Lifecycle:maturing

Brings Seurat to the tidyverse!

website: stemangiola.github.io/tidyseurat/

Please also have a look at

visual cue

Introduction

tidyseurat provides a bridge between the Seurat single-cell package (Butler et al. 2018; Stuart et al. 2019) and the tidyverse (Wickham et al. 2019). It creates an invisible layer that enables viewing the Seurat object as a tidyverse tibble, and provides Seurat-compatible dplyr, tidyr, ggplot and plotly functions.

Functions/utilities available

Seurat-compatible Functions Description
all After all tidyseurat is a Seurat object, just better
tidyverse Packages Description
dplyr All dplyr APIs like for any tibble
tidyr All tidyr APIs like for any tibble
ggplot2 ggplot like for any tibble
plotly plot_ly like for any tibble
Utilities Description
tidy Add tidyseurat invisible layer over a Seurat object
as_tibble Convert cell-wise information to a tbl_df
join_features Add feature-wise information, returns a tbl_df

Installation

From CRAN

install.packages("tidyseurat")

From Github (development)

devtools::install_github("stemangiola/tidyseurat")

Create tidyseurat, the best of both worlds!

This is a seurat object but it is evaluated as tibble. So it is fully compatible both with Seurat and tidyverse APIs.

pbmc_small = SeuratObject::pbmc_small

It looks like a tibble

pbmc_small
## # A Seurat-tibble abstraction: 80 × 15
## # Features=230 | Cells=80 | Active assay=RNA | Assays=RNA
##    .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 8 more variables: RNA_snn_res.1 <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 Seurat object after all

pbmc_small@assays
## $RNA
## Assay data with 230 features for 80 cells
## Top 10 variable features:
##  PPBP, IGLL5, VDAC3, CD1C, AKR1C3, PF4, MYL9, GNLY, TREML1, CA2

Preliminary plots

Set colours and theme for plots.

# Use colourblind-friendly colours
friendly_cols <- c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499", "#44AA99", "#999933", "#882255", "#661100", "#6699CC")

# 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 effectively as a normal tibble for plotting.

Here we plot number of features per cell.

pbmc_small %>%
  tidyseurat::ggplot(aes(nFeature_RNA, fill = groups)) +
  geom_histogram() +
  my_theme

Here we plot total features per cell.

pbmc_small %>%
  tidyseurat::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 features for each group.

pbmc_small %>%
  join_features(features = c("HLA-DRA", "LYZ")) %>%
  ggplot(aes(groups, .abundance_RNA + 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

Preprocess the dataset

Also you can treat the object as Seurat object and proceed with data processing.

pbmc_small_pca <-
  pbmc_small %>%
  SCTransform(verbose = FALSE) %>%
  FindVariableFeatures(verbose = FALSE) %>%
  RunPCA(verbose = FALSE)

pbmc_small_pca
## # A Seurat-tibble abstraction: 80 × 17
## # Features=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT
##    .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>,
## #   nCount_SCT <dbl>, nFeature_SCT <int>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>,
## #   PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>

If a tool is not included in the tidyseurat collection, we can use as_tibble to permanently convert tidyseurat into tibble.

pbmc_small_pca %>%
  as_tibble() %>%
  select(contains("PC"), everything()) %>%
  GGally::ggpairs(columns = 1:5, ggplot2::aes(colour = groups)) +
  my_theme

Identify clusters

We proceed with cluster identification with Seurat.

pbmc_small_cluster <-
  pbmc_small_pca %>%
  FindNeighbors(verbose = FALSE) %>%
  FindClusters(method = "igraph", verbose = FALSE)

pbmc_small_cluster
## # A Seurat-tibble abstraction: 80 × 19
## # Features=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT
##    .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 12 more variables: RNA_snn_res.1 <fct>,
## #   nCount_SCT <dbl>, nFeature_SCT <int>, SCT_snn_res.0.8 <fct>,
## #   seurat_clusters <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>,
## #   PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>

Now we can interrogate the object as if it was a regular tibble data frame.

pbmc_small_cluster %>%
  tidyseurat::count(groups, seurat_clusters)
## # A tibble: 8 × 3
##   groups seurat_clusters     n
##   <chr>  <fct>           <int>
## 1 g1     0                  17
## 2 g1     1                  14
## 3 g1     2                   9
## 4 g1     3                   4
## 5 g2     0                  13
## 6 g2     1                  12
## 7 g2     2                   6
## 8 g2     3                   5

We can identify cluster markers using Seurat.

