Comparison
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 | f99c608 | Luke Zappia | 2019-06-26 | Add correlation structure to comparison |
| html | 7e24e88 | Luke Zappia | 2019-06-25 | Add pairs plots to comparison |
| Rmd | fdabdcc | Luke Zappia | 2019-06-24 | Add antibody/gene comparison |
| html | fdabdcc | Luke Zappia | 2019-06-24 | Add antibody/gene comparison |
#### LIBRARIES ####
# Package conflicts
library("conflicted")
# Single-cell
library("SingleCellExperiment")
library("scater")
# File paths
library("fs")
library("here")
# Presentation
library("knitr")
library("jsonlite")
# Tidyverse
library("tidyverse")
### CONFLICT PREFERENCES ####
conflict_prefer("path", "fs")
conflict_prefer("filter", "dplyr")
conflict_prefer("mutate", "dplyr")
conflict_prefer("rename", "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 this document we are going to compare the RNA-seq and CITE data to see how similar they are to each other.
if (all(file_exists(c(PATHS$sce_qc, PATHS$cite_qc)))) {
sce <- read_rds(PATHS$sce_qc)
cite <- read_rds(PATHS$cite_qc)
} else {
stop("Filtered dataset is missing. ",
"Please run '02-quality-control.Rmd' first.",
call. = FALSE)
}Presence
First let’s check that if all the CITE targets are present in the RNA-seq data (and if we can match them up).
targets <- str_remove(rownames(cite), "Anti-")
kable(table(targets %in% rownames(sce)), col.names = c("Present", "Count"))| Present | Count |
|---|---|
| FALSE | 60 |
| TRUE | 36 |
That doesn’t look great, not many of our antibody names match our gene names. After looking at the list it seems that many of the antibody names are obsolute gene symbols or other identifiers. I have manually matched these up with the (hopefully) appropriate genes, mainly using the Gene cards and Novus biologicals websites. Let’s read in this list and use it to match up our datasets.
anti_gene <- read_tsv(PATHS$anti_gene,
col_types = cols(
Antibody = col_character(),
Gene = col_character()
))
anti_geneThere are still a couple of genes that aren’t present in the RNA-seq dataset (possible because they aren’t expressed) but this is a much better match. There are also a few cases were the pairing is ambiguous, either because multiple antibodies target different isoforms of the same gene or an antibody matches multiple genes (for example if it targets a protein complex). We will ignore these for the rest of this document.
anti_gene <- anti_gene %>%
filter(Gene %in% rownames(sce)) %>%
group_by(Gene) %>%
filter(n() == 1) %>%
group_by(Antibody) %>%
filter(n() == 1) %>%
ungroup()
sce_match <- sce[anti_gene$Gene, ]
cite_match <- cite[paste0("Anti-", anti_gene$Antibody), ]
rownames(cite_match) <- anti_gene$Antibody
cells_match <- colSums(counts(sce_match)) > 0 & colSums(counts(cite_match)) > 0
sce_match <- sce_match[, cells_match]
cite_match <- cite_match[, cells_match]
sizeFactors(sce_match) <- librarySizeFactors(sce_match)
sce_match <- normalize(sce_match)
sizeFactors(cite_match) <- librarySizeFactors(cite_match)
cite_match <- normalize(cite_match)Removing these leaves us with 82 unambiguous antibody-gene pairs.
Similarity
Now that we have matched up the two datasets we want to look at how similar the RNA and protein expression is.
