Brings SingleCellExperiment to the tidyverse!

Please also have a look at

tidySummarizedExperiment for tidy manipulation of SummarizedExperiment objects)
tidyseurat for tidy manipulation of Seurat objects
tidybulk for tidy bulk RNA-seq data analysis
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 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. `select`)
`tidyr`	All `tidyr` tibble functions (e.g. `pivot_longer`)
`ggplot2`	`ggplot` (`ggplot`)
`plotly`	`plot_ly` (`plot_ly`)

Utilities	Description
`as_tibble`	Convert cell-wise information to a `tbl_df`
`join_features`	Add feature-wise information, returns a `tbl_df`
`aggregate_cells`	Aggregate cell gene-transcription abundance as pseudobulk tissue

Installation

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")

BiocManager::install("tidySingleCellExperiment")

Load libraries used in this vignette.

# Bioconductor single-cell packages
library(scater)
library(scran)
library(SingleR)
library(SingleCellSignalR)

# Tidyverse-compatible packages
library(purrr)
library(magrittr)
library(tidyHeatmap)

# Both
library(tidySingleCellExperiment)

Data representation of `tidySingleCellExperiment`

This is a SingleCellExperiment object but it is evaluated as a tibble. So it is compatible both with SingleCellExperiment and tidyverse.

data(pbmc_small, package="tidySingleCellExperiment")

It looks like a tibble

pbmc_small

## [90m# A SingleCellExperiment-tibble abstraction: 80 × 17[39m
## [90m# [90mFeatures=230 | Cells=80 | Assays=counts, logcounts[0m[39m
##    .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
##    [3m[90m<chr>[39m[23m [3m[90m<fct>[39m[23m           [3m[90m<dbl>[39m[23m        [3m[90m<int>[39m[23m [3m[90m<fct>[39m[23m           [3m[90m<fct>[39m[23m         [3m[90m<chr>[39m[23m 
## [90m 1[39m ATGC… SeuratPro…         70           47 0               A             g2    
## [90m 2[39m CATG… SeuratPro…         85           52 0               A             g1    
## [90m 3[39m GAAC… SeuratPro…         87           50 1               B             g2    
## [90m 4[39m TGAC… SeuratPro…        127           56 0               A             g2    
## [90m 5[39m AGTC… SeuratPro…        173           53 0               A             g2    
## [90m 6[39m TCTG… SeuratPro…         70           48 0               A             g1    
## [90m 7[39m TGGT… SeuratPro…         64           36 0               A             g1    
## [90m 8[39m GCAG… SeuratPro…         72           45 0               A             g1    
## [90m 9[39m GATA… SeuratPro…         52           36 0               A             g1    
## [90m10[39m AATG… SeuratPro…        100           41 0               A             g1    
## [90m# ℹ 70 more rows[39m
## [90m# ℹ 10 more variables: RNA_snn_res.1 <fct>, file <chr>, ident <fct>,[39m
## [90m#   PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>,[39m
## [90m#   tSNE_2 <dbl>[39m

But it is a SingleCellExperiment object after all

assay(pbmc_small, "counts")[1:5, 1:5]

## 5 x 5 sparse Matrix of class "dgCMatrix"
##         ATGCCAGAACGACT CATGGCCTGTGCAT GAACCTGATGAACC TGACTGGATTCTCA
## MS4A1                .              .              .              .
## CD79B                1              .              .              .
## CD79A                .              .              .              .
## HLA-DRA              .              1              .              .
## TCL1A                .              .              .              .
##         AGTCAGACTGCACA
## MS4A1                .
## CD79B                .
## CD79A                .
## HLA-DRA              1
## TCL1A                .

The SingleCellExperiment object’s tibble visualisation can be turned off, or back on at any time.

# Turn off the tibble visualisation
options("restore_SingleCellExperiment_show" = TRUE)
pbmc_small

## class: SingleCellExperiment 
## dim: 230 80 
## metadata(0):
## assays(2): counts logcounts
## rownames(230): MS4A1 CD79B ... SPON2 S100B
## rowData names(5): vst.mean vst.variance vst.variance.expected
##   vst.variance.standardized vst.variable
## colnames(80): ATGCCAGAACGACT CATGGCCTGTGCAT ... GGAACACTTCAGAC
##   CTTGATTGATCTTC
## colData names(9): orig.ident nCount_RNA ... file ident

