrix::rowSums(seurat@assays$SCT@data[genes_to_show,cells_in_current_cluster] != 0)
    }
  ) %>%
  t() %>%
  as.data.frame() %>%
  mutate(cluster = rownames(.)) %>%
  select(cluster, everything()) %>%
  pivot_longer(
    cols = c(2:ncol(.)),
    names_to = 'gene'
  ) %>%
  rename(cell_count = value) %>%
  left_join(
    .,
    seurat@meta.data %>%
      group_by(seurat_clusters) %>%
      tally() %>%
      rename(cluster = seurat_clusters),
    by = 'cluster') %>%
  mutate(
    id_to_merge = paste0(cluster, '_', gene),
    percent_cells = cell_count / n
  )

# merge the two data frames created before and plot the data
p <- left_join(
    expression_levels_per_cluster,
    percentage_of_cells_expressing_gene %>% select(id_to_merge, percent_cells),
    by = 'id_to_merge'
  ) %>%
  mutate(cluster = factor(cluster, levels = rev(cluster_ids))) %>%
  ggplot(aes(gene, cluster)) +
  geom_point(aes(color = expression, size = percent_cells)) +
  scale_color_distiller(
    palette = 'Reds',
    direction = 1,
    name = 'Log-normalised\nexpression',
    guide = guide_colorbar(frame.colour = "black", ticks.colour = "black")
  ) +
  scale_size(name = 'Percent\nof cells', labels = scales::percent) +
  labs(y = 'Cluster', color = 'Expression') +
  coord_fixed() +
  theme_bw() +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )
ggsave('4.png', p, height = 7, width = 8)

library(tidyverse)
library(Seurat)
library(ComplexHeatmap)
library(circlize)
library(gridExtra)
library(ggplotify)

# create color palette from flatuicolors.com
colors_dutch <- c(
  '#FFC312','#C4E538','#12CBC4','#FDA7DF','#ED4C67',
  '#F79F1F','#A3CB38','#1289A7','#D980FA','#B53471',
  '#EE5A24','#009432','#0652DD','#9980FA','#833471',
  '#EA2027','#006266','#1B1464','#5758BB','#6F1E51'
)
colors_spanish <- c(
  '#40407a','#706fd3','#f7f1e3','#34ace0','#33d9b2',
  '#2c2c54','#474787','#aaa69d','#227093','#218c74',
  '#ff5252','#ff793f','#d1ccc0','#ffb142','#ffda79',
  '#b33939','#cd6133','#84817a','#cc8e35','#ccae62'
)
custom_colors <- c(colors_dutch, colors_spanish)

# load a single cell expression data set (generated in the lab I work at)
seurat <- readRDS('seurat.rds')

# calculate average expression value for all variable genes for each cluster
average_expression_profiles_by_cluster <- seurat@assays$RNA@data[seurat@assays$RNA@var.features,] %>%
  t() %>%
  as.matrix() %>%
  as_tibble() %>%
  mutate(cluster = seurat@meta.data$seurat_clusters) %>%
  select(cluster, everything()) %>%
  group_by(cluster) %>%
  summarize_all(~mean(.))

# calculate Spearman correlation matrix
correlation_matrix <- average_expression_profiles_by_cluster %>%
  select(-1) %>%
  as.matrix() %>%
  t() %>%
  cor(method = 'spearman')

# assign row and column names
rownames(correlation_matrix) <- levels(seurat@meta.data$seurat_clusters)
colnames(correlation_matrix) <- levels(seurat@meta.data$seurat_clusters)

# save cluster names for later
cluster <- rownames(correlation_matrix)

# assign a color to each cluster
colors_for_clusters <- c(custom_colors$discrete[1:length(cluster)])
names(colors_for_clusters) <- cluster

# create annotation function
func_cell_cluster <- function(i, j, x, y, width, height, fill) {
  grid.text(cluster[j], x = x, y = y, gp = gpar(fontsize = 8))
}

# create main heatmap
ht_matrix <- Heatmap(
  correlation_matrix,
  name = 'Spearman\ncorrelation',
  col = colorRamp2(
    c(-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1),
    c('#00007F', 'blue', '#007FFF', 'cyan', '#7FFF7F', 'yellow', '#FF7F00', 'red', '#7F0000')
  ),
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  show_row_names = TRUE,
  show_column_names = TRUE,
  heatmap_legend_param = list(
    title = 'Spearman correlation',
    legend_height = unit(6, 'cm'),
    legend_width = unit(1, 'cm'),
    title_position = 'lefttop-rot',
    border = 'black'
  )
)

# create heatmap for annotation of clusters
ht_cluster <- Heatmap(
  cluster,
  name = 'Cluster',
  cell_fun = func_cell_cluster,
  show_row_names = FALSE,
  show_column_names = FALSE,
  width = unit(15, 'mm'),
  col = colors_for_clusters,
  show_heatmap_legend = FALSE,
  top_annotation = HeatmapAnnotation(
    cn = anno_text('Cluster', rot = 0, just = 'center', gp = gpar(fontface = 'bold')),
    height = max_text_height('Cluster')
  )
)

# plot
p <- as.ggplot(grid.grabExpr(draw(ht_matrix + ht_cluster)))
ggsave('1.png', p, height = 6, width = 7)

library(tidyverse)
library(wesanderson)

data(iris)

p <- ggplot(iris) +
  stat_density_2d(aes(Sepal.Length, Sepal.Width, fill = stat(level)), geom = 'polygon') +
  theme_bw() +
  labs(x = 'Sepal length', y = 'Sepal width', size = 'Petal width') +
  scale_fill_gradientn(
    colours = wes_palette('Zissou1', 21, type = 'continuous'),
    guide = guide_colorbar(frame.colour = 'black', ticks.colour = 'black')
  ) +
  theme(legend.position = 'right')
ggsave('1.png', p, height = 5, width = 6)