CITE clustering
2019-06-26
Last updated: 2019-06-26
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Knit directory: OzSingleCells2019/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5ccc682 | Luke Zappia | 2019-06-26 | Add CITE clustering |
#### LIBRARIES ####
# Package conflicts
library("conflicted")
# Single-cell
library("SingleCellExperiment")
library("scran")
library("scater")
# Plotting
library("clustree")
library("ggforce")
# Bioconductor
library("BiocSingular")
# File paths
library("fs")
library("here")
# Presentation
library("knitr")
library("jsonlite")
# Tidyverse
library("tidyverse")
### CONFLICT PREFERENCES ####
conflict_prefer("path", "fs")
conflict_prefer("mutate", "dplyr")
### SOURCE FUNCTIONS ####
source(here("R/output.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 document we are going to perform clustering using just the CITE data. This analysis is based on the “simpleSingleCell” workflow.
if (all(file_exists(c(PATHS$cite_qc)))) {
cite <- read_rds(PATHS$cite_qc)
} else {
stop("Filtered dataset is missing. ",
"Please run '02-quality-control.Rmd' first.",
call. = FALSE)
}
col_data <- as.data.frame(colData(cite))Normalisation
Because the CITE dataset is so much smaller we will just use library size factors for normalisation instead of a more complex approach.
sizeFactors(cite) <- librarySizeFactors(cite)
cite <- normalize(cite)Mean-variance
Fit
Fit the mean-variance relationship
tech_trend <- makeTechTrend(x = cite)
fit <- trendVar(cite, use.spikes = FALSE, loess.args = list(span = 0.5))
plot_data <- tibble(
Mean = fit$means,
Var = fit$vars,
Trend = fit$trend(fit$means),
TechTrend = tech_trend(fit$means)
)
ggplot(plot_data, aes(x = Mean)) +
geom_point(aes(y = Var)) +
geom_line(aes(y = Trend), colour = "blue") +
geom_line(aes(y = TechTrend), colour = "red") +
annotate("text", x = 7, y = 2, label = "Fitted trend", colour = "blue") +
annotate("text", x = 7, y = 1.8, label = "Poisson noise", colour = "red") +
labs(
title = "Mean-variance relationship",
x = "Mean log-expression",
y = "Variance of log-expression"
)
Dimensionality reduction
PCA
PCA is performed on the dataset and components selected based on the proportion of variance explained.
set.seed(1)
cite <- runPCA(cite, ncomponents = 30, BSPARAM = IrlbaParam())
n_pcs <- 10
plot_data <- tibble(
PC = seq_along(attr(reducedDim(cite), "percentVar")),
PercentVar = attr(reducedDim(cite), "percentVar")
) %>%
mutate(Selected = PC <= n_pcs)
ggplot(plot_data, aes(x = PC, y = PercentVar, colour = Selected)) +
geom_point() +
scale_colour_manual(values = c("grey40", "red")) +
labs(
title = "PC variance",
x = "Principal component",
y = "Proportion of variance explained"
)
plotPCA(cite, ncomponents = 3, colour_by = "total_features_by_counts")
Here we select the first 10 components.
t-SNE
set.seed(1)
cite <- runTSNE(cite, use_dimred = "PCA", perplexity = 40)
plotTSNE(cite, colour_by = "log10_total_counts")
Clustering
Cluster cells using the implementation in Seurat because it has a resolution parameter.
snn_mat <- Seurat::FindNeighbors(reducedDim(cite, "PCA"))$snn
resolutions <- seq(0, 1, 0.1)
for (res in resolutions) {
clusters <- Seurat:::RunModularityClustering(snn_mat, resolution = res)
col_data[[paste0("ClusterRes", res)]] <- factor(clusters)
}
colData(cite) <- DataFrame(col_data)Dimensionality reduction
Dimensionality reduction plots showing clusters at different resolutions.
PCA
src_list <- lapply(resolutions, function(res) {
src <- c(
"#### Res {{res}} {.unnumbered}",
"```{r res-pca-{{res}}}",
"plotPCA(cite, colour_by = 'ClusterRes{{res}}') + theme_minimal()",
"```",
""
)
knit_expand(text = src)
})
out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))Res 0
plotPCA(cite, colour_by = 'ClusterRes0') + theme_minimal()
Res 0.1
plotPCA(cite, colour_by = 'ClusterRes0.1') + theme_minimal()
Res 0.2
plotPCA(cite, colour_by = 'ClusterRes0.2') + theme_minimal()
Res 0.3
plotPCA(cite, colour_by = 'ClusterRes0.3') + theme_minimal()
Res 0.4
plotPCA(cite, colour_by = 'ClusterRes0.4') + theme_minimal()
Res 0.5
plotPCA(cite, colour_by = 'ClusterRes0.5') + theme_minimal()
Res 0.6
plotPCA(cite, colour_by = 'ClusterRes0.6') + theme_minimal()
Res 0.7
plotPCA(cite, colour_by = 'ClusterRes0.7') + theme_minimal()
Res 0.8
plotPCA(cite, colour_by = 'ClusterRes0.8') + theme_minimal()
Res 0.9
plotPCA(cite, colour_by = 'ClusterRes0.9') + theme_minimal()
Res 1
plotPCA(cite, colour_by = 'ClusterRes1') + theme_minimal()
t-SNE
src_list <- lapply(resolutions, function(res) {
src <- c(
"#### Res {{res}} {.unnumbered}",
"```{r res-tSNE-{{res}}}",
"plotTSNE(cite, colour_by = 'ClusterRes{{res}}') + theme_minimal()",
"```",
""
)
knit_expand(text = src)
})
out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))Res 0
plotTSNE(cite, colour_by = 'ClusterRes0') + theme_minimal()
Res 0.1
plotTSNE(cite, colour_by = 'ClusterRes0.1') + theme_minimal()
Res 0.2
plotTSNE(cite, colour_by = 'ClusterRes0.2') + theme_minimal()
Res 0.3
plotTSNE(cite, colour_by = 'ClusterRes0.3') + theme_minimal()
Res 0.4
plotTSNE(cite, colour_by = 'ClusterRes0.4') + theme_minimal()
Res 0.5
plotTSNE(cite, colour_by = 'ClusterRes0.5') + theme_minimal()
Res 0.6
plotTSNE(cite, colour_by = 'ClusterRes0.6') + theme_minimal()
Res 0.7
plotTSNE(cite, colour_by = 'ClusterRes0.7') + theme_minimal()
Res 0.8
plotTSNE(cite, colour_by = 'ClusterRes0.8') + theme_minimal()
Res 0.9
plotTSNE(cite, colour_by = 'ClusterRes0.9') + theme_minimal()
Res 1
plotTSNE(cite, colour_by = 'ClusterRes1') + theme_minimal()
Clustering trees
Clustering trees show the relationship between clusterings at adjacent resolutions. Each cluster is represented as a node in a graph and the edges show the overlap between clusters.
