Brings Seurat to the tidyverse!

website: stemangiola.github.io/tidyseurat/

Please also have a look at

tidyseurat for tidy single-cell RNA sequencing analysis
tidySummarizedExperiment for tidy bulk RNA sequencing analysis
tidybulk for tidy bulk RNA-seq 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

tidyseurat provides a bridge between the Seurat single-cell package [@butler2018integrating; @stuart2019comprehensive] and the tidyverse [@wickham2019welcome]. 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`

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`
`aggregate_cells`	Aggregate cell gene-transcription abundance as pseudobulk tissue

Installation

From CRAN

install.packages("tidyseurat")

From Github (development)

devtools::install_github("stemangiola/tidyseurat")

library(dplyr)
library(tidyr)
library(purrr)
library(magrittr)
library(ggplot2)
library(Seurat)
library(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

## [90m# A Seurat-tibble abstraction: 80 × 15[39m
## [90m# [90mFeatures=230 | Cells=80 | Active assay=RNA | Assays=RNA[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 8 more variables: RNA_snn_res.1 <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>,[39m
## [90m#   PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>[39m

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

Here we plot total features per cell.

pbmc_small %>%
  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

## [90m# A Seurat-tibble abstraction: 80 × 17[39m
## [90m# [90mFeatures=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT[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>, nCount_SCT <dbl>,[39m
## [90m#   nFeature_SCT <int>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>,[39m
## [90m#   PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>[39m

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

## [90m# A Seurat-tibble abstraction: 80 × 19[39m
## [90m# [90mFeatures=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT[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 12 more variables: RNA_snn_res.1 <fct>, nCount_SCT <dbl>,[39m
## [90m#   nFeature_SCT <int>, SCT_snn_res.0.8 <fct>, seurat_clusters <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

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

pbmc_small_cluster %>%
  count(groups, seurat_clusters)

## [90m# A tibble: 6 × 3[39m
##   groups seurat_clusters     n
##   [3m[90m<chr>[39m[23m  [3m[90m<fct>[39m[23m           [3m[90m<int>[39m[23m
## [90m1[39m g1     0                  23
## [90m2[39m g1     1                  17
## [90m3[39m g1     2                   4
## [90m4[39m g2     0                  17
## [90m5[39m g2     1                  13
## [90m6[39m g2     2                   6

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]
  )

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 <-
  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 %>%
  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 Seurat 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

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 tidyseurat, cell aggregation can be achieved using the aggregate_cells function.

pbmc_small %>%
  aggregate_cells(groups, assays = "RNA")

tidyseurat - part of tidytranscriptomics