Last updated: 2019-06-26
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Knit directory: OzSingleCells2019/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 2fff6a2 | Luke Zappia | 2019-06-25 | Add clustering of RNA data |
html | 2fff6a2 | Luke Zappia | 2019-06-25 | Add clustering of RNA data |
#### 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"))
In document we are going to perform a standard clustering analysis using just the RNA-seq data. This analysis is based on the “simpleSingleCell” workflow.
if (all(file_exists(c(PATHS$sce_qc)))) {
sce <- read_rds(PATHS$sce_qc)
} else {
stop("Filtered dataset is missing. ",
"Please run '02-quality-control.Rmd' first.",
call. = FALSE)
}
col_data <- as.data.frame(colData(sce))
Pre-cluster cells to avoid the assumption that most genes are non-DE.
set.seed(1)
clusters <- quickCluster(sce, use.ranks = FALSE, BSPARAM = IrlbaParam())
col_data$NormCluster <- clusters
kable(table(clusters), col.names = c("Cluster", "Size"))
Cluster | Size |
---|---|
1 | 430 |
2 | 104 |
3 | 128 |
4 | 112 |
5 | 262 |
6 | 194 |
7 | 234 |
8 | 274 |
9 | 162 |
10 | 101 |
11 | 170 |
Calculate scran
doconvolution size factors.
sce <- computeSumFactors(sce, min.mean = 0.1, cluster = clusters)
col_data$SizeFactor <- sizeFactors(sce)
ggplot(col_data,
aes(x = total_counts, y = SizeFactor, colour = NormCluster, group = 1)) +
geom_point() +
geom_smooth(method = "lm") +
scale_x_log10() +
scale_y_log10() +
labs(
title = "Correlation of size factors",
x = "Total counts",
y = "Size factor",
colour = "Normalisation\ncluster"
)
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
sce <- normalize(sce)
Fit the mean-variance relationship
tech_trend <- makeTechTrend(x = sce)
fit <- trendVar(sce, use.spikes = FALSE, loess.args = list(span = 0.05))
plot_data <- tibble(
Mean = fit$means,
Var = fit$vars,
Trend = fit$trend(fit$means),
TechTrend = tech_trend(fit$means)
)
ggplot(plot_data, aes(x = Mean)) +
geom_point(aes(y = Var)) +
geom_line(aes(y = Trend), colour = "blue") +
geom_line(aes(y = TechTrend), colour = "red") +
annotate("text", x = 7, y = 2, label = "Fitted trend", colour = "blue") +
annotate("text", x = 7, y = 1.8, label = "Poisson noise", colour = "red") +
labs(
title = "Mean-variance relationship",
x = "Mean log-expression",
y = "Variance of log-expression"
)
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Decompose variance into technical and biological factors based on the technical Poisson trend.
fit$trend <- tech_trend
decomposed <- decomposeVar(fit = fit)
top_dec <- decomposed[order(decomposed$bio, decreasing = TRUE), ]
kable(head(rownames_to_column(as.data.frame(top_dec), "Gene"), n = 10))
Gene | mean | total | bio | tech | p.value | FDR |
---|---|---|---|---|---|---|
CD74 | 2.651929 | 5.234573 | 4.663844 | 0.5707292 | 0 | 0 |
HLA-DRA | 1.615211 | 4.653876 | 3.832151 | 0.8217243 | 0 | 0 |
JUNB | 3.257231 | 2.824802 | 2.440419 | 0.3843838 | 0 | 0 |
FOS | 2.119546 | 2.955675 | 2.223345 | 0.7323296 | 0 | 0 |
HLA-DPA1 | 1.410277 | 2.837414 | 2.011976 | 0.8254388 | 0 | 0 |
HLA-DPB1 | 1.383950 | 2.566767 | 1.742578 | 0.8241897 | 0 | 0 |
IL7R | 1.732798 | 2.513209 | 1.703328 | 0.8098809 | 0 | 0 |
RGS1 | 1.601309 | 2.369054 | 1.546370 | 0.8226843 | 0 | 0 |
HLA-DQB1 | 1.174976 | 2.286776 | 1.488068 | 0.7987084 | 0 | 0 |
ZFP36L2 | 3.379996 | 1.835642 | 1.484051 | 0.3515910 | 0 | 0 |
plotExpression(sce, features = rownames(top_dec)[1:10]) +
ggtitle("Expression of biologically variable genes") +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
PCA is performed on the dataset and the Poisson technical variance trend is used to select the top components to use.