anti_gene <- anti_gene %>%
mutate(
AntiMean = rowMeans(logcounts(cite_match)[Antibody, ]),
AntiVar = rowVars(logcounts(cite_match)[Antibody, ]),
AntiTotal = rowSums(counts(cite_match)[Antibody, ]),
AntiProp = rowMeans(counts(cite_match)[Antibody, ] > 0)
) %>%
mutate(
GeneMean = rowMeans(logcounts(sce_match)[Gene, ]),
GeneVar = rowVars(as.matrix(logcounts(sce_match)[Gene, ])),
GeneTotal = rowSums(counts(sce_match)[Gene, ]),
GeneProp = rowMeans(counts(sce)[Gene, ] > 0)
) %>%
mutate(
Corr = map2_dbl(
Antibody, Gene, function(x, y) {
cor(
counts(cite)[paste0("Anti-", x), ],
counts(sce)[y, ],
method = "spearman"
)
}
)
)Antibody/gene
Mean
ggplot(anti_gene, aes(x = GeneMean, y = AntiMean, colour = Corr)) +
geom_point(size = 6, alpha = 0.8) +
geom_smooth(method = "loess") +
geom_abline(intercept = 0, slope = 1, colour = "red") +
scale_colour_viridis_c() +
labs(
title = "Comparison of mean expression",
x = "Gene mean normalised logcounts",
y = "Antibody mean normalised logcounts",
colour = "Spearman\ncorrelation"
)
| Version | Author | Date |
|---|---|---|
| fdabdcc | Luke Zappia | 2019-06-24 |
Variance
ggplot(anti_gene, aes(x = GeneVar, y = AntiVar, colour = Corr)) +
geom_point(size = 6, alpha = 0.8) +
geom_smooth(method = "loess") +
geom_abline(intercept = 0, slope = 1, colour = "red") +
scale_colour_viridis_c() +
labs(
title = "Comparison of variance",
x = "Gene variance (normalised logcounts)",
y = "Antibody variance (normalised logcounts)",
colour = "Spearman\ncorrelation"
)
| Version | Author | Date |
|---|---|---|
| fdabdcc | Luke Zappia | 2019-06-24 |
Mean-variance
feat_data <- anti_gene %>%
select(Feature = Antibody, Mean = AntiMean, Var = AntiVar, Corr) %>%
mutate(Type = "Antibody") %>%
bind_rows(
anti_gene %>%
select(Feature = Gene, Mean = GeneMean, Var = GeneVar, Corr) %>%
mutate(Type = "Gene")
)
ggplot(feat_data, aes(x = Mean, y = Var, colour = Corr)) +
geom_point(size = 6, alpha = 0.8) +
geom_smooth(method = "loess") +
scale_colour_viridis_c() +
facet_wrap(~ Type) +
labs(
title = "Mean-variance relationship",
x = "Mean (normalised logcounts)",
y = "Variance (normalised logcounts)",
colour = "Spearman\ncorrelation"
)
| Version | Author | Date |
|---|---|---|
| fdabdcc | Luke Zappia | 2019-06-24 |
Total
ggplot(anti_gene, aes(x = GeneTotal, y = AntiTotal, colour = Corr)) +
geom_point(size = 6, alpha = 0.8) +
geom_smooth(method = "loess") +
scale_x_log10() +
scale_y_log10() +
scale_colour_viridis_c() +
labs(
title = "Comparison of total counts",
x = "Gene total",
y = "Antibody total",
colour = "Spearman\ncorrelation"
)
| Version | Author | Date |
|---|---|---|
| fdabdcc | Luke Zappia | 2019-06-24 |
Proportion
ggplot(anti_gene, aes(x = GeneProp, y = AntiProp, colour = Corr)) +
geom_point(size = 6, alpha = 0.8) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_smooth(method = "loess") +
xlim(0, 1) +
ylim(0, 1.2) +
scale_colour_viridis_c() +
labs(
title = "Comparison of proportion expressed",
x = "Gene proportion",
y = "Antibody proportion",
colour = "Spearman\ncorrelation"
)
| Version | Author | Date |
|---|---|---|
| fdabdcc | Luke Zappia | 2019-06-24 |
Pairs
Plots of expression for individual cells. Orange cross shows the mean and purple cross the nonzero mean.
