Brings Seurat to the Tidyverse (original) (raw)
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
From Github (development)
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.
It looks like a tibble
## [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>[39mBut it is a Seurat object after all
## $RNA
## Assay data with 230 features for 80 cells
## Top 10 variable features:
## PPBP, IGLL5, VDAC3, CD1C, AKR1C3, PF4, MYL9, GNLY, TREML1, CA2Preliminary 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.
Here we plot total features per cell.
Here we plot abundance of two features for each group.
Preprocess the dataset
Also you can treat the object as Seurat object and proceed with data processing.
## [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>[39mIf a tool is not included in the tidyseurat collection, we can use as_tibble to permanently convert tidyseurat into tibble.
Identify clusters
We proceed with cluster identification with Seurat.
## [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>[39mNow 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 6We 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_themeWe can easily plot gene correlation per cell category, adding multi-layer annotations.
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_nestedNow 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.
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_themeAggregating 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.