# Identify top 10 markers per cluster
markers <-
  pbmc_small_cluster %>%
  FindAllMarkers(only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25) %>%
  group_by(cluster) %>%
  top_n(10, avg_log2FC)

# Plot heatmap
pbmc_small_cluster %>%
  DoHeatmap(
    features = markers$gene,
    group.colors = friendly_cols
  )

Reduce dimensions

We can calculate the first 3 UMAP dimensions using the Seurat framework.

pbmc_small_UMAP <-
  pbmc_small_cluster %>%
  RunUMAP(reduction = "pca", dims = 1:15, n.components = 3L)

And we can plot them using 3D plot using plotly.

pbmc_small_UMAP %>%
  plot_ly(
    x = ~`UMAP_1`,
    y = ~`UMAP_2`,
    z = ~`UMAP_3`,
    color = ~seurat_clusters,
    colors = friendly_cols[1:4]
  )

screenshot plotly

Cell type prediction

We can infer cell type identities using SingleR (Aran et al. 2019) and manipulate the output using tidyverse.

# Get cell type reference data
blueprint <- celldex::BlueprintEncodeData()

# Infer cell identities
cell_type_df <-
  GetAssayData(pbmc_small_UMAP, slot = 'counts', assay = "SCT") %>%
  log1p() %>%
  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
pbmc_small_cell_type <-
  pbmc_small_UMAP %>%
  left_join(cell_type_df, by = "cell")

# Reorder columns
pbmc_small_cell_type %>%
  tidyseurat::select(cell, first.labels, everything())

We can easily summarise the results. For example, we can see how cell type classification overlaps with cluster classification.

pbmc_small_cell_type %>%
  count(seurat_clusters, first.labels)

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(seurat_clusters, first.labels),
    names_to = "classifier", values_to = "label"
  ) %>%

  # UMAP plots for cell type and cluster
  ggplot(aes(UMAP_1, UMAP_2, color = label)) +
  geom_point() +
  facet_wrap(~classifier) +
  my_theme

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(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() +
  my_theme

Nested analyses

A powerful tool we can use with tidyseurat is nest. We can easily perform independent analyses on subsets of the dataset. First we classify cell types in lymphoid and myeloid; then, nest based on the new classification

pbmc_small_nested <-
  pbmc_small_cell_type %>%
  filter(first.labels != "Erythrocytes") %>%
  mutate(cell_class = if_else(`first.labels` %in% c("Macrophages", "Monocytes"), "myeloid", "lymphoid")) %>%
  nest(data = -cell_class)

pbmc_small_nested

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 %>%
      FindVariableFeatures(verbose = FALSE) %>%
      RunPCA(npcs = 10, verbose = FALSE) %>%
      FindNeighbors(verbose = FALSE) %>%
      FindClusters(method = "igraph", verbose = FALSE) %>%
      RunUMAP(reduction = "pca", dims = 1:10, n.components = 3L, verbose = FALSE)
  ))

pbmc_small_nested_reanalysed

Now we can unnest and plot the new classification.

pbmc_small_nested_reanalysed %>%

  # Convert to tibble otherwise Seurat drops reduced dimensions when unifying data sets.
  mutate(data = map(data, ~ .x %>% as_tibble())) %>%
  unnest(data) %>%

