Cluster comparison
2019-06-26
Last updated: 2019-06-26
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Knit directory: OzSingleCells2019/
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#### LIBRARIES ####
# Package conflicts
library("conflicted")
# Single-cell
library("SingleCellExperiment")
# Plotting
library("clustree")
# File paths
library("fs")
library("here")
# Presentation
library("knitr")
library("jsonlite")
# Tidyverse
library("tidyverse")
### CONFLICT PREFERENCES ####
conflict_prefer("path", "fs")
conflict_prefer("rename", "dplyr")
### SOURCE FUNCTIONS ####
source(here("R/output.R"))
source(here("R/crossover.R"))
### OUTPUT DIRECTORY ####
OUT_DIR <- here("output", DOCNAME)
dir_create(OUT_DIR)
#### SET GGPLOT THEME ####
theme_set(theme_minimal())
#### SET PATHS ####
source(here("R/set_paths.R"))Introduction
In this document we are going to compare the clustering results for the RNA-seq and CITE data to see how similar they are to each other.
if (all(file_exists(c(PATHS$sce_clust, PATHS$cite_clust)))) {
sce <- read_rds(PATHS$sce_clust)
cite <- read_rds(PATHS$cite_clust)
} else {
stop("Clustered dataset is missing. ",
"Please run '04-clustering.Rmd' and '05-cite-clustering.Rmd' first.",
call. = FALSE)
}
clust_data <- colData(sce) %>%
as.data.frame() %>%
select(Barcode, GeneCluster = Cluster) %>%
mutate(AntiCluster = colData(cite)$Cluster)Clustering
Clustering “tree”
clust_data %>%
rename(Cluster1 = GeneCluster, Cluster2 = AntiCluster) %>%
clustree(prefix = "Cluster", show_axis = TRUE) +
scale_y_continuous(
breaks = c(0, 1),
labels = c("CITE cluster", "RNA cluster")
)
Jaccard index
plot_data <- summariseClusts(clust_data, GeneCluster, AntiCluster) %>%
replace_na(list(Jaccard = 0))
ggplot(plot_data, aes(x = GeneCluster, y = AntiCluster, fill = Jaccard)) +
geom_tile() +
scale_fill_viridis_c(limits = c(0, 1), name = "Jaccard\nindex") +
coord_equal() +
labs(
x = "Gene cluster",
y = "CITE cluster"
) +
theme(axis.text = element_text(size = 10, colour = "black"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
legend.key.height = unit(30, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = element_blank())
Bars
Gene clusters
ggplot(clust_data, aes(x = GeneCluster, fill = AntiCluster)) +
geom_bar()
CITE clusters
ggplot(clust_data, aes(x = AntiCluster, fill = GeneCluster)) +
geom_bar()
Markers
cite_corr_mat <- logcounts(cite) %>%
t() %>%
cor(method = "spearman")
cite_corr_order <- hclust(dist(cite_corr_mat))$order
cite_corr_levels <- rownames(cite_corr_mat)[cite_corr_order]
cite_props <- crossing(
Cluster = clust_data$AntiCluster,
Antibody = rownames(cite)
) %>%
mutate(
AntiClust = map2_dbl(Cluster, Antibody, function(c, a) {
sum(counts(cite)[a, clust_data$AntiCluster == c])
})
) %>%
group_by(Antibody) %>%
mutate(AntiTotal = sum(AntiClust)) %>%
group_by(Cluster) %>%
mutate(ClustTotal = sum(AntiClust)) %>%
ungroup() %>%
mutate(
AntiClustProp = AntiClust / ClustTotal,
AntiDataProp = AntiTotal / sum(counts(cite)),
Ratio = AntiClustProp / AntiDataProp
)
ggplot(cite_props,
aes(
x = factor(str_remove(Antibody, "Anti-"),
levels = str_remove(cite_corr_levels, "Anti-")),
y = Ratio,
colour = log10(Ratio)
)) +
annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = 2,
fill = "grey", colour = "grey", alpha = 0.3) +
geom_hline(yintercept = 2, colour = "red") +
geom_point() +
scale_colour_viridis_c() +
facet_wrap(~ Cluster, ncol = 1, strip.position = "right", scales = "free_y") +
labs(
title = "Anitbody proportions",
y = "(Cluster proportion) / (Dataset proportion)"
) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
legend.position = "none",
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
panel.border = element_rect(color = "black", fill = NA, size = 1)
)
Summary
Parameters
This table describes parameters used and set in this document.
params <- list(
)
params <- toJSON(params, pretty = TRUE)
kable(fromJSON(params))Output files
This table describes the output files produced by this document. Right click and Save Link As… to download the results.
kable(data.frame(
File = c(
download_link("parameters.json", OUT_DIR)
),
Description = c(
"Parameters set and used in this analysis"
)
))| File | Description |
|---|---|
| parameters.json | Parameters set and used in this analysis |
Session information
sessioninfo::session_info()─ Session info ──────────────────────────────────────────────────────────
setting value
version R version 3.6.0 (2019-04-26)
os CentOS release 6.7 (Final)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Australia/Melbourne
date 2019-06-26
─ Packages ──────────────────────────────────────────────────────────────
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P ── Loaded and on-disk path mismatch.