# Turn on the tibble visualisation
options("restore_SingleCellExperiment_show" = FALSE)

Annotation polishing

We may have a column that contains the directory each run was taken from, such as the “file” column in pbmc_small.

pbmc_small$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 |>
    extract(file, "sample", "../data/([a-z0-9]+)/outs.+", remove=FALSE)

# Reorder to have sample column up front
pbmc_small_polished |>
    select(sample, everything())

## [90m# A SingleCellExperiment-tibble abstraction: 80 × 18[39m
## [90m# [90mFeatures=230 | Cells=80 | Assays=counts, logcounts[0m[39m
##    .cell sample orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents
##    [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m  [3m[90m<fct>[39m[23m           [3m[90m<dbl>[39m[23m        [3m[90m<int>[39m[23m [3m[90m<fct>[39m[23m           [3m[90m<fct>[39m[23m        
## [90m 1[39m ATGC… sampl… SeuratPro…         70           47 0               A            
## [90m 2[39m CATG… sampl… SeuratPro…         85           52 0               A            
## [90m 3[39m GAAC… sampl… SeuratPro…         87           50 1               B            
## [90m 4[39m TGAC… sampl… SeuratPro…        127           56 0               A            
## [90m 5[39m AGTC… sampl… SeuratPro…        173           53 0               A            
## [90m 6[39m TCTG… sampl… SeuratPro…         70           48 0               A            
## [90m 7[39m TGGT… sampl… SeuratPro…         64           36 0               A            
## [90m 8[39m GCAG… sampl… SeuratPro…         72           45 0               A            
## [90m 9[39m GATA… sampl… SeuratPro…         52           36 0               A            
## [90m10[39m AATG… sampl… SeuratPro…        100           41 0               A            
## [90m# ℹ 70 more rows[39m
## [90m# ℹ 11 more variables: groups <chr>, RNA_snn_res.1 <fct>, file <chr>,[39m
## [90m#   ident <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>,[39m
## [90m#   tSNE_1 <dbl>, tSNE_2 <dbl>[39m

Preliminary plots

Set colours and theme for plots.

# Use colourblind-friendly colours
friendly_cols <- dittoSeq::dittoColors()

# Set theme
custom_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))
            )
    )

## Warning: [1m[22mThe `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## [36mℹ[39m Please use the `linewidth` argument instead.
## [90mThis warning is displayed once every 8 hours.[39m
## [90mCall `lifecycle::last_lifecycle_warnings()` to see where this warning was[39m
## [90mgenerated.[39m

We can treat pbmc_small_polished as a tibble for plotting.

Here we plot number of features per cell.

pbmc_small_polished |>
    ggplot(aes(nFeature_RNA, fill=groups)) +
    geom_histogram() +
    custom_theme

## [1m[22m`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Here we plot total features per cell.

pbmc_small_polished |>
    ggplot(aes(groups, nCount_RNA, fill=groups)) +
    geom_boxplot(outlier.shape=NA) +
    geom_jitter(width=0.1) +
    custom_theme

Here we plot abundance of two features for each group.

pbmc_small_polished |>
    join_features(features=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() +
    custom_theme

## tidySingleCellExperiment says: join_features produces duplicate cell names to accomadate the long data format. For this reason, a data frame is returned for independent data analysis. Assay feature abundance is appended as .abundance_counts and .abundance_logcounts.

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.

## Warning in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth =
## TRUE, : did not converge--results might be invalid!; try increasing work or
## maxit

pbmc_small_pca

## [90m# A SingleCellExperiment-tibble abstraction: 80 × 18[39m
## [90m# [90mFeatures=230 | Cells=80 | Assays=counts, logcounts[0m[39m
##    .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
##    [3m[90m<chr>[39m[23m [3m[90m<fct>[39m[23m           [3m[90m<dbl>[39m[23m        [3m[90m<int>[39m[23m [3m[90m<fct>[39m[23m           [3m[90m<fct>[39m[23m         [3m[90m<chr>[39m[23m 
## [90m 1[39m ATGC… SeuratPro…         70           47 0               A             g2    
## [90m 2[39m CATG… SeuratPro…         85           52 0               A             g1    
## [90m 3[39m GAAC… SeuratPro…         87           50 1               B             g2    
## [90m 4[39m TGAC… SeuratPro…        127           56 0               A             g2    
## [90m 5[39m AGTC… SeuratPro…        173           53 0               A             g2    
## [90m 6[39m TCTG… SeuratPro…         70           48 0               A             g1    
## [90m 7[39m TGGT… SeuratPro…         64           36 0               A             g1    
## [90m 8[39m GCAG… SeuratPro…         72           45 0               A             g1    
## [90m 9[39m GATA… SeuratPro…         52           36 0               A             g1    
## [90m10[39m AATG… SeuratPro…        100           41 0               A             g1    
## [90m# ℹ 70 more rows[39m
## [90m# ℹ 11 more variables: RNA_snn_res.1 <fct>, file <chr>, sample <chr>,[39m
## [90m#   ident <fct>, PC1 <dbl>, PC2 <dbl>, PC3 <dbl>, PC4 <dbl>, PC5 <dbl>,[39m
## [90m#   tSNE_1 <dbl>, tSNE_2 <dbl>[39m