anti_expr <- reshape2::melt(
logcounts(cite_match),
varnames = c("Antibody", "Barcode"),
value.name = "AntiExpr"
)
gene_expr <- reshape2::melt(
as.matrix(logcounts(sce_match)),
varnames = c("Gene", "Barcode"),
value.name = "GeneExpr"
)
expr <- anti_expr %>%
rename() %>%
mutate(
Gene = gene_expr$Gene,
GeneExpr = gene_expr$GeneExpr
) %>%
mutate(Anti_Gene = paste(Antibody, Gene, sep = "_")) %>%
select(Barcode, Anti_Gene, Antibody, Gene, AntiExpr, GeneExpr)
nonzero_mean <- function(x) {
mean(x[x > 0])
}
plot_expr <- function(expr, pairs) {
expr_filt <- expr %>%
filter(Anti_Gene %in% pairs)
expr_means <- expr_filt %>%
group_by(Anti_Gene) %>%
summarise(
AntiExpr = mean(AntiExpr),
GeneExpr = mean(GeneExpr)
)
expr_nonzero <- expr_filt %>%
group_by(Anti_Gene) %>%
summarise(
AntiExpr = nonzero_mean(AntiExpr),
GeneExpr = nonzero_mean(GeneExpr)
)
ggplot(expr_filt, aes(x = GeneExpr, y = AntiExpr)) +
geom_point(alpha = 0.4) +
geom_point(data = expr_nonzero, size = 10,
colour = "purple", shape = 3, stroke = 1) +
geom_point(data = expr_means, size = 10,
colour = "orange", shape = 3, stroke = 1) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
labs(
x = "Gene expression (normalised logcounts)",
y = "Antibody expression (normalised logcounts)"
) +
facet_wrap(~ Anti_Gene)
}
pairs <- unique(expr$Anti_Gene)
pair_sets <- split(pairs, rep(1:7, each = 12)[1:length(pairs)])
src_list <- lapply(seq_along(pair_sets), function(id) {
src <- c(
"#### Page {{id}} {.unnumbered}",
"```{r pairs-{{id}}}",
"plot_expr(expr, pair_sets[[{{id}}]])",
"```",
""
)
knit_expand(text = src)
})
out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))Page 1
plot_expr(expr, pair_sets[[1]])
Page 2
plot_expr(expr, pair_sets[[2]])
Page 3
plot_expr(expr, pair_sets[[3]])
Page 4
plot_expr(expr, pair_sets[[4]])
Page 5
plot_expr(expr, pair_sets[[5]])
Page 6
plot_expr(expr, pair_sets[[6]])
Page 7
plot_expr(expr, pair_sets[[7]])
Correlation structure
cite_corr_mat <- logcounts(cite_match) %>%
t() %>%
cor(method = "spearman")
cite_corr_order <- hclust(dist(cite_corr_mat))$order
cite_corr_levels <- rownames(cite_corr_mat)[cite_corr_order]
cite_corr <- reshape2::melt(
cite_corr_mat,
varnames = c("Antibody1", "Antibody2"),
value.name = "Corr"
) %>%
mutate(
Antibody1 = factor(Antibody1, levels = cite_corr_levels),
Antibody2 = factor(Antibody2, levels = cite_corr_levels)
)
cite_corr_plot <- ggplot(cite_corr) +
aes(x = Antibody1, y = Antibody2, fill = Corr) +
geom_tile() +
scale_fill_distiller(palette = "RdBu", limits = c(-1, 1)) +
coord_equal() +
labs(
title = "Antibody correlation"
) +
theme(
axis.title = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
)
gene_corr_mat <- logcounts(sce_match) %>%
as.matrix() %>%
t() %>%
cor(method = "spearman")
gene_corr_levels <- rownames(gene_corr_mat)[cite_corr_order]
gene_corr <- reshape2::melt(
gene_corr_mat,
varnames = c("Gene1", "Gene2"),
value.name = "Corr"
) %>%
mutate(
Gene1 = factor(Gene1, levels = gene_corr_levels),
Gene2 = factor(Gene2, levels = gene_corr_levels)
)
gene_corr_plot <- ggplot(gene_corr) +
aes(x = Gene1, y = Gene2, fill = Corr) +
geom_tile() +
scale_fill_distiller(palette = "RdBu", limits = c(-1, 1)) +
coord_equal() +
labs(
title = "Gene correlation"
) +
theme(
axis.title = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
)
multiplot(cite_corr_plot, gene_corr_plot, cols = 2)
Cell
cell_data <- tibble(Barcode = colnames(cite_match)) %>%
mutate(
AntiTotal = colSums(counts(cite_match)[, Barcode])
) %>% mutate(
GeneTotal = colSums(counts(sce_match)[, Barcode])
) %>%
mutate(
Corr = map_dbl(
Barcode, function(x) {
cor(
counts(cite_match)[, x],
counts(sce_match)[, x],
method = "spearman"
)
}
)
)Total
ggplot(cell_data, aes(x = GeneTotal, y = AntiTotal, colour = Corr)) +
geom_point(size = 4, alpha = 0.8, shape = 18) +
geom_smooth(method = "loess") +
scale_x_log10() +
scale_y_log10() +
scale_colour_viridis_c() +
labs(
title = "Comparison of total counts",
x = "Gene total",
y = "Antibody total",
colour = "Spearman\ncorrelation"
)
| Version | Author | Date |
|---|---|---|
| fdabdcc | Luke Zappia | 2019-06-24 |
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 ──────────────────────────────────────────────────────────────
! package * version date lib source
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[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.