Last updated: 2018-12-05

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# scRNA-seq
library("Seurat")

# Plotting
library("clustree")
library("viridis")

# Presentation
library("glue")
library("knitr")

# Parallel
library("BiocParallel")

# Paths
library("here")

# Output
library("writexl")
library("jsonlite")

# Tidyverse
library("tidyverse")

source(here("R/output.R"))

orgs.path <- here("data/processed/Organoids_clustered.Rds")

bpparam <- MulticoreParam(workers = 10)

if (file.exists(orgs.path)) {
    orgs <- read_rds(orgs.path)
} else {
    stop("Clustered Organoids dataset is missing. ",
         "Please run '04_Organoids_Clustering.Rmd' first.",
         call. = FALSE)
}

clusters <- c(2, 9)
orgs.neph <- SubsetData(orgs, ident.use = clusters)
orgs.neph <- RunTSNE(orgs.neph, reduction.use = "cca.aligned", dims.use = 1:25)

# Clear old clustering
not.res <- !grepl("res\\.", colnames(orgs.neph@meta.data))
orgs.neph@meta.data <- orgs.neph@meta.data[, not.res]

n.dims <- 25
resolutions <- seq(0, 1, 0.1)
orgs.neph <- FindClusters(orgs.neph, reduction.type = "cca.aligned",
                          dims.use = 1:n.dims, resolution = resolutions,
                          force.recalc = TRUE)

src_list <- lapply(resolutions, function(res) {
    src <- c("#### Res {{res}} {.unnumbered}",
             "```{r cluster-tSNE-{{res}}}",
             "TSNEPlot(orgs.neph, group.by = 'res.{{res}}', do.return = TRUE)",  
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))

TSNEPlot(orgs.neph, group.by = 'res.0', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.1', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.2', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.3', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.4', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.5', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.6', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.7', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.8', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.0.9', do.return = TRUE)

TSNEPlot(orgs.neph, group.by = 'res.1', do.return = TRUE)

clustree(orgs.neph)

clustree(orgs.neph, node_colour = "sc3_stability")

genes <- c("PECAM1", "CDH5", "MEIS1", "PDGFRA", "HMGB2", "CENPA", "SIX1",
           "DAPL1", "NPHS1", "PODXL", "S100A8", "TYROBP", "MAL", "EMX2",
           "LRP2", "GATA3", "SLC12A1", "SPINT2", "TUBB2B", "STMN2", "TTYH1",
           "HBA1", "HBG1")

is_present <- genes %in% rownames(orgs.neph@data)

src_list <- lapply(genes[is_present], function(gene) {
    src <- c("##### {{gene}} {.unnumbered}",
             "```{r clustree-{{gene}}}",
             "clustree(orgs.neph, node_colour = '{{gene}}',",
                      "node_colour_aggr = 'mean',",
             "exprs = 'scale.data') +",
             "scale_colour_viridis_c(option = 'plasma', begin = 0.3)",
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))

clustree(orgs.neph, node_colour = 'PECAM1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'CDH5',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'MEIS1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'PDGFRA',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'HMGB2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'CENPA',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'SIX1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'DAPL1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'NPHS1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'PODXL',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'S100A8',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'TYROBP',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'MAL',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'EMX2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'LRP2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'GATA3',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'SLC12A1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'SPINT2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'TUBB2B',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'STMN2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'TTYH1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

clustree(orgs.neph, node_colour = 'HBA1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

res <- 0.5
orgs.neph <- SetIdent(orgs.neph,
                      ident.use = orgs.neph@meta.data[, paste0("res.", res)])
n.clusts <- length(unique(orgs.neph@ident))

p1 <- TSNEPlot(orgs.neph, do.return = TRUE, pt.size = 0.5,
               group.by = "DatasetSample")
p2 <- TSNEPlot(orgs.neph, do.label = TRUE, do.return = TRUE, pt.size = 0.5)
plot_grid(p1, p2)

plot.data <- orgs.neph@meta.data %>%
    select(Dataset, cluster = paste0("res.", res)) %>%
    mutate(cluster = factor(as.numeric(cluster))) %>%
    group_by(cluster, Dataset) %>%
    summarise(count = n()) %>%
    mutate(clust_total = sum(count)) %>%
    mutate(clust_prop = count / clust_total) %>%
    group_by(Dataset) %>%
    mutate(dataset_total = sum(count)) %>%
    ungroup() %>%
    mutate(dataset_prop = count / dataset_total)

ggplot(plot.data, aes(x = cluster, y = count, fill = Dataset)) +
    geom_col()

ggplot(plot.data, aes(x = cluster, y = clust_prop, fill = Dataset)) +
    geom_col()

ggplot(plot.data, aes(x = cluster, y = dataset_prop, fill = Dataset)) +
    geom_col(position = position_dodge(0.9))

markers <- bplapply(seq_len(n.clusts) - 1, function(cl) {
    cl.markers <- FindMarkers(orgs.neph, cl, logfc.threshold = 0, min.pct = 0.1,
                              print.bar = FALSE)
    cl.markers$cluster <- cl
    cl.markers$gene <- rownames(cl.markers)
    return(cl.markers)
}, BPPARAM = bpparam)

markers <- bind_rows(markers) %>%
    select(gene, cluster, everything())

markers %>% group_by(cluster) %>% top_n(2, abs(avg_logFC)) %>% data.frame

top <- markers %>% group_by(cluster) %>% top_n(5, avg_logFC)

cols <- viridis(100)[c(1, 50, 100)]

DoHeatmap(orgs.neph, genes.use = top$gene, slim.col.label = TRUE,
          remove.key = TRUE, col.low = cols[1], col.mid = cols[2],
          col.high = cols[3])

markers.list <- lapply(0:(n.clusts - 1), function(x) {
    markers %>%
        filter(cluster == x, p_val < 0.05) %>%
        dplyr::arrange(-avg_logFC) %>%
        select(Gene = gene, LogFC = avg_logFC, pVal = p_val)
})

names(markers.list) <- paste("Cluster", 0:(n.clusts - 1))

marker.summary <- markers.list %>%
    map2_df(names(markers.list), ~ mutate(.x, Cluster = .y)) %>%
    mutate(IsUp = LogFC > 0) %>%
    group_by(Cluster) %>%
    summarise(Up = sum(IsUp), Down = sum(!IsUp)) %>%
    mutate(Down = -Down) %>%
    gather(key = "Direction", value = "Count", -Cluster) %>%
    mutate(Cluster = factor(Cluster, levels = names(markers.list)))

ggplot(marker.summary,
       aes(x = fct_rev(Cluster), y = Count, fill = Direction)) +
    geom_col() +
    geom_text(aes(y = Count + sign(Count) * max(abs(Count)) * 0.07,
                  label = abs(Count)),
              size = 6, colour = "grey25") +
    coord_flip() +
    scale_fill_manual(values = c("#377eb8", "#e41a1c")) +
    ggtitle("Number of identified genes") +
    theme(axis.title = element_blank(),
          axis.line = element_blank(),
          axis.ticks = element_blank(),
          axis.text.x = element_blank(),
          legend.position = "bottom")

src_list <- lapply(0:(n.clusts - 1), function(i) {
    src <- c("### {{i}} {.unnumbered}",
             "```{r marker-cluster-{{i}}}",
             "markers.list[[{{i}} + 1]]",
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list),
                  options = list(echo = FALSE, cache = FALSE))