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README.md
In tidySingleCellExperiment: Brings SingleCellExperiment to the Tidyverse
tidySingleCellExperiment - part of tidytranscriptomics
Brings SingleCellExperiment to the tidyverse!
Website:
tidySingleCellExperiment
Please also have a look at
- tidyseurat for tidy
manipulation of Seurat objects
- tidybulk for tidy bulk
RNA-seq data analysis
- nanny for tidy high-level
data analysis and manipulation
- tidygate for adding
custom gate information to your tibble
- tidyHeatmap for
heatmaps produced with tidy principles
Introduction
tidySingleCellExperiment provides a bridge between Bioconductor single-cell packages
[@amezquita2019orchestrating] and the tidyverse
[@wickham2019welcome]. It creates an invisible layer that enables
viewing the Bioconductor SingleCellExperiment object as a tidyverse
tibble, and provides SingleCellExperiment-compatible dplyr, tidyr,
ggplot and plotly functions. This allows users to get the best of
both Bioconductor and tidyverse worlds.
Functions/utilities available
| SingleCellExperiment-compatible Functions | Description |
|-------------------------------------------|-------------------------------------------------------------------|
| all | After all tidySingleCellExperiment is a SingleCellExperiment object, just better |
| tidyverse Packages | Description |
|--------------------|-------------------------------------------------------------|
| dplyr | All dplyr tibble functions (e.g. tidySingleCellExperiment::select) |
| tidyr | All tidyr tibble functions (e.g. tidySingleCellExperiment::pivot_longer) |
| ggplot2 | ggplot (tidySingleCellExperiment::ggplot) |
| plotly | plot_ly (tidySingleCellExperiment::plot_ly) |
| Utilities | Description |
|--------------------|------------------------------------------------------------------|
| tidy | Add tidySingleCellExperiment invisible layer over a SingleCellExperiment object |
| as_tibble | Convert cell-wise information to a tbl_df |
| join_transcripts | Add transcript-wise information, returns a tbl_df |
Installation
From Bioconductor (under submission)
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("tidySingleCellExperiment")
From GitHub
devtools::install_github("stemangiola/tidySingleCellExperiment")
Load libraries used in this vignette.
# Bioconductor single-cell packages
library(scater)
library(scran)
library(SingleR)
library(SingleCellSignalR)
# Tidyverse-compatible packages
library(ggplot2)
library(purrr)
library(tidyHeatmap)
# Both
library(tidySingleCellExperiment)
Create tidySingleCellExperiment, the best of both worlds!
This is a SingleCellExperiment object but it is evaluated as a tibble.
So it is compatible both with SingleCellExperiment and tidyverse.
pbmc_small_tidy <- tidySingleCellExperiment::pbmc_small %>% tidy()
It looks like a tibble
pbmc_small_tidy
## # A tibble: 80 x 17
## cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
## <chr> <fct> <dbl> <int> <fct> <fct> <chr>
## 1 ATGC… SeuratPro… 70 47 0 A g2
## 2 CATG… SeuratPro… 85 52 0 A g1
## 3 GAAC… SeuratPro… 87 50 1 B g2
## 4 TGAC… SeuratPro… 127 56 0 A g2
## 5 AGTC… SeuratPro… 173 53 0 A g2
## 6 TCTG… SeuratPro… 70 48 0 A g1
## 7 TGGT… SeuratPro… 64 36 0 A g1
## 8 GCAG… SeuratPro… 72 45 0 A g1
## 9 GATA… SeuratPro… 52 36 0 A g1
## 10 AATG… SeuratPro… 100 41 0 A g1
## # … with 70 more rows, and 10 more variables: RNA_snn_res.1 <fct>, file <chr>,
## # ident <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>,
## # tSNE_1 <dbl>, tSNE_2 <dbl>
But it is a SingleCellExperiment object after all
pbmc_small_tidy@assays
## An object of class "SimpleAssays"
## Slot "data":
## List of length 2
## names(2): counts logcounts
Annotation polishing
We may have a column that contains the directory each run was taken
from, such as the “file” column in pbmc_small_tidy.
pbmc_small_tidy$file[1:5]
## [1] "../data/sample2/outs/filtered_feature_bc_matrix/"
## [2] "../data/sample1/outs/filtered_feature_bc_matrix/"
## [3] "../data/sample2/outs/filtered_feature_bc_matrix/"
## [4] "../data/sample2/outs/filtered_feature_bc_matrix/"
## [5] "../data/sample2/outs/filtered_feature_bc_matrix/"
We may want to extract the run/sample name out of it into a separate
column. Tidyverse extract can be used to convert a character column
into multiple columns using regular expression groups.
# Create sample column
pbmc_small_polished <-
pbmc_small_tidy %>%
extract(file, "sample", "../data/([a-z0-9]+)/outs.+", remove=FALSE)
# Reorder to have sample column up front
pbmc_small_polished %>%
select(sample, everything())
## # A tibble: 80 x 18
## cell sample orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents
## <chr> <chr> <fct> <dbl> <int> <fct> <fct>
## 1 ATGC… sampl… SeuratPro… 70 47 0 A
## 2 CATG… sampl… SeuratPro… 85 52 0 A
## 3 GAAC… sampl… SeuratPro… 87 50 1 B
## 4 TGAC… sampl… SeuratPro… 127 56 0 A
## 5 AGTC… sampl… SeuratPro… 173 53 0 A
## 6 TCTG… sampl… SeuratPro… 70 48 0 A
## 7 TGGT… sampl… SeuratPro… 64 36 0 A
## 8 GCAG… sampl… SeuratPro… 72 45 0 A
## 9 GATA… sampl… SeuratPro… 52 36 0 A
## 10 AATG… sampl… SeuratPro… 100 41 0 A
## # … with 70 more rows, and 11 more variables: groups <chr>,
## # RNA_snn_res.1 <fct>, file <chr>, ident <fct>, PC_1 <dbl>, PC_2 <dbl>,
## # PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
Preliminary plots
Set colours and theme for plots.
