Last updated: 2018-12-05

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Expand here to see past versions:
    File Version Author Date Message
    Rmd bd6173a Luke Zappia 2018-12-05 Add gene ids to output files
    html 582acea Luke Zappia 2018-12-03 Fix DE results summary plot cluster labels
    Rmd d79b8e7 Luke Zappia 2018-11-20 Create DE signature and filter results
    html d79b8e7 Luke Zappia 2018-11-20 Create DE signature and filter results
    html a61f9c9 Luke Zappia 2018-09-13 Rebuild site
    html ad10b21 Luke Zappia 2018-09-13 Switch to GitHub
    Rmd 8fc820c Luke Zappia 2018-09-06 Fix C7 C15 DE output files
    Rmd 6d6cabf Luke Zappia 2018-09-03 Fix output file links
    Rmd 46fe054 Luke Zappia 2018-09-03 Add C7 C15 DE
    Rmd bff4d5b Luke Zappia 2018-08-14 Add crossover document


# scRNA-seq
library("Seurat")
library("limma")

# 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"))
combined.path <- here("data/processed/Combined_Seurat.Rds")
bpparam <- MulticoreParam(workers = 10)

Introduction

In this document we are going to load the combined Organoids and Linstrom dataset and complete the rest of the Seurat analysis.

if (file.exists(combined.path)) {
    combined <- read_rds(combined.path)
} else {
    stop("Combined dataset is missing. ",
         "Please run '06_Combined_Integration.Rmd' first.",
         call. = FALSE)
}

Clustering

Selecting resolution

n.dims <- 20
resolutions <- seq(0, 1, 0.1)
combined <- FindClusters(combined, reduction.type = "cca.aligned",
                         dims.use = 1:n.dims, resolution = resolutions)

Seurat has a resolution parameter that indirectly controls the number of clusters it produces. We tried clustering at a range of resolutions from 0 to 1.

t-SNE plots

Here are t-SNE plots of the different clusterings.

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

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

Res 0

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

Expand here to see past versions of cluster-tSNE-0-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.1

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

Expand here to see past versions of cluster-tSNE-0.1-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.2

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

Expand here to see past versions of cluster-tSNE-0.2-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.3

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

Expand here to see past versions of cluster-tSNE-0.3-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.4

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

Expand here to see past versions of cluster-tSNE-0.4-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.5

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

Expand here to see past versions of cluster-tSNE-0.5-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.6

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

Expand here to see past versions of cluster-tSNE-0.6-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.7

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

Expand here to see past versions of cluster-tSNE-0.7-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.8

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

Expand here to see past versions of cluster-tSNE-0.8-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 0.9

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

Expand here to see past versions of cluster-tSNE-0.9-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Res 1

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

Expand here to see past versions of cluster-tSNE-1-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Clustering tree

Standard

Coloured by clustering resolution.

clustree(combined)

Expand here to see past versions of clustree-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Stability

Coloured by the SC3 stability metric.

clustree(combined, node_colour = "sc3_stability")

Expand here to see past versions of clustree-stability-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Gene expression

Coloured by the expression of some well-known kidney marker genes.

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(combined@data)

The following genes aren’t present in this dataset and will be skipped:

src_list <- lapply(genes[is_present], function(gene) {
    src <- c("##### {{gene}} {.unnumbered}",
             "```{r clustree-{{gene}}}",
             "clustree(combined, 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))
PECAM1
clustree(combined, node_colour = 'PECAM1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

Expand here to see past versions of clustree-PECAM1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-CDH5-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-MEIS1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-PDGFRA-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-HMGB2-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-CENPA-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-SIX1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-DAPL1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-NPHS1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-PODXL-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-S100A8-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-TYROBP-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-MAL-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-EMX2-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-LRP2-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-GATA3-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-SLC12A1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-SPINT2-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-TUBB2B-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

Expand here to see past versions of clustree-STMN2-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

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

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

Expand here to see past versions of clustree-HBA1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

HBG1
clustree(combined, node_colour = 'HBG1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)

Expand here to see past versions of clustree-HBG1-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

Selected resolution

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

Based on these plots we will use a resolution of 0.5.

Clusters

Let’s have a look at the clusters on a t-SNE plot.

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

Expand here to see past versions of tSNE-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

We can also look at the number of cells in each cluster.

plot.data <- combined@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()

Expand here to see past versions of cluster-sizes-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

We are also interested in what proportions of the cells in each cluster come from each datasets (i.e. are there dataset specific clusters?).

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

Expand here to see past versions of cluster-props-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Alternatively we can look at what proportion of the cells in each dataset are in each cluster. If each dataset has the same distribution of cell types the heights of the bars should be the same.

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

Expand here to see past versions of dataset-props-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

Marker genes

Clustering is not very useful if we don’t know what cell types the clusters represent. One way to work that out is to look at marker genes, genes that are differentially expressed in one cluster compared to all other cells. Here we use the Wilcoxon rank sum test genes that are present in at least 10 percent of cells in at least one group (a cluster or all other cells).

markers <- bplapply(seq_len(n.clusts) - 1, function(cl) {
    cl.markers <- FindMarkers(combined, 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())

Here we print out the top two markers for each cluster.

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

A heatmap can give us a better view. We show the top five positive marker genes for each cluster.

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

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

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

Expand here to see past versions of markers-heatmap-1.png:
Version Author Date
5476164 Luke Zappia 2018-08-10

By cluster

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")

Expand here to see past versions of marker-cluster-counts-1.png:
Version Author Date
ad10b21 Luke Zappia 2018-09-13

We can also look at the full table of significant marker genes for each cluster.

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))

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