Standard
Coloured by clustering resolution.
clustree(cite, prefix = "ClusterRes")
Stability
Coloured by the SC3 stability metric.
clustree(cite, prefix = "ClusterRes", node_colour = "sc3_stability")
Selection
res <- 0.8
col_data$Cluster <- col_data[[paste0("ClusterRes", res)]]
colData(cite) <- DataFrame(col_data)
n_clusts <- length(unique(col_data$Cluster))Based on these plots we will use a resolution of 0.8 which gives us 12 clusters.
Validation
To validate the clusters we will repeat some of our quality control plots separated by cluster. At this stage we just want to check that none of the clusters are obviously the result of technical factors.
Cluster
Clusters assigned by Seurat.
Count
ggplot(col_data, aes(x = Cluster, fill = Cluster)) +
geom_bar()
PCA
plotPCA(cite, colour_by = "Cluster", point_alpha = 1) +
scale_fill_discrete() +
theme_minimal()
t-SNE
plotTSNE(cite, colour_by = "Cluster", point_alpha = 1) +
scale_fill_discrete() +
theme_minimal()
Total counts
Total counts per cell.
Distribution
ggplot(col_data, aes(x = Cluster, y = log10_total_counts, colour = Cluster)) +
geom_violin() +
geom_sina(size = 0.5) +
theme(legend.position = "none")
PCA
plotPCA(cite, colour_by = "log10_total_counts", point_alpha = 1) +
scale_fill_viridis_c() +
theme_minimal()
t-SNE
plotTSNE(cite, colour_by = "log10_total_counts", point_alpha = 1) +
scale_fill_viridis_c()+
theme_minimal()
Total features
Total number of expressed features per cell.
Distribution
ggplot(col_data,
aes(x = Cluster, y = total_features_by_counts, colour = Cluster)) +
geom_violin() +
geom_sina(size = 0.5) +
theme(legend.position = "none")
PCA
plotPCA(cite, colour_by = "total_features_by_counts", point_alpha = 1) +
scale_fill_viridis_c() +
theme_minimal()
t-SNE
plotTSNE(cite, colour_by = "total_features_by_counts", point_alpha = 1) +
scale_fill_viridis_c()+
theme_minimal()
Summary
Parameters
This table describes parameters used and set in this document.
params <- list(
list(
Parameter = "n_pcs",
Value = n_pcs,
Description = "Selected number of principal components for clustering"
),
list(
Parameter = "resolutions",
Value = resolutions,
Description = "Range of possible clustering resolutions"
),
list(
Parameter = "res",
Value = res,
Description = "Selected resolution parameter for clustering"
),
list(
Parameter = "n_clusts",
Value = n_clusts,
Description = "Number of clusters produced by selected resolution"
)
)
params <- toJSON(params, pretty = TRUE)
kable(fromJSON(params))| Parameter | Value | Description |
|---|---|---|
| n_pcs | 10 | Selected number of principal components for clustering |
| resolutions | c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) | Range of possible clustering resolutions |
| res | 0.8 | Selected resolution parameter for clustering |
| n_clusts | 12 | Number of clusters produced by selected resolution |
Output files
This table describes the output files produced by this document. Right click and Save Link As… to download the results.
write_rds(cite, PATHS$cite_clust, compress = "bz", compression = 9)
write_lines(params, path(OUT_DIR, "parameters.json"))
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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xfun 0.7 2019-05-14 [1] CRAN (R 3.6.0)
xml2 1.2.0 2018-01-24 [1] CRAN (R 3.6.0)
XVector 0.24.0 2019-05-02 [1] Bioconductor
yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
zlibbioc 1.30.0 2019-05-02 [1] Bioconductor
zoo 1.8-6 2019-05-28 [1] CRAN (R 3.6.0)
[1] /group/bioi1/luke/analysis/OzSingleCells2019/packrat/lib/x86_64-pc-linux-gnu/3.6.0
[2] /group/bioi1/luke/analysis/OzSingleCells2019/packrat/lib-ext/x86_64-pc-linux-gnu/3.6.0
[3] /group/bioi1/luke/analysis/OzSingleCells2019/packrat/lib-R/x86_64-pc-linux-gnu/3.6.0
[4] /home/luke.zappia/R/x86_64-pc-linux-gnu-library/3.6
[5] /usr/local/installed/R/3.6.0/lib64/R/library
P ── Loaded and on-disk path mismatch.