# Use colourblind-friendly colours
friendly_cols <- dittoSeq::dittoColors()
# Set theme
my_theme <-
list(
scale_fill_manual(values=friendly_cols),
scale_color_manual(values=friendly_cols),
theme_bw() +
theme(
panel.border=element_blank(),
axis.line=element_line(),
panel.grid.major=element_line(size=0.2),
panel.grid.minor=element_line(size=0.1),
text=element_text(size=12),
legend.position="bottom",
aspect.ratio=1,
strip.background=element_blank(),
axis.title.x=element_text(margin=margin(t=10, r=10, b=10, l=10)),
axis.title.y=element_text(margin=margin(t=10, r=10, b=10, l=10))
)
)
We can treat pbmc_small_polished as a tibble for plotting.
Here we plot number of transcripts per cell.
pbmc_small_polished %>%
tidySingleCellExperiment::ggplot(aes(nFeature_RNA, fill=groups)) +
geom_histogram() +
my_theme
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Here we plot total transcripts per cell.
pbmc_small_polished %>%
tidySingleCellExperiment::ggplot(aes(groups, nCount_RNA, fill=groups)) +
geom_boxplot(outlier.shape=NA) +
geom_jitter(width=0.1) +
my_theme
Here we plot abundance of two transcripts for each group.
pbmc_small_polished %>%
join_transcripts(transcripts=c("HLA-DRA", "LYZ")) %>%
ggplot(aes(groups, abundance_counts + 1, fill=groups)) +
geom_boxplot(outlier.shape=NA) +
geom_jitter(aes(size=nCount_RNA), alpha=0.5, width=0.2) +
scale_y_log10() +
my_theme
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
Preprocess the dataset
We can also treat pbmc_small_polished as a SingleCellExperiment
object and proceed with data processing with Bioconductor packages, such
as scran [@lun2016pooling] and scater [@mccarthy2017scater].
# Identify variable genes with scran
variable_genes <-
pbmc_small_polished %>%
modelGeneVar() %>%
getTopHVGs(prop=0.1)
# Perform PCA with scater
pbmc_small_pca <-
pbmc_small_polished %>%
runPCA(subset_row=variable_genes)
## Warning in check_numbers(k = k, nu = nu, nv = nv, limit = min(dim(x)) - : more
## singular values/vectors requested than available
## Warning in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth =
## TRUE, : You're computing too large a percentage of total singular values, use a
## standard svd instead.
pbmc_small_pca
## # A tibble: 80 x 18
## cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
## <chr> <fct> <dbl> <int> <fct> <fct> <chr>
## 1 ATGC… SeuratPro… 70 47 0 A g2
## 2 CATG… SeuratPro… 85 52 0 A g1
## 3 GAAC… SeuratPro… 87 50 1 B g2
## 4 TGAC… SeuratPro… 127 56 0 A g2
## 5 AGTC… SeuratPro… 173 53 0 A g2
## 6 TCTG… SeuratPro… 70 48 0 A g1
## 7 TGGT… SeuratPro… 64 36 0 A g1
## 8 GCAG… SeuratPro… 72 45 0 A g1
## 9 GATA… SeuratPro… 52 36 0 A g1
## 10 AATG… SeuratPro… 100 41 0 A g1
## # … with 70 more rows, and 11 more variables: RNA_snn_res.1 <fct>, file <chr>,
## # sample <chr>, ident <fct>, PC1 <dbl>, PC2 <dbl>, PC3 <dbl>, PC4 <dbl>,
## # PC5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
If a tidyverse-compatible package is not included in the tidySingleCellExperiment
collection, we can use as_tibble to permanently convert tidySingleCellExperiment into
a tibble.
# Create pairs plot with GGally
pbmc_small_pca %>%
as_tibble() %>%
select(contains("PC"), everything()) %>%
GGally::ggpairs(columns=1:5, ggplot2::aes(colour=groups)) +
my_theme
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
Identify clusters
We can proceed with cluster identification with scran.
pbmc_small_cluster <- pbmc_small_pca
# Assign clusters to the 'colLabels' of the SummarizedExperiment object
colLabels(pbmc_small_cluster) <-
pbmc_small_pca %>%
buildSNNGraph(use.dimred="PCA") %>%
igraph::cluster_walktrap() %$%
membership %>%
as.factor()
## Warning in (function (to_check, X, clust_centers, clust_info, dtype, nn, :
## detected tied distances to neighbors, see ?'BiocNeighbors-ties'
# Reorder columns
pbmc_small_cluster %>% select(label, everything())
## # A tibble: 80 x 19
## cell label orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents
## <chr> <fct> <fct> <dbl> <int> <fct> <fct>
## 1 ATGC… 2 SeuratPro… 70 47 0 A
## 2 CATG… 2 SeuratPro… 85 52 0 A
## 3 GAAC… 2 SeuratPro… 87 50 1 B
## 4 TGAC… 1 SeuratPro… 127 56 0 A
## 5 AGTC… 2 SeuratPro… 173 53 0 A
## 6 TCTG… 2 SeuratPro… 70 48 0 A
## 7 TGGT… 1 SeuratPro… 64 36 0 A
## 8 GCAG… 2 SeuratPro… 72 45 0 A
## 9 GATA… 2 SeuratPro… 52 36 0 A
## 10 AATG… 2 SeuratPro… 100 41 0 A
## # … with 70 more rows, and 12 more variables: groups <chr>,
## # RNA_snn_res.1 <fct>, file <chr>, sample <chr>, ident <fct>, PC1 <dbl>,
## # PC2 <dbl>, PC3 <dbl>, PC4 <dbl>, PC5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
And interrogate the output as if it was a regular tibble.
