Last updated: 2018-12-05
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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 |
html | a61f9c9 | Luke Zappia | 2018-09-13 | Rebuild site |
html | ad10b21 | Luke Zappia | 2018-09-13 | Switch to GitHub |
Rmd | 7755ac7 | Luke Zappia | 2018-08-15 | Add methods document |
Rmd | bff4d5b | Luke Zappia | 2018-08-14 | Add crossover document |
Rmd | 30718d3 | Luke Zappia | 2018-08-14 | Add nephron reclustering |
# 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)
In this document we are going to recluster the nephron clusters identified in the organoids analysis.
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)
We are going to select only the cells in clusters 2 and 9. This leaves us with 1125 cells.
# 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)
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.
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(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)
Coloured by clustering resolution.
clustree(orgs.neph)
Coloured by the SC3 stability metric.
clustree(orgs.neph, node_colour = "sc3_stability")
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(orgs.neph@data)
The following genes aren’t present in this dataset and will be skipped: HBG1
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)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'CDH5',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'MEIS1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'PDGFRA',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'HMGB2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'CENPA',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'SIX1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'DAPL1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'NPHS1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'PODXL',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'S100A8',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'TYROBP',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'MAL',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'EMX2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'LRP2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'GATA3',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'SLC12A1',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'SPINT2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'TUBB2B',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
clustree(orgs.neph, node_colour = 'STMN2',
node_colour_aggr = 'mean',
exprs = 'scale.data') +
scale_colour_viridis_c(option = 'plasma', begin = 0.3)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
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)
Version | Author | Date |
---|---|---|
ad10b21 | Luke Zappia | 2018-09-13 |
res <- 0.5
orgs.neph <- SetIdent(orgs.neph,
ident.use = orgs.neph@meta.data[, paste0("res.", res)])
n.clusts <- length(unique(orgs.neph@ident))
Based on these plots we will use a resolution of 0.5.
Let’s have a look at the clusters on a t-SNE plot.
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)
We can also look at the number of cells in each cluster.
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()
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()
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))
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(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())
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(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")
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))