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)) +
    custom_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 SingleCellExperiment object
colLabels(pbmc_small_cluster) <-
    pbmc_small_pca |>
    buildSNNGraph(use.dimred="PCA") |>
    igraph::cluster_walktrap() %$%
    membership |>
    as.factor()

# Reorder columns
pbmc_small_cluster |> select(label, everything())

## [90m# A SingleCellExperiment-tibble abstraction: 80 × 19[39m
## [90m# [90mFeatures=230 | Cells=80 | Assays=counts, logcounts[0m[39m
##    .cell  label orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents
##    [3m[90m<chr>[39m[23m  [3m[90m<fct>[39m[23m [3m[90m<fct>[39m[23m           [3m[90m<dbl>[39m[23m        [3m[90m<int>[39m[23m [3m[90m<fct>[39m[23m           [3m[90m<fct>[39m[23m        
## [90m 1[39m ATGCC… 2     SeuratPro…         70           47 0               A            
## [90m 2[39m CATGG… 2     SeuratPro…         85           52 0               A            
## [90m 3[39m GAACC… 2     SeuratPro…         87           50 1               B            
## [90m 4[39m TGACT… 1     SeuratPro…        127           56 0               A            
## [90m 5[39m AGTCA… 2     SeuratPro…        173           53 0               A            
## [90m 6[39m TCTGA… 2     SeuratPro…         70           48 0               A            
## [90m 7[39m TGGTA… 1     SeuratPro…         64           36 0               A            
## [90m 8[39m GCAGC… 2     SeuratPro…         72           45 0               A            
## [90m 9[39m GATAT… 2     SeuratPro…         52           36 0               A            
## [90m10[39m AATGT… 2     SeuratPro…        100           41 0               A            
## [90m# ℹ 70 more rows[39m
## [90m# ℹ 12 more variables: groups <chr>, RNA_snn_res.1 <fct>, file <chr>,[39m
## [90m#   sample <chr>, ident <fct>, PC1 <dbl>, PC2 <dbl>, PC3 <dbl>, PC4 <dbl>,[39m
## [90m#   PC5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>[39m

And interrogate the output as if it was a regular tibble.

# Count number of cells for each cluster per group
pbmc_small_cluster |>
    count(groups, label)

## tidySingleCellExperiment says: A data frame is returned for independent data analysis.

## [90m# A tibble: 8 × 3[39m
##   groups label     n
##   [3m[90m<chr>[39m[23m  [3m[90m<fct>[39m[23m [3m[90m<int>[39m[23m
## [90m1[39m g1     1        12
## [90m2[39m g1     2        14
## [90m3[39m g1     3        14
## [90m4[39m g1     4         4
## [90m5[39m g2     1        10
## [90m6[39m g2     2        11
## [90m7[39m g2     3        10
## [90m8[39m 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_features(features=marker_genes) |>
    group_by(label) |>
    heatmap(.feature, .cell, .abundance_counts, .scale="column")

## tidySingleCellExperiment says: join_features produces duplicate cell names to accomadate the long data format. For this reason, a data frame is returned for independent data analysis. Assay feature abundance is appended as .abundance_counts and .abundance_logcounts.