# Count number of cells for each cluster per group
pbmc_small_cluster %>%
tidySingleCellExperiment::count(groups, label)
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
## # A tibble: 8 x 3
## groups label n
## <chr> <fct> <int>
## 1 g1 1 12
## 2 g1 2 14
## 3 g1 3 14
## 4 g1 4 4
## 5 g2 1 10
## 6 g2 2 11
## 7 g2 3 10
## 8 g2 4 5
We can identify and visualise cluster markers combining
SingleCellExperiment, tidyverse functions and tidyHeatmap
[@mangiola2020tidyheatmap]
# Identify top 10 markers per cluster
marker_genes <-
pbmc_small_cluster %>%
findMarkers(groups=pbmc_small_cluster$label) %>%
as.list() %>%
map(~ .x %>%
head(10) %>%
rownames()) %>%
unlist()
# Plot heatmap
pbmc_small_cluster %>%
join_transcripts(transcripts=marker_genes) %>%
group_by(label) %>%
heatmap(transcript, cell, abundance_counts, .scale="column")
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
Reduce dimensions
We can calculate the first 3 UMAP dimensions using the
SingleCellExperiment framework and scater.
pbmc_small_UMAP <-
pbmc_small_cluster %>%
runUMAP(ncomponents=3)
And we can plot the result in 3D using plotly.
pbmc_small_UMAP %>%
plot_ly(
x=~`UMAP1`,
y=~`UMAP2`,
z=~`UMAP3`,
color=~label,
colors=friendly_cols[1:4]
)
Cell type prediction
We can infer cell type identities using SingleR [@aran2019reference]
and manipulate the output using tidyverse.
# Get cell type reference data
blueprint <- celldex::BlueprintEncodeData()
## snapshotDate(): 2020-09-04
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
# Infer cell identities
cell_type_df <-
pbmc_small_UMAP@assays@data$logcounts %>%
Matrix::Matrix(sparse=TRUE) %>%
SingleR(
ref=blueprint,
labels=blueprint$label.main
) %>%
as.data.frame() %>%
as_tibble(rownames="cell") %>%
select(cell, first.labels)
# Join UMAP and cell type info
pbmc_small_cell_type <-
pbmc_small_UMAP %>%
left_join(cell_type_df, by="cell")
# Reorder columns
pbmc_small_cell_type %>%
tidySingleCellExperiment::select(cell, first.labels, everything())
## # A tibble: 80 x 23
## cell first.labels orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8
## <chr> <chr> <fct> <dbl> <int> <fct>
## 1 ATGC… CD4+ T-cells SeuratPro… 70 47 0
## 2 CATG… CD8+ T-cells SeuratPro… 85 52 0
## 3 GAAC… CD8+ T-cells SeuratPro… 87 50 1
## 4 TGAC… CD4+ T-cells SeuratPro… 127 56 0
## 5 AGTC… CD4+ T-cells SeuratPro… 173 53 0
## 6 TCTG… CD4+ T-cells SeuratPro… 70 48 0
## 7 TGGT… CD4+ T-cells SeuratPro… 64 36 0
## 8 GCAG… CD4+ T-cells SeuratPro… 72 45 0
## 9 GATA… CD8+ T-cells SeuratPro… 52 36 0
## 10 AATG… CD4+ T-cells SeuratPro… 100 41 0
## # … with 70 more rows, and 17 more variables: letter.idents <fct>,
## # groups <chr>, RNA_snn_res.1 <fct>, file <chr>, sample <chr>, ident <fct>,
## # label <fct>, PC1 <dbl>, PC2 <dbl>, PC3 <dbl>, PC4 <dbl>, PC5 <dbl>,
## # tSNE_1 <dbl>, tSNE_2 <dbl>, UMAP1 <dbl>, UMAP2 <dbl>, UMAP3 <dbl>
We can easily summarise the results. For example, we can see how cell
type classification overlaps with cluster classification.
# Count number of cells for each cell type per cluster
pbmc_small_cell_type %>%
count(label, first.labels)
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
## # A tibble: 9 x 3
## label first.labels n
## <fct> <chr> <int>
## 1 1 CD4+ T-cells 2
## 2 1 CD8+ T-cells 8
## 3 1 NK cells 12
## 4 2 B-cells 10
## 5 2 CD4+ T-cells 5
## 6 2 CD8+ T-cells 3
## 7 2 Monocytes 7
## 8 3 Monocytes 24
## 9 4 Erythrocytes 9
We can easily reshape the data for building information-rich faceted
plots.
pbmc_small_cell_type %>%
# Reshape and add classifier column
pivot_longer(
cols=c(label, first.labels),
names_to="classifier", values_to="label"
) %>%
# UMAP plots for cell type and cluster
ggplot(aes(UMAP1, UMAP2, color=label)) +
geom_point() +
facet_wrap(~classifier) +
my_theme
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
We can easily plot gene correlation per cell category, adding
multi-layer annotations.