set.seed(1)
sce <- denoisePCA(sce, technical = tech_trend, BSPARAM = IrlbaParam())
n_pcs <- ncol(reducedDim(sce, "PCA"))
plot_data <- tibble(
PC = seq_along(attr(reducedDim(sce), "percentVar")),
PercentVar = attr(reducedDim(sce), "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"
)
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, ncomponents = 3, colour_by = "log10_total_features_by_counts")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Here we select the first 25 components.
set.seed(1)
sce <- runTSNE(sce, use_dimred = "PCA", perplexity = 40)
plotTSNE(sce, colour_by = "log10_total_features_by_counts")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Cluster cells using the implementation in Seurat
because it has a resolution parameter.
snn_mat <- Seurat::FindNeighbors(reducedDim(sce, "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(sce) <- DataFrame(col_data)
Dimensionality reduction plots showing clusters at different resolutions.
src_list <- lapply(resolutions, function(res) {
src <- c(
"#### Res {{res}} {.unnumbered}",
"```{r res-pca-{{res}}}",
"plotPCA(sce, colour_by = 'ClusterRes{{res}}') + theme_minimal()",
"```",
""
)
knit_expand(text = src)
})
out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))
plotPCA(sce, colour_by = 'ClusterRes0') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.1') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.2') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.3') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.4') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.5') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.6') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.7') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.8') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes0.9') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = 'ClusterRes1') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
src_list <- lapply(resolutions, function(res) {
src <- c(
"#### Res {{res}} {.unnumbered}",
"```{r res-tSNE-{{res}}}",
"plotTSNE(sce, colour_by = 'ClusterRes{{res}}') + theme_minimal()",
"```",
""
)
knit_expand(text = src)
})
out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))
plotTSNE(sce, colour_by = 'ClusterRes0') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.1') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.2') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.3') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.4') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.5') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.6') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.7') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.8') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes0.9') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = 'ClusterRes1') + theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
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.
Coloured by clustering resolution.
clustree(sce, prefix = "ClusterRes")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Coloured by the SC3 stability metric.
clustree(sce, prefix = "ClusterRes", node_colour = "sc3_stability")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
res <- 0.8
col_data$Cluster <- col_data[[paste0("ClusterRes", res)]]
colData(sce) <- DataFrame(col_data)
n_clusts <- length(unique(col_data$Cluster))
Based on these plots we will use a resolution of 0.8 which gives us 13 clusters.
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.
Clusters assigned by Seurat
.
ggplot(col_data, aes(x = Cluster, fill = Cluster)) +
geom_bar()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = "Cluster", point_alpha = 1) +
scale_fill_discrete() +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = "Cluster", point_alpha = 1) +
scale_fill_discrete() +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Cell cycle phases assigned by scran
.
ggplot(col_data, aes(x = Cluster, fill = CellCycle)) +
geom_bar()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plot_data <- col_data %>%
group_by(Cluster, CellCycle) %>%
summarise(Count = n()) %>%
mutate(Prop = Count / sum(Count))
ggplot(plot_data, aes(x = Cluster, y = Prop, fill = CellCycle)) +
geom_col() +
ylab("Proportion of cluster")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = "CellCycle", point_alpha = 1) +
scale_fill_discrete() +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = "CellCycle", point_alpha = 1) +
scale_fill_discrete() +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Total counts per cell.
ggplot(col_data, aes(x = Cluster, y = log10_total_counts, colour = Cluster)) +
geom_violin() +
geom_sina(size = 0.5) +
theme(legend.position = "none")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = "log10_total_counts", point_alpha = 1) +
scale_fill_viridis_c() +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = "log10_total_counts", point_alpha = 1) +
scale_fill_viridis_c()+
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Total number of expressed features per cell.
ggplot(col_data,
aes(x = Cluster, y = log10_total_features_by_counts, colour = Cluster)) +
geom_violin() +
geom_sina(size = 0.5) +
theme(legend.position = "none")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = "log10_total_features_by_counts", point_alpha = 1) +
scale_fill_viridis_c() +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = "log10_total_features_by_counts", point_alpha = 1) +
scale_fill_viridis_c()+
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
Percentage of counts assigned to mitochondrial genes per cell.
