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 |
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_transcripts |
Add transcript-wise information, returns a tbl_df |
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_tidy <- tidyseurat::pbmc_small %>% tidy()
It looks like a tibble
pbmc_small_tidy
## # A tibble abstraction: 80 x 16
## 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 9 more variables: RNA_snn_res.1 <fct>, file <chr>,
## # 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_tidy@assays
## $RNA
## Assay data with 230 features for 80 cells
## Top 10 variable features:
## PPBP, IGLL5, VDAC3, CD1C, AKR1C3, PF4, MYL9, GNLY, TREML1, CA2
We may have a column that contains the directory each run was taken from, such as the “file” column in pbmc_small_tidy.
pbmc_small_tidy$file[1:5]
## ATGCCAGAACGACT
## "../data/sample2/outs/filtered_feature_bc_matrix/"
## CATGGCCTGTGCAT
## "../data/sample1/outs/filtered_feature_bc_matrix/"
## GAACCTGATGAACC
## "../data/sample2/outs/filtered_feature_bc_matrix/"
## TGACTGGATTCTCA
## "../data/sample2/outs/filtered_feature_bc_matrix/"
## AGTCAGACTGCACA
## "../data/sample2/outs/filtered_feature_bc_matrix/"
We may want to extract the run/sample name out of it into a separate column. Tidyverse extract can be used to convert a character column into multiple columns using regular expression groups.
# Create sample column
pbmc_small_polished <-
pbmc_small_tidy %>%
extract(file, "sample", "../data/([a-z0-9]+)/outs.+", remove = FALSE)
# Reorder to have sample column up front
pbmc_small_polished %>%
select(sample, everything())
## # A tibble abstraction: 80 x 17
## cell sample orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents
## <chr> <chr> <fct> <dbl> <int> <fct> <fct>
## 1 ATGC… sampl… SeuratPro… 70 47 0 A
## 2 CATG… sampl… SeuratPro… 85 52 0 A
## 3 GAAC… sampl… SeuratPro… 87 50 1 B
## 4 TGAC… sampl… SeuratPro… 127 56 0 A
## 5 AGTC… sampl… SeuratPro… 173 53 0 A
## 6 TCTG… sampl… SeuratPro… 70 48 0 A
## 7 TGGT… sampl… SeuratPro… 64 36 0 A
## 8 GCAG… sampl… SeuratPro… 72 45 0 A
## 9 GATA… sampl… SeuratPro… 52 36 0 A
## 10 AATG… sampl… SeuratPro… 100 41 0 A
## # … with 70 more rows, and 10 more variables: groups <chr>,
## # RNA_snn_res.1 <fct>, file <chr>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>,
## # PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
Set colours and theme for plots.
# Use colourblind-friendly colours
if (requireNamespace("dittoSeq", quietly = TRUE)) {
friendly_cols <- dittoSeq::dittoColors()
} else {
friendly_cols <- c("red", "blue", "green", "purple")
}
# 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_polished effectively as a normal tibble for plotting.
Here we plot number of transcripts per cell.
pbmc_small_polished %>%
tidyseurat::ggplot(aes(nFeature_RNA, fill = groups)) +
geom_histogram() +
my_theme
Here we plot total transcripts per cell.
pbmc_small_polished %>%
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 transcripts for each group.
pbmc_small_polished %>%
join_transcripts(transcripts = 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_polished %>%
SCTransform(verbose = FALSE) %>%
FindVariableFeatures(verbose = FALSE) %>%
RunPCA(verbose = FALSE)
pbmc_small_pca
## # A tibble abstraction: 80 x 19
## 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>, file <chr>,
## # sample <chr>, 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 tibble abstraction: 80 x 21
## 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 14 more variables: RNA_snn_res.1 <fct>, file <chr>,
## # sample <chr>, 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 x 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_logFC)
# Plot heatmap
pbmc_small_cluster %>%
DoHeatmap(
features = markers$gene,
group.colors = friendly_cols
)
We can calculate the first 3 UMAP dimensions using the Seurat framework.