pbmc_small_cell_type %>%
# Add some mitochondrial abundance values
mutate(mitochondrial=rnorm(dplyr::n())) %>%
# Plot correlation
join_transcripts(transcripts=c("CST3", "LYZ"), shape="wide") %>%
ggplot(aes(CST3 + 1, LYZ + 1, color=groups, size=mitochondrial)) +
geom_point() +
facet_wrap(~first.labels, scales="free") +
scale_x_log10() +
scale_y_log10() +
my_theme
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
Nested analyses
A powerful tool we can use with tidySingleCellExperiment is tidyverse nest. We can
easily perform independent analyses on subsets of the dataset. First we
classify cell types into lymphoid and myeloid, and then nest based on
the new classification.
pbmc_small_nested <-
pbmc_small_cell_type %>%
filter(first.labels != "Erythrocytes") %>%
mutate(cell_class=dplyr::if_else(`first.labels` %in% c("Macrophages", "Monocytes"), "myeloid", "lymphoid")) %>%
nest(data=-cell_class)
pbmc_small_nested
## # A tibble: 2 x 2
## cell_class data
## <chr> <list>
## 1 lymphoid <tidySingleCellExperiment>
## 2 myeloid <tidySingleCellExperiment>
Now we can independently for the lymphoid and myeloid subsets (i) find
variable features, (ii) reduce dimensions, and (iii) cluster using both
tidyverse and SingleCellExperiment seamlessly.
pbmc_small_nested_reanalysed <-
pbmc_small_nested %>%
mutate(data=map(
data, ~ {
.x <- runPCA(.x, subset_row=variable_genes)
variable_genes <-
.x %>%
modelGeneVar() %>%
getTopHVGs(prop=0.3)
colLabels(.x) <-
.x %>%
buildSNNGraph(use.dimred="PCA") %>%
igraph::cluster_walktrap() %$%
membership %>%
as.factor()
.x %>% runUMAP(ncomponents=3)
}
))
pbmc_small_nested_reanalysed
## # A tibble: 2 x 2
## cell_class data
## <chr> <list>
## 1 lymphoid <tidySingleCellExperiment>
## 2 myeloid <tidySingleCellExperiment>
We can then unnest and plot the new classification.
pbmc_small_nested_reanalysed %>%
# Convert to tibble otherwise SingleCellExperiment drops reduced dimensions when unifying data sets.
mutate(data=map(data, ~ .x %>% as_tibble())) %>%
unnest(data) %>%
# Define unique clusters
unite("cluster", c(cell_class, label), remove=FALSE) %>%
# Plotting
ggplot(aes(UMAP1, UMAP2, color=cluster)) +
geom_point() +
facet_wrap(~cell_class) +
my_theme
We can perform a large number of functional analyses on data subsets.
For example, we can identify intra-sample cell-cell interactions using
SingleCellSignalR [@cabello2020singlecellsignalr], and then compare
whether interactions are stronger or weaker across conditions. The code
below demonstrates how this analysis could be performed. It won’t work
with this small example dataset as we have just two samples (one for
each condition). But some example output is shown below and you can
imagine how you can use tidyverse on the output to perform t-tests and
visualisation.
pbmc_small_nested_interactions <-
pbmc_small_nested_reanalysed %>%
# Unnest based on cell category
unnest(data) %>%
# Create unambiguous clusters
mutate(integrated_clusters=first.labels %>% as.factor() %>% as.integer()) %>%
# Nest based on sample
tidySingleCellExperiment::nest(data=-sample) %>%
tidySingleCellExperiment::mutate(interactions=map(data, ~ {
# Produce variables. Yuck!
cluster <- .x@colData$integrated_clusters
data <- data.frame(.x@assays@data %>% as.list() %>% .[[1]] %>% as.matrix())
# Ligand/Receptor analysis using SingleCellSignalR
data %>%
cell_signaling(genes=rownames(data), cluster=cluster) %>%
inter_network(data=data, signal=., genes=rownames(data), cluster=cluster) %$%
`individual-networks` %>%
map_dfr(~ bind_rows(as_tibble(.x)))
}))
pbmc_small_nested_interactions %>%
select(-data) %>%
unnest(interactions)
If the dataset was not so small, and interactions could be identified,
you would see something like below.
tidySingleCellExperiment::pbmc_small_nested_interactions
## # A tibble: 100 x 9
## sample ligand receptor ligand.name receptor.name origin destination
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 sampl… clust… cluster… PTMA VIPR1 clust… cluster 2
## 2 sampl… clust… cluster… B2M KLRD1 clust… cluster 2
## 3 sampl… clust… cluster… IL16 CD4 clust… cluster 2
## 4 sampl… clust… cluster… HLA-B KLRD1 clust… cluster 2
## 5 sampl… clust… cluster… CALM1 VIPR1 clust… cluster 2
## 6 sampl… clust… cluster… HLA-E KLRD1 clust… cluster 2
## 7 sampl… clust… cluster… GNAS VIPR1 clust… cluster 2
## 8 sampl… clust… cluster… B2M HFE clust… cluster 2
## 9 sampl… clust… cluster… PTMA VIPR1 clust… cluster 3
## 10 sampl… clust… cluster… CALM1 VIPR1 clust… cluster 3
## # … with 90 more rows, and 2 more variables: interaction.type <chr>,
## # LRscore <dbl>
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tidySingleCellExperiment - part of tidytranscriptomics
Brings SingleCellExperiment to the tidyverse!