## tidyHeatmap says: (once per session) from release 1.7.0 the scaling is set to "none" by default. Please use scale = "row", "column" or "both" to apply scaling

## Warning: [1m[22mThe `.scale` argument of `heatmap()` is deprecated as of tidyHeatmap 1.7.0.
## [36mℹ[39m Please use scale (without dot prefix) instead: heatmap(scale = ...)
## [90mThis warning is displayed once every 8 hours.[39m
## [90mCall `lifecycle::last_lifecycle_warnings()` to see where this warning was[39m
## [90mgenerated.[39m

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()

# Infer cell identities
cell_type_df <-

    assays(pbmc_small_UMAP)$logcounts |>
    Matrix::Matrix(sparse = TRUE) |>
    SingleR::SingleR(
        ref = blueprint,
        labels = blueprint$label.main,
        method = "single"
    ) |>
    as.data.frame() |>
    as_tibble(rownames="cell") |>
    select(cell, first.labels)

# Join UMAP and cell type info
data(cell_type_df)
pbmc_small_cell_type <-
    pbmc_small_UMAP |>
    left_join(cell_type_df, by="cell")

## Warning in is_sample_feature_deprecated_used(x, .cols):
## tidySingleCellExperiment says: from version 1.3.1, the special columns
## including cell id (colnames(se)) has changed to ".cell". This dataset is
## returned with the old-style vocabulary (cell), however, we suggest to update
## your workflow to reflect the new vocabulary (.cell).

# Reorder columns
pbmc_small_cell_type |>
    select(cell, first.labels, everything())

## Warning in is_sample_feature_deprecated_used(.data, .cols):
## tidySingleCellExperiment says: from version 1.3.1, the special columns
## including cell id (colnames(se)) has changed to ".cell". This dataset is
## returned with the old-style vocabulary (cell), however, we suggest to update
## your workflow to reflect the new vocabulary (.cell).

## [90m# A SingleCellExperiment-tibble abstraction: 80 × 23[39m
## [90m# [90mFeatures=230 | Cells=80 | Assays=counts, logcounts[0m[39m
##    cell          first.labels orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8
##    [3m[90m<chr>[39m[23m         [3m[90m<chr>[39m[23m        [3m[90m<fct>[39m[23m           [3m[90m<dbl>[39m[23m        [3m[90m<int>[39m[23m [3m[90m<fct>[39m[23m          
## [90m 1[39m ATGCCAGAACGA… CD4+ T-cells SeuratPro…         70           47 0              
## [90m 2[39m CATGGCCTGTGC… CD8+ T-cells SeuratPro…         85           52 0              
## [90m 3[39m GAACCTGATGAA… CD8+ T-cells SeuratPro…         87           50 1              
## [90m 4[39m TGACTGGATTCT… CD4+ T-cells SeuratPro…        127           56 0              
## [90m 5[39m AGTCAGACTGCA… CD4+ T-cells SeuratPro…        173           53 0              
## [90m 6[39m TCTGATACACGT… CD4+ T-cells SeuratPro…         70           48 0              
## [90m 7[39m TGGTATCTAAAC… CD4+ T-cells SeuratPro…         64           36 0              
## [90m 8[39m GCAGCTCTGTTT… CD4+ T-cells SeuratPro…         72           45 0              
## [90m 9[39m GATATAACACGC… CD4+ T-cells SeuratPro…         52           36 0              
## [90m10[39m AATGTTGACAGT… CD4+ T-cells SeuratPro…        100           41 0              
## [90m# ℹ 70 more rows[39m
## [90m# ℹ 17 more variables: letter.idents <fct>, groups <chr>, RNA_snn_res.1 <fct>,[39m
## [90m#   file <chr>, sample <chr>, ident <fct>, label <fct>, PC1 <dbl>, PC2 <dbl>,[39m
## [90m#   PC3 <dbl>, PC4 <dbl>, PC5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>, UMAP1 <dbl>,[39m
## [90m#   UMAP2 <dbl>, UMAP3 <dbl>[39m

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.

## [90m# A tibble: 11 × 3[39m
##    label first.labels     n
##    [3m[90m<fct>[39m[23m [3m[90m<chr>[39m[23m        [3m[90m<int>[39m[23m
## [90m 1[39m 1     CD4+ T-cells     2
## [90m 2[39m 1     CD8+ T-cells     8
## [90m 3[39m 1     NK cells        12
## [90m 4[39m 2     B-cells         10
## [90m 5[39m 2     CD4+ T-cells     6
## [90m 6[39m 2     CD8+ T-cells     2
## [90m 7[39m 2     Macrophages      1
## [90m 8[39m 2     Monocytes        6
## [90m 9[39m 3     Macrophages      1
## [90m10[39m 3     Monocytes       23
## [90m11[39m 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) +
    custom_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_features(features=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() +
    custom_theme

## Warning in is_sample_feature_deprecated_used(x, .cols):
## tidySingleCellExperiment says: from version 1.3.1, the special columns
## including cell id (colnames(se)) has changed to ".cell". This dataset is
## returned with the old-style vocabulary (cell), however, we suggest to update
## your workflow to reflect the new vocabulary (.cell).

