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README.md
tidySingleCellExperiment - part of tidytranscriptomics ================ <!-- badges: start --> [![Lifecycle:maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing) [![R build status](https://github.com/stemangiola/tidySingleCellExperiment/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/stemangiola/tidySingleCellExperiment/actions) <!-- badges: end --> **Brings SingleCellExperiment to the tidyverse!** Website: [tidySingleCellExperiment](https://stemangiola.github.io/tidySingleCellExperiment/articles/introduction.html) Please also have a look at - [tidySummarizedExperiment](https://stemangiola.github.io/tidySummarizedExperiment/) for tidy manipulation of SummarizedExperiment objects) - [tidyseurat](https://stemangiola.github.io/tidyseurat/) for tidy manipulation of Seurat objects - [tidybulk](https://stemangiola.github.io/tidybulk/) for tidy bulk RNA-seq data analysis - [tidygate](https://github.com/stemangiola/tidygate) for adding custom gate information to your tibble - [tidyHeatmap](https://stemangiola.github.io/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 ``` r if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("tidySingleCellExperiment") ``` Load libraries used in this vignette. ``` r # 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. ``` r data(pbmc_small, package="tidySingleCellExperiment") ``` **It looks like a tibble** ``` r pbmc_small ``` ## # A SingleCellExperiment-tibble abstraction: 80 × 17 ## # Features=230 | Cells=80 | Assays=counts, logcounts ## .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 ## # ℹ 70 more rows ## # ℹ 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** ``` r 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. ``` r # 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 ``` r # 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`. ``` r 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. ``` r # 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()) ``` ## # A SingleCellExperiment-tibble abstraction: 80 × 18 ## # Features=230 | Cells=80 | Assays=counts, logcounts ## .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 ## # ℹ 70 more rows ## # ℹ 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. ``` r # 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: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0. ## ℹ Please use the `linewidth` argument instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. We can treat `pbmc_small_polished` as a tibble for plotting. Here we plot number of features per cell. ``` r pbmc_small_polished |> ggplot(aes(nFeature_RNA, fill=groups)) + geom_histogram() + custom_theme ``` ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ![](inst/extdata/readme_figures/plot1-1.png)<!-- --> Here we plot total features per cell. ``` r pbmc_small_polished |> ggplot(aes(groups, nCount_RNA, fill=groups)) + geom_boxplot(outlier.shape=NA) + geom_jitter(width=0.1) + custom_theme ``` ![](inst/extdata/readme_figures/plot2-1.png)<!-- --> Here we plot abundance of two features for each group. ``` r 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. ![](inst/extdata/readme_figures/unnamed-chunk-12-1.png)<!-- --> # 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* \[@mccarthy2017scater\]. ``` r # 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 ``` r pbmc_small_pca ``` ## # A SingleCellExperiment-tibble abstraction: 80 × 18 ## # Features=230 | Cells=80 | Assays=counts, logcounts ## .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 ## # ℹ 70 more rows ## # ℹ 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. ``` r # 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 ![](inst/extdata/readme_figures/pc_plot-1.png)<!-- --> # Identify clusters We can proceed with cluster identification with *scran*. ``` r 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() ``` ## Warning in (function (to_check, X, clust_centers, clust_info, dtype, nn, : ## detected tied distances to neighbors, see ?'BiocNeighbors-ties' ``` r # Reorder columns pbmc_small_cluster |> select(label, everything()) ``` ## # A SingleCellExperiment-tibble abstraction: 80 × 19 ## # Features=230 | Cells=80 | Assays=counts, logcounts ## .cell label orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents ## <chr> <fct> <fct> <dbl> <int> <fct> <fct> ## 1 ATGCC… 2 SeuratPro… 70 47 0 A ## 2 CATGG… 2 SeuratPro… 85 52 0 A ## 3 GAACC… 2 SeuratPro… 87 50 1 B ## 4 TGACT… 1 SeuratPro… 127 56 0 A ## 5 AGTCA… 2 SeuratPro… 173 53 0 A ## 6 TCTGA… 2 SeuratPro… 70 48 0 A ## 7 TGGTA… 1 SeuratPro… 64 36 0 A ## 8 GCAGC… 2 SeuratPro… 72 45 0 A ## 9 GATAT… 2 SeuratPro… 52 36 0 A ## 10 AATGT… 2 SeuratPro… 100 41 0 A ## # ℹ 70 more rows ## # ℹ 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. ``` r # 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. ## # A tibble: 8 × 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 \[@mangiola2020tidyheatmap\] ``` r # 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: The `.scale` argument of `heatmap()` is deprecated as of tidyHeatmap 1.7.0. ## ℹ Please use scale (without dot prefix) instead: heatmap(scale = ...) ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. ![](inst/extdata/readme_figures/unnamed-chunk-13-1.png)<!-- --> # Reduce dimensions We can calculate the first 3 UMAP dimensions using the SingleCellExperiment framework and *scater*. ``` r pbmc_small_UMAP <- pbmc_small_cluster |> runUMAP(ncomponents=3) ``` And we can plot the result in 3D using plotly. ``` r pbmc_small_UMAP |> plot_ly( x=~`UMAP1`, y=~`UMAP2`, z=~`UMAP3`, color=~label, colors=friendly_cols[1:4] ) ``` <figure> <img src="inst/extdata/plotly.png" alt="plotly screenshot" /> <figcaption aria-hidden="true">plotly screenshot</figcaption> </figure> # Cell type prediction We can infer cell type identities using *SingleR* \[@aran2019reference\] and manipulate the output using tidyverse. ``` r # 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) ``` ``` r # 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). ``` r # 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). ## # A SingleCellExperiment-tibble abstraction: 80 × 23 ## # Features=230 | Cells=80 | Assays=counts, logcounts ## cell first.labels orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 ## <chr> <chr> <fct> <dbl> <int> <fct> ## 1 ATGCCAGAACGA… CD4+ T-cells SeuratPro… 70 47 0 ## 2 CATGGCCTGTGC… CD8+ T-cells SeuratPro… 85 52 0 ## 3 GAACCTGATGAA… CD8+ T-cells SeuratPro… 87 50 1 ## 4 TGACTGGATTCT… CD4+ T-cells SeuratPro… 127 56 0 ## 5 AGTCAGACTGCA… CD4+ T-cells SeuratPro… 173 53 0 ## 6 TCTGATACACGT… CD4+ T-cells SeuratPro… 70 48 0 ## 7 TGGTATCTAAAC… CD4+ T-cells SeuratPro… 64 36 0 ## 8 GCAGCTCTGTTT… CD4+ T-cells SeuratPro… 72 45 0 ## 9 GATATAACACGC… CD4+ T-cells SeuratPro… 52 36 0 ## 10 AATGTTGACAGT… CD4+ T-cells SeuratPro… 100 41 0 ## # ℹ 70 more rows ## # ℹ 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. ``` r # 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: 11 × 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 6 ## 6 2 CD8+ T-cells 2 ## 7 2 Macrophages 1 ## 8 2 Monocytes 6 ## 9 3 Macrophages 1 ## 10 3 Monocytes 23 ## 11 4 Erythrocytes 9 We can easily reshape the data for building information-rich faceted plots. ``` r 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. ![](inst/extdata/readme_figures/unnamed-chunk-17-1.png)<!-- --> We can easily plot gene correlation per cell category, adding multi-layer annotations. ``` r 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). ![](inst/extdata/readme_figures/unnamed-chunk-18-1.png)<!-- --> # 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. ``` r 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) ``` ## Warning: There were 2 warnings in `mutate()`. ## The first warning was: ## ℹ In argument: `data = map(...)`. ## Caused by warning in `is_sample_feature_deprecated_used()`: ## ! 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). ## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning. ``` r pbmc_small_nested ``` ## # A tibble: 2 × 2 ## cell_class data ## <chr> <list> ## 1 lymphoid <SnglCllE[,40]> ## 2 myeloid <SnglCllE[,31]> 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. ``` r 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 × 2 ## cell_class data ## <chr> <list> ## 1 lymphoid <SnglCllE[,40]> ## 2 myeloid <SnglCllE[,31]> We can then unnest and plot the new classification. ``` r 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 ``` ![](inst/extdata/readme_figures/unnamed-chunk-21-1.png)<!-- --> 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. ``` r 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. ``` r data(pbmc_small_nested_interactions) pbmc_small_nested_interactions ``` ## # A tibble: 100 × 9 ## sample ligand receptor ligand.name receptor.name origin destination ## <chr> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 sample1 cluster 1.PTMA cluster… PTMA VIPR1 clust… cluster 2 ## 2 sample1 cluster 1.B2M cluster… B2M KLRD1 clust… cluster 2 ## 3 sample1 cluster 1.IL16 cluster… IL16 CD4 clust… cluster 2 ## 4 sample1 cluster 1.HLA-B cluster… HLA-B KLRD1 clust… cluster 2 ## 5 sample1 cluster 1.CALM1 cluster… CALM1 VIPR1 clust… cluster 2 ## 6 sample1 cluster 1.HLA-E cluster… HLA-E KLRD1 clust… cluster 2 ## 7 sample1 cluster 1.GNAS cluster… GNAS VIPR1 clust… cluster 2 ## 8 sample1 cluster 1.B2M cluster… B2M HFE clust… cluster 2 ## 9 sample1 cluster 1.PTMA cluster… PTMA VIPR1 clust… cluster 3 ## 10 sample1 cluster 1.CALM1 cluster… CALM1 VIPR1 clust… cluster 3 ## # ℹ 90 more rows ## # ℹ 2 more variables: interaction.type <chr>, LRscore <dbl> # 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. ``` r 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(0): ## colnames(2): g1 g2 ## colData names(4): .aggregated_cells groups orig.ident file