Website: tidySingleCellExperiment
Please also have a look at
- tidyseurat for tidy manipulation of Seurat objects
- tidybulk for tidy bulk RNA-seq data analysis
- nanny for tidy high-level data analysis and manipulation
- tidygate for adding custom gate information to your tibble
- tidyHeatmap for heatmaps produced with tidy principles
Introduction
tidySingleCellExperiment provides a bridge between Bioconductor single-cell packages [@amezquita2019orchestrating] and the tidyverse [@wickham2019welcome]. It creates an invisible layer that enables viewing the Bioconductor SingleCellExperiment object as a tidyverse tibble, and provides SingleCellExperiment-compatible dplyr, tidyr, ggplot and plotly functions. This allows users to get the best of both Bioconductor and tidyverse worlds.
Functions/utilities available
| SingleCellExperiment-compatible Functions | Description |
|-------------------------------------------|-------------------------------------------------------------------|
| all | After all tidySingleCellExperiment is a SingleCellExperiment object, just better |
| tidyverse Packages | Description |
|--------------------|-------------------------------------------------------------|
| dplyr | All dplyr tibble functions (e.g. tidySingleCellExperiment::select) |
| tidyr | All tidyr tibble functions (e.g. tidySingleCellExperiment::pivot_longer) |
| ggplot2 | ggplot (tidySingleCellExperiment::ggplot) |
| plotly | plot_ly (tidySingleCellExperiment::plot_ly) |
| Utilities | Description |
|--------------------|------------------------------------------------------------------|
| tidy | Add tidySingleCellExperiment invisible layer over a SingleCellExperiment object |
| as_tibble | Convert cell-wise information to a tbl_df |
| join_transcripts | Add transcript-wise information, returns a tbl_df |
Installation
From Bioconductor (under submission)
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("tidySingleCellExperiment")
From GitHub
devtools::install_github("stemangiola/tidySingleCellExperiment")
Load libraries used in this vignette.
# Bioconductor single-cell packages
library(scater)
library(scran)
library(SingleR)
library(SingleCellSignalR)
# Tidyverse-compatible packages
library(ggplot2)
library(purrr)
library(tidyHeatmap)
# Both
library(tidySingleCellExperiment)
Create tidySingleCellExperiment, the best of both worlds!
This is a SingleCellExperiment object but it is evaluated as a tibble. So it is compatible both with SingleCellExperiment and tidyverse.
pbmc_small_tidy <- tidySingleCellExperiment::pbmc_small %>% tidy()
It looks like a tibble
pbmc_small_tidy
## # A tibble: 80 x 17
## cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
## <chr> <fct> <dbl> <int> <fct> <fct> <chr>
## 1 ATGC… SeuratPro… 70 47 0 A g2
## 2 CATG… SeuratPro… 85 52 0 A g1
## 3 GAAC… SeuratPro… 87 50 1 B g2
## 4 TGAC… SeuratPro… 127 56 0 A g2
## 5 AGTC… SeuratPro… 173 53 0 A g2
## 6 TCTG… SeuratPro… 70 48 0 A g1
## 7 TGGT… SeuratPro… 64 36 0 A g1
## 8 GCAG… SeuratPro… 72 45 0 A g1
## 9 GATA… SeuratPro… 52 36 0 A g1
## 10 AATG… SeuratPro… 100 41 0 A g1
## # … with 70 more rows, and 10 more variables: RNA_snn_res.1 <fct>, file <chr>,
## # ident <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>,
## # tSNE_1 <dbl>, tSNE_2 <dbl>
But it is a SingleCellExperiment object after all
pbmc_small_tidy@assays
## An object of class "SimpleAssays"
## Slot "data":
## List of length 2
## names(2): counts logcounts
Annotation polishing
We may have a column that contains the directory each run was taken
from, such as the “file” column in pbmc_small_tidy.
pbmc_small_tidy$file[1:5]
## [1] "../data/sample2/outs/filtered_feature_bc_matrix/"
## [2] "../data/sample1/outs/filtered_feature_bc_matrix/"
## [3] "../data/sample2/outs/filtered_feature_bc_matrix/"
## [4] "../data/sample2/outs/filtered_feature_bc_matrix/"
## [5] "../data/sample2/outs/filtered_feature_bc_matrix/"
We may want to extract the run/sample name out of it into a separate
column. Tidyverse extract can be used to convert a character column
into multiple columns using regular expression groups.
# Create sample column
pbmc_small_polished <-
pbmc_small_tidy %>%
extract(file, "sample", "../data/([a-z0-9]+)/outs.+", remove=FALSE)
# Reorder to have sample column up front
pbmc_small_polished %>%
select(sample, everything())
## # A tibble: 80 x 18
## cell sample orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents
## <chr> <chr> <fct> <dbl> <int> <fct> <fct>
## 1 ATGC… sampl… SeuratPro… 70 47 0 A
## 2 CATG… sampl… SeuratPro… 85 52 0 A
## 3 GAAC… sampl… SeuratPro… 87 50 1 B
## 4 TGAC… sampl… SeuratPro… 127 56 0 A
## 5 AGTC… sampl… SeuratPro… 173 53 0 A
## 6 TCTG… sampl… SeuratPro… 70 48 0 A
## 7 TGGT… sampl… SeuratPro… 64 36 0 A
## 8 GCAG… sampl… SeuratPro… 72 45 0 A
## 9 GATA… sampl… SeuratPro… 52 36 0 A
## 10 AATG… sampl… SeuratPro… 100 41 0 A
## # … with 70 more rows, and 11 more variables: groups <chr>,
## # RNA_snn_res.1 <fct>, file <chr>, ident <fct>, PC_1 <dbl>, PC_2 <dbl>,
## # PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
Preliminary plots
Set colours and theme for plots.