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

## [90m# A tibble: 2 × 2[39m
##   cell_class data           
##   [3m[90m<chr>[39m[23m      [3m[90m<list>[39m[23m         
## [90m1[39m lymphoid   [90m<SnglCllE[,40]>[39m
## [90m2[39m myeloid    [90m<SnglCllE[,31]>[39m

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

## [90m# A tibble: 2 × 2[39m
##   cell_class data           
##   [3m[90m<chr>[39m[23m      [3m[90m<list>[39m[23m         
## [90m1[39m lymphoid   [90m<SnglCllE[,40]>[39m
## [90m2[39m myeloid    [90m<SnglCllE[,31]>[39m

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) +
    custom_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
    nest(data=-sample) |>
    mutate(interactions=map(data, ~ {

        # Produce variables. Yuck!
        cluster <- colData(.x)$integrated_clusters
        data <- data.frame(assays(.x) |> as.list() |> extract2(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.

data(pbmc_small_nested_interactions)
pbmc_small_nested_interactions

## [90m# A tibble: 100 × 9[39m
##    sample  ligand          receptor ligand.name receptor.name origin destination
##    [3m[90m<chr>[39m[23m   [3m[90m<chr>[39m[23m           [3m[90m<chr>[39m[23m    [3m[90m<chr>[39m[23m       [3m[90m<chr>[39m[23m         [3m[90m<chr>[39m[23m  [3m[90m<chr>[39m[23m      
## [90m 1[39m sample1 cluster 1.PTMA  cluster… PTMA        VIPR1         clust… cluster 2  
## [90m 2[39m sample1 cluster 1.B2M   cluster… B2M         KLRD1         clust… cluster 2  
## [90m 3[39m sample1 cluster 1.IL16  cluster… IL16        CD4           clust… cluster 2  
## [90m 4[39m sample1 cluster 1.HLA-B cluster… HLA-B       KLRD1         clust… cluster 2  
## [90m 5[39m sample1 cluster 1.CALM1 cluster… CALM1       VIPR1         clust… cluster 2  
## [90m 6[39m sample1 cluster 1.HLA-E cluster… HLA-E       KLRD1         clust… cluster 2  
## [90m 7[39m sample1 cluster 1.GNAS  cluster… GNAS        VIPR1         clust… cluster 2  
## [90m 8[39m sample1 cluster 1.B2M   cluster… B2M         HFE           clust… cluster 2  
## [90m 9[39m sample1 cluster 1.PTMA  cluster… PTMA        VIPR1         clust… cluster 3  
## [90m10[39m sample1 cluster 1.CALM1 cluster… CALM1       VIPR1         clust… cluster 3  
## [90m# ℹ 90 more rows[39m
## [90m# ℹ 2 more variables: interaction.type <chr>, LRscore <dbl>[39m

Aggregating cells

Sometimes, it is necessary to aggregate the gene-transcript abundance from a group of cells into a single value. For example, when comparing groups of cells across different samples with fixed-effect models.

In tidySingleCellExperiment, cell aggregation can be achieved using the aggregate_cells function.

pbmc_small |>
  aggregate_cells(groups, assays = "counts")

## class: SummarizedExperiment 
## dim: 230 2 
## metadata(0):
## assays(1): counts
## rownames(230): ACAP1 ACRBP ... ZNF330 ZNF76
## rowData names(5): vst.mean vst.variance vst.variance.expected
##   vst.variance.standardized vst.variable
## colnames(2): g1 g2
## colData names(4): .aggregated_cells groups orig.ident file

tidySingleCellExperiment - part of tidytranscriptomics

Introduction

Functions/utilities available

Installation

Data representation of `tidySingleCellExperiment`

Annotation polishing

Preliminary plots

Preprocess the dataset

Identify clusters

Reduce dimensions

Cell type prediction

Nested analyses

Aggregating cells

Links

License

Citation

Developers

Dev status

tidySingleCellExperiment - part of tidytranscriptomics

Introduction

Functions/utilities available

Installation

Data representation of tidySingleCellExperiment

Annotation polishing

Preliminary plots

Preprocess the dataset

Identify clusters

Reduce dimensions

Cell type prediction

Nested analyses

Aggregating cells

Links

License

Citation

Developers

Dev status

Data representation of `tidySingleCellExperiment`