pbmc_small_UMAP <-
pbmc_small_cluster %>%
RunUMAP(reduction = "pca", dims = 1:15, n.components = 3L, )
And we can plot them using 3D plot using plotly.
pbmc_small_UMAP %>%
plot_ly(
x = ~`UMAP_1`,
y = ~`UMAP_2`,
z = ~`UMAP_3`,
color = ~seurat_clusters,
colors = friendly_cols[1:4]
)
We can infer cell type identities using SingleR [@aran2019reference] and manipulate the output using tidyverse.
# Get cell type reference data
blueprint <- celldex::BlueprintEncodeData()
# Infer cell identities
cell_type_df <-
pbmc_small_UMAP@assays[["SCT"]]@counts %>%
log1p() %>%
Matrix::Matrix(sparse = TRUE) %>%
SingleR::SingleR(
ref = blueprint,
labels = blueprint$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())
## # A tibble abstraction: 80 x 25
## cell first.labels orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8
## <chr> <chr> <fct> <dbl> <int> <fct>
## 1 ATGC… CD4+ T-cells SeuratPro… 70 47 0
## 2 CATG… CD8+ T-cells SeuratPro… 85 52 0
## 3 GAAC… CD8+ T-cells SeuratPro… 87 50 1
## 4 TGAC… CD4+ T-cells SeuratPro… 127 56 0
## 5 AGTC… CD4+ T-cells SeuratPro… 173 53 0
## 6 TCTG… CD4+ T-cells SeuratPro… 70 48 0
## 7 TGGT… CD4+ T-cells SeuratPro… 64 36 0
## 8 GCAG… CD4+ T-cells SeuratPro… 72 45 0
## 9 GATA… CD4+ T-cells SeuratPro… 52 36 0
## 10 AATG… CD4+ T-cells SeuratPro… 100 41 0
## # … with 70 more rows, and 19 more variables: letter.idents <fct>,
## # groups <chr>, RNA_snn_res.1 <fct>, file <chr>, sample <chr>,
## # 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>, UMAP_1 <dbl>, UMAP_2 <dbl>,
## # UMAP_3 <dbl>
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)
## # A tibble: 9 x 3
## seurat_clusters first.labels n
## <fct> <chr> <int>
## 1 0 CD4+ T-cells 8
## 2 0 CD8+ T-cells 10
## 3 0 NK cells 12
## 4 1 Macrophages 1
## 5 1 Monocytes 25
## 6 2 B-cells 10
## 7 2 Macrophages 1
## 8 2 Monocytes 4
## 9 3 Erythrocytes 9
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_transcripts(transcripts = 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
## # A tibble: 2 x 2
## cell_class data
## <chr> <list>
## 1 lymphoid <tidysert>
## 2 myeloid <tidysert>
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
## # A tibble: 2 x 2
## cell_class data
## <chr> <list>
## 1 lymphoid <tidysert>
## 2 myeloid <tidysert>
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
We can perform a large number of functional analyses on data subsets. For example, we can identify intra-sample cell-cell interactions using SingleCellSignalR [@cabello2020singlecellsignalr], and then compare whether interactions are stronger or weaker across conditions. The code below demonstrates how this analysis could be performed. It won't work with this small example dataset as we have just two samples (one for each condition). But some example output is shown below and you can imagine how you can use tidyverse on the output to perform t-tests and visualisation.
