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
## # A Seurat-tibble abstraction: 80 × 15
## # [90mFeatures=230 | Cells=80 | Active assay=RNA | Assays=RNA[0m
## .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 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 %>%
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
## # A Seurat-tibble abstraction: 80 × 17
## # [90mFeatures=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT[0m
## .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>, 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
## # [90mFeatures=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT[0m
## .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 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 %>%
count(groups, seurat_clusters)
## # A tibble: 6 × 3
## groups seurat_clusters n
## <chr> <fct> <int>
## 1 g1 0 23
## 2 g1 1 17
## 3 g1 2 4
## 4 g2 0 17
## 5 g2 1 13
## 6 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")
Session Info
sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Australia/Melbourne
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidyseurat_0.8.0 ttservice_0.4.0 Seurat_5.0.1 SeuratObject_5.0.1
## [5] sp_2.1-2 ggplot2_3.4.4 magrittr_2.0.3 purrr_1.0.2
## [9] tidyr_1.3.0 dplyr_1.1.4 knitr_1.45
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.15.0
## [3] jsonlite_1.8.8 spatstat.utils_3.0-4
## [5] farver_2.1.1 zlibbioc_1.48.0
## [7] vctrs_0.6.5 ROCR_1.0-11
## [9] DelayedMatrixStats_1.22.6 spatstat.explore_3.2-5
## [11] RCurl_1.98-1.13 S4Arrays_1.2.0
## [13] htmltools_0.5.7 SparseArray_1.2.2
## [15] sctransform_0.4.1 parallelly_1.36.0
## [17] KernSmooth_2.23-21 htmlwidgets_1.6.4
## [19] ica_1.0-3 plyr_1.8.9
## [21] plotly_4.10.3 zoo_1.8-12
## [23] commonmark_1.9.0 igraph_1.6.0
## [25] mime_0.12 lifecycle_1.0.4
## [27] pkgconfig_2.0.3 Matrix_1.6-4
## [29] R6_2.5.1 fastmap_1.1.1
## [31] GenomeInfoDbData_1.2.11 MatrixGenerics_1.14.0
## [33] fitdistrplus_1.1-11 future_1.33.0
## [35] shiny_1.8.0 digest_0.6.33
## [37] GGally_2.2.0 colorspace_2.1-0
## [39] patchwork_1.1.3 S4Vectors_0.40.2
## [41] tensor_1.5 RSpectra_0.16-1
## [43] irlba_2.3.5.1 GenomicRanges_1.54.1
## [45] labeling_0.4.3 progressr_0.14.0
## [47] fansi_1.0.6 spatstat.sparse_3.0-3
## [49] httr_1.4.7 polyclip_1.10-6
## [51] abind_1.4-5 compiler_4.3.1
## [53] withr_2.5.2 ggstats_0.5.1
## [55] fastDummies_1.7.3 highr_0.10
## [57] MASS_7.3-60 DelayedArray_0.28.0
## [59] tools_4.3.1 lmtest_0.9-40
## [61] httpuv_1.6.13 future.apply_1.11.0
## [63] goftest_1.2-3 glmGamPoi_1.12.2
## [65] glue_1.6.2 nlme_3.1-162
## [67] promises_1.2.1 grid_4.3.1
## [69] Rtsne_0.17 cluster_2.1.4
## [71] reshape2_1.4.4 generics_0.1.3
## [73] gtable_0.3.4 spatstat.data_3.0-3
## [75] data.table_1.14.10 XVector_0.42.0
## [77] utf8_1.2.4 BiocGenerics_0.48.1
## [79] spatstat.geom_3.2-7 RcppAnnoy_0.0.21
## [81] ggrepel_0.9.4 RANN_2.6.1
## [83] pillar_1.9.0 markdown_1.12
## [85] stringr_1.5.1 spam_2.10-0
## [87] RcppHNSW_0.5.0 later_1.3.2
## [89] splines_4.3.1 lattice_0.21-8
## [91] survival_3.5-5 deldir_2.0-2
## [93] tidyselect_1.2.0 miniUI_0.1.1.1
## [95] pbapply_1.7-2 gridExtra_2.3
## [97] IRanges_2.36.0 SummarizedExperiment_1.32.0
## [99] scattermore_1.2 stats4_4.3.1
## [101] xfun_0.41 Biobase_2.62.0
## [103] matrixStats_1.2.0 stringi_1.8.3
## [105] lazyeval_0.2.2 yaml_2.3.8
## [107] evaluate_0.23 codetools_0.2-19
## [109] tibble_3.2.1 cli_3.6.2
## [111] uwot_0.1.16 xtable_1.8-4
## [113] reticulate_1.34.0 munsell_0.5.0
## [115] GenomeInfoDb_1.38.1 Rcpp_1.0.11
## [117] globals_0.16.2 spatstat.random_3.2-2
## [119] png_0.1-8 parallel_4.3.1
## [121] ellipsis_0.3.2 dotCall64_1.1-1
## [123] sparseMatrixStats_1.12.2 bitops_1.0-7
## [125] listenv_0.9.0 viridisLite_0.4.2
## [127] scales_1.3.0 ggridges_0.5.5
## [129] crayon_1.5.2 leiden_0.4.3.1
## [131] rlang_1.1.2 cowplot_1.1.2