# Use colourblind-friendly colours
friendly_cols <- dittoSeq::dittoColors()
# Set theme
my_theme <-
list(
scale_fill_manual(values=friendly_cols),
scale_color_manual(values=friendly_cols),
theme_bw() +
theme(
panel.border=element_blank(),
axis.line=element_line(),
panel.grid.major=element_line(size=0.2),
panel.grid.minor=element_line(size=0.1),
text=element_text(size=12),
legend.position="bottom",
aspect.ratio=1,
strip.background=element_blank(),
axis.title.x=element_text(margin=margin(t=10, r=10, b=10, l=10)),
axis.title.y=element_text(margin=margin(t=10, r=10, b=10, l=10))
)
)
We can treat pbmc_small_polished as a tibble for plotting.
Here we plot number of transcripts per cell.
pbmc_small_polished %>%
tidySingleCellExperiment::ggplot(aes(nFeature_RNA, fill=groups)) +
geom_histogram() +
my_theme
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Here we plot total transcripts per cell.
pbmc_small_polished %>%
tidySingleCellExperiment::ggplot(aes(groups, nCount_RNA, fill=groups)) +
geom_boxplot(outlier.shape=NA) +
geom_jitter(width=0.1) +
my_theme
Here we plot abundance of two transcripts for each group.
pbmc_small_polished %>%
join_transcripts(transcripts=c("HLA-DRA", "LYZ")) %>%
ggplot(aes(groups, abundance_counts + 1, fill=groups)) +
geom_boxplot(outlier.shape=NA) +
geom_jitter(aes(size=nCount_RNA), alpha=0.5, width=0.2) +
scale_y_log10() +
my_theme
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
Preprocess the dataset
We can also treat pbmc_small_polished as a SingleCellExperiment
object and proceed with data processing with Bioconductor packages, such
as scran [@lun2016pooling] and scater [@mccarthy2017scater].
# Identify variable genes with scran
variable_genes <-
pbmc_small_polished %>%
modelGeneVar() %>%
getTopHVGs(prop=0.1)
# Perform PCA with scater
pbmc_small_pca <-
pbmc_small_polished %>%
runPCA(subset_row=variable_genes)
## Warning in check_numbers(k = k, nu = nu, nv = nv, limit = min(dim(x)) - : more
## singular values/vectors requested than available
## Warning in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth =
## TRUE, : You're computing too large a percentage of total singular values, use a
## standard svd instead.
pbmc_small_pca
## # A tibble: 80 x 18
## cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
## <chr> <fct> <dbl> <int> <fct> <fct> <chr>
## 1 ATGC… SeuratPro… 70 47 0 A g2
## 2 CATG… SeuratPro… 85 52 0 A g1
## 3 GAAC… SeuratPro… 87 50 1 B g2
## 4 TGAC… SeuratPro… 127 56 0 A g2
## 5 AGTC… SeuratPro… 173 53 0 A g2
## 6 TCTG… SeuratPro… 70 48 0 A g1
## 7 TGGT… SeuratPro… 64 36 0 A g1
## 8 GCAG… SeuratPro… 72 45 0 A g1
## 9 GATA… SeuratPro… 52 36 0 A g1
## 10 AATG… SeuratPro… 100 41 0 A g1
## # … with 70 more rows, and 11 more variables: RNA_snn_res.1 <fct>, file <chr>,
## # sample <chr>, ident <fct>, PC1 <dbl>, PC2 <dbl>, PC3 <dbl>, PC4 <dbl>,
## # PC5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
If a tidyverse-compatible package is not included in the tidySingleCellExperiment
collection, we can use as_tibble to permanently convert tidySingleCellExperiment into
a tibble.
# Create pairs plot with GGally
pbmc_small_pca %>%
as_tibble() %>%
select(contains("PC"), everything()) %>%
GGally::ggpairs(columns=1:5, ggplot2::aes(colour=groups)) +
my_theme
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
Identify clusters
We can proceed with cluster identification with scran.
pbmc_small_cluster <- pbmc_small_pca
# Assign clusters to the 'colLabels' of the SummarizedExperiment object
colLabels(pbmc_small_cluster) <-
pbmc_small_pca %>%
buildSNNGraph(use.dimred="PCA") %>%
igraph::cluster_walktrap() %$%
membership %>%
as.factor()
## Warning in (function (to_check, X, clust_centers, clust_info, dtype, nn, :
## detected tied distances to neighbors, see ?'BiocNeighbors-ties'
# Reorder columns
pbmc_small_cluster %>% select(label, everything())
## # A tibble: 80 x 19
## cell label orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents
## <chr> <fct> <fct> <dbl> <int> <fct> <fct>
## 1 ATGC… 2 SeuratPro… 70 47 0 A
## 2 CATG… 2 SeuratPro… 85 52 0 A
## 3 GAAC… 2 SeuratPro… 87 50 1 B
## 4 TGAC… 1 SeuratPro… 127 56 0 A
## 5 AGTC… 2 SeuratPro… 173 53 0 A
## 6 TCTG… 2 SeuratPro… 70 48 0 A
## 7 TGGT… 1 SeuratPro… 64 36 0 A
## 8 GCAG… 2 SeuratPro… 72 45 0 A
## 9 GATA… 2 SeuratPro… 52 36 0 A
## 10 AATG… 2 SeuratPro… 100 41 0 A
## # … with 70 more rows, and 12 more variables: groups <chr>,
## # RNA_snn_res.1 <fct>, file <chr>, sample <chr>, ident <fct>, PC1 <dbl>,
## # PC2 <dbl>, PC3 <dbl>, PC4 <dbl>, PC5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
And interrogate the output as if it was a regular tibble.