  # Define unique clusters
  unite("cluster", c(cell_class, seurat_clusters), remove = FALSE) %>%

  # Plotting
  ggplot(aes(UMAP_1, UMAP_2, color = cluster)) +
  geom_point() +
  facet_wrap(~cell_class) +
  my_theme

Session Info

## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] tidyseurat_0.5.3   ttservice_0.1.2    sp_1.4-7           SeuratObject_4.1.0
##  [5] Seurat_4.1.1       ggplot2_3.3.6      magrittr_2.0.3     purrr_0.3.4       
##  [9] tidyr_1.2.0        dplyr_1.0.9        knitr_1.39        
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.16            colorspace_2.0-3      deldir_1.0-6         
##   [4] ellipsis_0.3.2        ggridges_0.5.3        rprojroot_2.0.3      
##   [7] fs_1.5.2              spatstat.data_2.2-0   farver_2.1.0         
##  [10] leiden_0.4.2          listenv_0.8.0         ggrepel_0.9.1        
##  [13] fansi_1.0.3           codetools_0.2-18      splines_4.2.0        
##  [16] cachem_1.0.6          polyclip_1.10-0       jsonlite_1.8.0       
##  [19] ica_1.0-2             cluster_2.1.3         png_0.1-7            
##  [22] rgeos_0.5-9           uwot_0.1.11           shiny_1.7.1          
##  [25] sctransform_0.3.3     spatstat.sparse_2.1-1 compiler_4.2.0       
##  [28] httr_1.4.3            Matrix_1.4-1          fastmap_1.1.0        
##  [31] lazyeval_0.2.2        cli_3.3.0             later_1.3.0          
##  [34] htmltools_0.5.2       tools_4.2.0           igraph_1.3.1         
##  [37] gtable_0.3.0          glue_1.6.2            RANN_2.6.1           
##  [40] reshape2_1.4.4        Rcpp_1.0.8.3          scattermore_0.8      
##  [43] jquerylib_0.1.4       pkgdown_2.0.3         vctrs_0.4.1          
##  [46] nlme_3.1-157          progressr_0.10.0      lmtest_0.9-40        
##  [49] spatstat.random_2.2-0 xfun_0.31             stringr_1.4.0        
##  [52] globals_0.15.0        mime_0.12             miniUI_0.1.1.1       
##  [55] lifecycle_1.0.1       irlba_2.3.5           goftest_1.2-3        
##  [58] future_1.25.0         MASS_7.3-56           zoo_1.8-10           
##  [61] scales_1.2.0          spatstat.core_2.4-4   ragg_1.2.2           
##  [64] promises_1.2.0.1      spatstat.utils_2.3-1  parallel_4.2.0       
##  [67] RColorBrewer_1.1-3    yaml_2.3.5            memoise_2.0.1        
##  [70] reticulate_1.25       pbapply_1.5-0         gridExtra_2.3        
##  [73] sass_0.4.1            rpart_4.1.16          reshape_0.8.9        
##  [76] stringi_1.7.6         highr_0.9             desc_1.4.1           
##  [79] rlang_1.0.2           pkgconfig_2.0.3       systemfonts_1.0.4    
##  [82] matrixStats_0.62.0    evaluate_0.15         lattice_0.20-45      
##  [85] tensor_1.5            ROCR_1.0-11           labeling_0.4.2       
##  [88] patchwork_1.1.1       htmlwidgets_1.5.4     cowplot_1.1.1        
##  [91] tidyselect_1.1.2      GGally_2.1.2          parallelly_1.31.1    
##  [94] RcppAnnoy_0.0.19      plyr_1.8.7            R6_2.5.1             
##  [97] generics_0.1.2        mgcv_1.8-40           pillar_1.7.0         
## [100] withr_2.5.0           fitdistrplus_1.1-8    abind_1.4-5          
## [103] survival_3.3-1        tibble_3.1.7          future.apply_1.9.0   
## [106] crayon_1.5.1          KernSmooth_2.23-20    utf8_1.2.2           
## [109] spatstat.geom_2.4-0   plotly_4.10.0         rmarkdown_2.14       
## [112] grid_4.2.0            data.table_1.14.2     digest_0.6.29        
## [115] xtable_1.8-4          httpuv_1.6.5          textshaping_0.3.6    
## [118] munsell_0.5.0         viridisLite_0.4.0     bslib_0.3.1

References

Aran, Dvir, Agnieszka P Looney, Leqian Liu, Esther Wu, Valerie Fong, Austin Hsu, Suzanna Chak, et al. 2019. “Reference-Based Analysis of Lung Single-Cell Sequencing Reveals a Transitional Profibrotic Macrophage.” Nature Immunology 20 (2): 163–72.

Butler, Andrew, Paul Hoffman, Peter Smibert, Efthymia Papalexi, and Rahul Satija. 2018. “Integrating Single-Cell Transcriptomic Data Across Different Conditions, Technologies, and Species.” Nature Biotechnology 36 (5): 411–20.

Stuart, Tim, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M Mauck III, Yuhan Hao, Marlon Stoeckius, Peter Smibert, and Rahul Satija. 2019. “Comprehensive Integration of Single-Cell Data.” Cell 177 (7): 1888–1902.

Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4 (43): 1686.