Brings Seurat to the tidyverse!
website: stemangiola.github.io/tidyseurat/
Please also have a look at
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.
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 |
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)
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
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
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
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: 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
)
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]
)
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 %>%
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
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
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")
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: America/Bahia
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidyseurat_0.7.4 ttservice_0.3.8 SeuratObject_4.9.9.9091
## [4] Seurat_4.3.0.1 ggplot2_3.4.3 magrittr_2.0.3
## [7] purrr_1.0.2 tidyr_1.3.0 dplyr_1.1.3
## [10] knitr_1.44
##
## loaded via a namespace (and not attached):
## [1] deldir_1.0-9 pbapply_1.7-2 gridExtra_2.3
## [4] rlang_1.1.1 RcppAnnoy_0.0.21 spatstat.geom_3.2-5
## [7] matrixStats_1.0.0 ggridges_0.5.4 compiler_4.3.1
## [10] png_0.1-8 vctrs_0.6.3 reshape2_1.4.4
## [13] stringr_1.5.0 pkgconfig_2.0.3 fastmap_1.1.1
## [16] ellipsis_0.3.2 labeling_0.4.3 utf8_1.2.3
## [19] promises_1.2.1 markdown_1.8 xfun_0.40
## [22] jsonlite_1.8.7 goftest_1.2-3 later_1.3.1
## [25] reshape_0.8.9 spatstat.utils_3.0-3 irlba_2.3.5.1
## [28] parallel_4.3.1 cluster_2.1.4 R6_2.5.1
## [31] ica_1.0-3 spatstat.data_3.0-1 stringi_1.7.12
## [34] RColorBrewer_1.1-3 GGally_2.1.2 reticulate_1.32.0
## [37] parallelly_1.36.0 lmtest_0.9-40 scattermore_1.2
## [40] Rcpp_1.0.11 tensor_1.5 future.apply_1.11.0
## [43] zoo_1.8-12 sctransform_0.3.5 httpuv_1.6.11
## [46] Matrix_1.6-1 splines_4.3.1 igraph_1.5.1
## [49] tidyselect_1.2.0 abind_1.4-5 rstudioapi_0.15.0
## [52] yaml_2.3.7 spatstat.random_3.1-5 codetools_0.2-19
## [55] miniUI_0.1.1.1 spatstat.explore_3.2-1 listenv_0.9.0
## [58] lattice_0.21-8 tibble_3.2.1 plyr_1.8.8
## [61] shiny_1.7.5 withr_2.5.0 ROCR_1.0-11
## [64] evaluate_0.21 Rtsne_0.16 future_1.33.0
## [67] survival_3.5-5 polyclip_1.10-4 fitdistrplus_1.1-11
## [70] pillar_1.9.0 KernSmooth_2.23-21 plotly_4.10.2
## [73] generics_0.1.3 sp_2.0-0 munsell_0.5.0
## [76] commonmark_1.9.0 scales_1.2.1 globals_0.16.2
## [79] xtable_1.8-4 glue_1.6.2 lazyeval_0.2.2
## [82] tools_4.3.1 data.table_1.14.8 RANN_2.6.1
## [85] leiden_0.4.3 dotCall64_1.0-2 cowplot_1.1.1
## [88] grid_4.3.1 colorspace_2.1-0 nlme_3.1-162
## [91] patchwork_1.1.3 cli_3.6.1 spatstat.sparse_3.0-2
## [94] spam_2.9-1 fansi_1.0.4 viridisLite_0.4.2
## [97] uwot_0.1.16 gtable_0.3.4 digest_0.6.33
## [100] progressr_0.14.0 ggrepel_0.9.3 farver_2.1.1
## [103] htmlwidgets_1.6.2 htmltools_0.5.6 lifecycle_1.0.3
## [106] httr_1.4.7 mime_0.12 MASS_7.3-60