# Count number of cells for each cluster per group
pbmc_small_cluster %>%
tidySingleCellExperiment::count(groups, label)
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
## # A tibble: 8 x 3
## groups label n
## <chr> <fct> <int>
## 1 g1 1 12
## 2 g1 2 14
## 3 g1 3 14
## 4 g1 4 4
## 5 g2 1 10
## 6 g2 2 11
## 7 g2 3 10
## 8 g2 4 5
We can identify and visualise cluster markers combining SingleCellExperiment, tidyverse functions and tidyHeatmap [@mangiola2020tidyheatmap]
# Identify top 10 markers per cluster
marker_genes <-
pbmc_small_cluster %>%
findMarkers(groups=pbmc_small_cluster$label) %>%
as.list() %>%
map(~ .x %>%
head(10) %>%
rownames()) %>%
unlist()
# Plot heatmap
pbmc_small_cluster %>%
join_transcripts(transcripts=marker_genes) %>%
group_by(label) %>%
heatmap(transcript, cell, abundance_counts, .scale="column")
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
Reduce dimensions
We can calculate the first 3 UMAP dimensions using the SingleCellExperiment framework and scater.
pbmc_small_UMAP <-
pbmc_small_cluster %>%
runUMAP(ncomponents=3)
And we can plot the result in 3D using plotly.
pbmc_small_UMAP %>%
plot_ly(
x=~`UMAP1`,
y=~`UMAP2`,
z=~`UMAP3`,
color=~label,
colors=friendly_cols[1:4]
)
Cell type prediction
We can infer cell type identities using SingleR [@aran2019reference] and manipulate the output using tidyverse.
# Get cell type reference data
blueprint <- celldex::BlueprintEncodeData()
## snapshotDate(): 2020-09-04
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
# Infer cell identities
cell_type_df <-
pbmc_small_UMAP@assays@data$logcounts %>%
Matrix::Matrix(sparse=TRUE) %>%
SingleR(
ref=blueprint,
labels=blueprint$label.main
) %>%
as.data.frame() %>%
as_tibble(rownames="cell") %>%
select(cell, first.labels)
# Join UMAP and cell type info
pbmc_small_cell_type <-
pbmc_small_UMAP %>%
left_join(cell_type_df, by="cell")
# Reorder columns
pbmc_small_cell_type %>%
tidySingleCellExperiment::select(cell, first.labels, everything())
## # A tibble: 80 x 23
## cell first.labels orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8
## <chr> <chr> <fct> <dbl> <int> <fct>
## 1 ATGC… CD4+ T-cells SeuratPro… 70 47 0
## 2 CATG… CD8+ T-cells SeuratPro… 85 52 0
## 3 GAAC… CD8+ T-cells SeuratPro… 87 50 1
## 4 TGAC… CD4+ T-cells SeuratPro… 127 56 0
## 5 AGTC… CD4+ T-cells SeuratPro… 173 53 0
## 6 TCTG… CD4+ T-cells SeuratPro… 70 48 0
## 7 TGGT… CD4+ T-cells SeuratPro… 64 36 0
## 8 GCAG… CD4+ T-cells SeuratPro… 72 45 0
## 9 GATA… CD8+ T-cells SeuratPro… 52 36 0
## 10 AATG… CD4+ T-cells SeuratPro… 100 41 0
## # … with 70 more rows, and 17 more variables: letter.idents <fct>,
## # groups <chr>, RNA_snn_res.1 <fct>, file <chr>, sample <chr>, ident <fct>,
## # label <fct>, PC1 <dbl>, PC2 <dbl>, PC3 <dbl>, PC4 <dbl>, PC5 <dbl>,
## # tSNE_1 <dbl>, tSNE_2 <dbl>, UMAP1 <dbl>, UMAP2 <dbl>, UMAP3 <dbl>
We can easily summarise the results. For example, we can see how cell type classification overlaps with cluster classification.
# Count number of cells for each cell type per cluster
pbmc_small_cell_type %>%
count(label, first.labels)
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
## # A tibble: 9 x 3
## label first.labels n
## <fct> <chr> <int>
## 1 1 CD4+ T-cells 2
## 2 1 CD8+ T-cells 8
## 3 1 NK cells 12
## 4 2 B-cells 10
## 5 2 CD4+ T-cells 5
## 6 2 CD8+ T-cells 3
## 7 2 Monocytes 7
## 8 3 Monocytes 24
## 9 4 Erythrocytes 9
We can easily reshape the data for building information-rich faceted plots.
pbmc_small_cell_type %>%
# Reshape and add classifier column
pivot_longer(
cols=c(label, first.labels),
names_to="classifier", values_to="label"
) %>%
# UMAP plots for cell type and cluster
ggplot(aes(UMAP1, UMAP2, color=label)) +
geom_point() +
facet_wrap(~classifier) +
my_theme
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
We can easily plot gene correlation per cell category, adding multi-layer annotations.
