tidySCE - part of tidytranscriptomics ================ <!-- badges: start --> [](https://www.tidyverse.org/lifecycle/#maturing) [](https://github.com/stemangiola/tidySCE/actions) <!-- badges: end --> **Brings SingleCellExperiment to the tidyverse!** Website: [tidySCE](https://stemangiola.github.io/tidySCE/articles/introduction.html) Please also have a look at - [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 - [nanny](https://github.com/stemangiola/nanny) for tidy high-level data analysis and manipulation - [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 ============ tidySCE 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 `tidySCE` is a SingleCellExperiment object, just better | | tidyverse Packages | Description | |--------------------|-------------------------------------------------------------| | `dplyr` | All `dplyr` tibble functions (e.g. `tidySCE::select`) | | `tidyr` | All `tidyr` tibble functions (e.g. `tidySCE::pivot_longer`) | | `ggplot2` | `ggplot` (`tidySCE::ggplot`) | | `plotly` | `plot_ly` (`tidySCE::plot_ly`) | | Utilities | Description | |--------------------|------------------------------------------------------------------| | `tidy` | Add `tidySCE` 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("tidySCE") From GitHub devtools::install_github("stemangiola/tidySCE") 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(tidySCE) Create `tidySCE`, 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 <- tidySCE::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 %>% tidySCE::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 %>% tidySCE::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 ## tidySCE 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 tidySCE collection, we can use `as_tibble` to permanently convert `tidySCE` 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 %>% tidySCE::count(groups, label) ## tidySCE 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") ## tidySCE 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 %>% tidySCE::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) ## tidySCE 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 ## tidySCE 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 ## tidySCE says: A data frame is returned for independent data analysis. <!-- --> Nested analyses =============== A powerful tool we can use with tidySCE 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 <tidySCE> ## 2 myeloid <tidySCE> 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 <tidySCE> ## 2 myeloid <tidySCE> 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 tidySCE::nest(data=-sample) %>% tidySCE::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. tidySCE::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>