ggplot(col_data, aes(x = Cluster, y = pct_counts_MT, colour = Cluster)) +
geom_violin() +
geom_sina(size = 0.5) +
theme(legend.position = "none")
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotPCA(sce, colour_by = "pct_counts_MT", point_alpha = 1) +
scale_fill_viridis_c() +
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
plotTSNE(sce, colour_by = "pct_counts_MT", point_alpha = 1) +
scale_fill_viridis_c()+
theme_minimal()
Version | Author | Date |
---|---|---|
2fff6a2 | Luke Zappia | 2019-06-25 |
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 | 25 | 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 | 13 | Number of clusters produced by selected resolution |
This table describes the output files produced by this document. Right click and Save Link As… to download the results.
write_rds(sce, PATHS$sce_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 |
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 ──────────────────────────────────────────────────────────────
! package * version date lib source
ape 5.3 2019-03-17 [1] CRAN (R 3.6.0)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
backports 1.1.4 2019-04-10 [1] CRAN (R 3.6.0)
beeswarm 0.2.3 2016-04-25 [1] CRAN (R 3.6.0)
bibtex 0.4.2 2017-06-30 [1] CRAN (R 3.6.0)
Biobase * 2.44.0 2019-05-02 [1] Bioconductor
BiocGenerics * 0.30.0 2019-05-02 [1] Bioconductor
BiocNeighbors 1.2.0 2019-05-02 [1] Bioconductor
BiocParallel * 1.18.0 2019-05-03 [1] Bioconductor
BiocSingular * 1.0.0 2019-05-02 [1] Bioconductor
bitops 1.0-6 2013-08-17 [1] CRAN (R 3.6.0)
broom 0.5.2 2019-04-07 [1] CRAN (R 3.6.0)
caTools 1.17.1.2 2019-03-06 [1] CRAN (R 3.6.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 3.6.0)
checkmate 1.9.3 2019-05-03 [1] CRAN (R 3.6.0)
cli 1.1.0 2019-03-19 [1] CRAN (R 3.6.0)
P cluster 2.0.9 2019-05-01 [5] CRAN (R 3.6.0)
clustree * 0.4.0 2019-04-18 [1] CRAN (R 3.6.0)
P codetools 0.2-16 2018-12-24 [5] CRAN (R 3.6.0)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.0)
conflicted * 1.0.3 2019-05-01 [1] CRAN (R 3.6.0)
cowplot 0.9.4 2019-01-08 [1] CRAN (R 3.6.0)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
data.table 1.12.2 2019-04-07 [1] CRAN (R 3.6.0)
DelayedArray * 0.10.0 2019-05-02 [1] Bioconductor
DelayedMatrixStats 1.6.0 2019-05-02 [1] Bioconductor
digest 0.6.19 2019-05-20 [1] CRAN (R 3.6.0)
dplyr * 0.8.1 2019-05-14 [1] CRAN (R 3.6.0)
dqrng 0.2.1 2019-05-17 [1] CRAN (R 3.6.0)
dynamicTreeCut 1.63-1 2016-03-11 [1] CRAN (R 3.6.0)
edgeR 3.26.4 2019-05-27 [1] Bioconductor
evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
farver 1.1.0 2018-11-20 [1] CRAN (R 3.6.0)
fitdistrplus 1.0-14 2019-01-23 [1] CRAN (R 3.6.0)
forcats * 0.4.0 2019-02-17 [1] CRAN (R 3.6.0)
fs * 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
future 1.13.0 2019-05-08 [1] CRAN (R 3.6.0)
future.apply 1.3.0 2019-06-18 [1] CRAN (R 3.6.0)
gbRd 0.4-11 2012-10-01 [1] CRAN (R 3.6.0)
gdata 2.18.0 2017-06-06 [1] CRAN (R 3.6.0)
generics 0.0.2 2018-11-29 [1] CRAN (R 3.6.0)
GenomeInfoDb * 1.20.0 2019-05-02 [1] Bioconductor
GenomeInfoDbData 1.2.1 2019-06-19 [1] Bioconductor
GenomicRanges * 1.36.0 2019-05-02 [1] Bioconductor
ggbeeswarm 0.6.0 2017-08-07 [1] CRAN (R 3.6.0)
ggforce * 0.2.2 2019-04-23 [1] CRAN (R 3.6.0)
ggplot2 * 3.2.0 2019-06-16 [1] CRAN (R 3.6.0)
ggraph * 1.0.2 2018-07-07 [1] CRAN (R 3.6.0)
ggrepel 0.8.1 2019-05-07 [1] CRAN (R 3.6.0)
ggridges 0.5.1 2018-09-27 [1] CRAN (R 3.6.0)
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P ── Loaded and on-disk path mismatch.