pbmc_small_cell_type %>%
# Add some mitochondrial abundance values
mutate(mitochondrial=rnorm(dplyr::n())) %>%
# Plot correlation
join_transcripts(transcripts=c("CST3", "LYZ"), shape="wide") %>%
ggplot(aes(CST3 + 1, LYZ + 1, color=groups, size=mitochondrial)) +
geom_point() +
facet_wrap(~first.labels, scales="free") +
scale_x_log10() +
scale_y_log10() +
my_theme
## tidySingleCellExperiment says: A data frame is returned for independent data analysis.
Nested analyses
A powerful tool we can use with tidySingleCellExperiment is tidyverse nest. We can
easily perform independent analyses on subsets of the dataset. First we
classify cell types into lymphoid and myeloid, and then nest based on
the new classification.
pbmc_small_nested <-
pbmc_small_cell_type %>%
filter(first.labels != "Erythrocytes") %>%
mutate(cell_class=dplyr::if_else(`first.labels` %in% c("Macrophages", "Monocytes"), "myeloid", "lymphoid")) %>%
nest(data=-cell_class)
pbmc_small_nested
## # A tibble: 2 x 2
## cell_class data
## <chr> <list>
## 1 lymphoid <tidySingleCellExperiment>
## 2 myeloid <tidySingleCellExperiment>
Now we can independently for the lymphoid and myeloid subsets (i) find variable features, (ii) reduce dimensions, and (iii) cluster using both tidyverse and SingleCellExperiment seamlessly.
pbmc_small_nested_reanalysed <-
pbmc_small_nested %>%
mutate(data=map(
data, ~ {
.x <- runPCA(.x, subset_row=variable_genes)
variable_genes <-
.x %>%
modelGeneVar() %>%
getTopHVGs(prop=0.3)
colLabels(.x) <-
.x %>%
buildSNNGraph(use.dimred="PCA") %>%
igraph::cluster_walktrap() %$%
membership %>%
as.factor()
.x %>% runUMAP(ncomponents=3)
}
))
pbmc_small_nested_reanalysed
## # A tibble: 2 x 2
## cell_class data
## <chr> <list>
## 1 lymphoid <tidySingleCellExperiment>
## 2 myeloid <tidySingleCellExperiment>
We can then unnest and plot the new classification.
pbmc_small_nested_reanalysed %>%
# Convert to tibble otherwise SingleCellExperiment drops reduced dimensions when unifying data sets.
mutate(data=map(data, ~ .x %>% as_tibble())) %>%
unnest(data) %>%
# Define unique clusters
unite("cluster", c(cell_class, label), remove=FALSE) %>%
# Plotting
ggplot(aes(UMAP1, UMAP2, color=cluster)) +
geom_point() +
facet_wrap(~cell_class) +
my_theme
We can perform a large number of functional analyses on data subsets. For example, we can identify intra-sample cell-cell interactions using SingleCellSignalR [@cabello2020singlecellsignalr], and then compare whether interactions are stronger or weaker across conditions. The code below demonstrates how this analysis could be performed. It won’t work with this small example dataset as we have just two samples (one for each condition). But some example output is shown below and you can imagine how you can use tidyverse on the output to perform t-tests and visualisation.
pbmc_small_nested_interactions <-
pbmc_small_nested_reanalysed %>%
# Unnest based on cell category
unnest(data) %>%
# Create unambiguous clusters
mutate(integrated_clusters=first.labels %>% as.factor() %>% as.integer()) %>%
# Nest based on sample
tidySingleCellExperiment::nest(data=-sample) %>%
tidySingleCellExperiment::mutate(interactions=map(data, ~ {
# Produce variables. Yuck!
cluster <- .x@colData$integrated_clusters
data <- data.frame(.x@assays@data %>% as.list() %>% .[[1]] %>% as.matrix())
# Ligand/Receptor analysis using SingleCellSignalR
data %>%
cell_signaling(genes=rownames(data), cluster=cluster) %>%
inter_network(data=data, signal=., genes=rownames(data), cluster=cluster) %$%
`individual-networks` %>%
map_dfr(~ bind_rows(as_tibble(.x)))
}))
pbmc_small_nested_interactions %>%
select(-data) %>%
unnest(interactions)
If the dataset was not so small, and interactions could be identified, you would see something like below.
tidySingleCellExperiment::pbmc_small_nested_interactions
## # A tibble: 100 x 9
## sample ligand receptor ligand.name receptor.name origin destination
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 sampl… clust… cluster… PTMA VIPR1 clust… cluster 2
## 2 sampl… clust… cluster… B2M KLRD1 clust… cluster 2
## 3 sampl… clust… cluster… IL16 CD4 clust… cluster 2
## 4 sampl… clust… cluster… HLA-B KLRD1 clust… cluster 2
## 5 sampl… clust… cluster… CALM1 VIPR1 clust… cluster 2
## 6 sampl… clust… cluster… HLA-E KLRD1 clust… cluster 2
## 7 sampl… clust… cluster… GNAS VIPR1 clust… cluster 2
## 8 sampl… clust… cluster… B2M HFE clust… cluster 2
## 9 sampl… clust… cluster… PTMA VIPR1 clust… cluster 3
## 10 sampl… clust… cluster… CALM1 VIPR1 clust… cluster 3
## # … with 90 more rows, and 2 more variables: interaction.type <chr>,
## # LRscore <dbl>
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