library(SingleCellSignalR)
pbmc_small_nested_interactions <-
pbmc_small_nested_reanalysed %>%
# Unnest based on cell category
unnest(data) %>%
# Create unambiguous clusters
mutate(integrated_clusters = first.labels %>% as.factor() %>% as.integer()) %>%
# Nest based on sample
tidyseurat::nest(data = -sample) %>%
tidyseurat::mutate(interactions = map(data, ~ {
# Produce variables. Yuck!
cluster <- .x@meta.data$integrated_clusters
data <- data.frame(.x[["SCT"]]@data)
# Ligand/Receptor analysis using SingleCellSignalR
data %>%
cell_signaling(genes = rownames(data), cluster = cluster) %>%
inter_network(data = data, signal = ., genes = rownames(data), cluster = cluster) %$%
`individual-networks` %>%
map_dfr(~ bind_rows(as_tibble(.x)))
}))
pbmc_small_nested_interactions %>%
select(-data) %>%
unnest(interactions)
If the data set was not so small, and interactions could be identified, you would see something as below.
## # A tibble: 100 x 9
## sample ligand receptor ligand.name receptor.name origin destination
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 sampl… clust… cluster… PTMA VIPR1 clust… cluster 2
## 2 sampl… clust… cluster… B2M KLRD1 clust… cluster 2
## 3 sampl… clust… cluster… IL16 CD4 clust… cluster 2
## 4 sampl… clust… cluster… HLA-B KLRD1 clust… cluster 2
## 5 sampl… clust… cluster… CALM1 VIPR1 clust… cluster 2
## 6 sampl… clust… cluster… HLA-E KLRD1 clust… cluster 2
## 7 sampl… clust… cluster… GNAS VIPR1 clust… cluster 2
## 8 sampl… clust… cluster… B2M HFE clust… cluster 2
## 9 sampl… clust… cluster… PTMA VIPR1 clust… cluster 3
## 10 sampl… clust… cluster… CALM1 VIPR1 clust… cluster 3
## # … with 90 more rows, and 2 more variables: interaction.type <chr>,
## # LRscore <dbl>
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS: /stornext/System/data/apps/R/R-4.0.2/lib64/R/lib/libRblas.so
## LAPACK: /stornext/System/data/apps/R/R-4.0.2/lib64/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidyseurat_0.1.11 Seurat_3.2.2 ggplot2_3.3.2 magrittr_1.5
## [5] purrr_0.3.4 tidyr_1.1.2 dplyr_1.0.2 knitr_1.30
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_1.4-1
## [3] deldir_0.1-29 ellipsis_0.3.1
## [5] ggridges_0.5.2 XVector_0.28.0
## [7] GenomicRanges_1.40.0 spatstat.data_1.4-3
## [9] farver_2.0.3 leiden_0.3.3
## [11] listenv_0.8.0 ggrepel_0.8.2
## [13] RSpectra_0.16-0 fansi_0.4.1
## [15] codetools_0.2-16 splines_4.0.2
## [17] polyclip_1.10-0 jsonlite_1.7.1
## [19] ica_1.0-2 cluster_2.1.0
## [21] png_0.1-7 pheatmap_1.0.12
## [23] uwot_0.1.8 shiny_1.5.0
## [25] sctransform_0.3 compiler_4.0.2
## [27] httr_1.4.2 assertthat_0.2.1
## [29] Matrix_1.2-18 fastmap_1.0.1
## [31] lazyeval_0.2.2 limma_3.44.3
## [33] cli_2.0.2 later_1.1.0.1
## [35] htmltools_0.5.0 tools_4.0.2
## [37] rsvd_1.0.3 igraph_1.2.5
## [39] GenomeInfoDbData_1.2.3 gtable_0.3.0
## [41] glue_1.4.2 RANN_2.6.1
## [43] reshape2_1.4.4 rappdirs_0.3.1
## [45] Rcpp_1.0.5 spatstat_1.64-1
## [47] Biobase_2.48.0 vctrs_0.3.4
## [49] nlme_3.1-148 lmtest_0.9-38
## [51] xfun_0.18 stringr_1.4.0
## [53] globals_0.13.0 mime_0.9
## [55] miniUI_0.1.1.1 lifecycle_0.2.0
## [57] irlba_2.3.3 goftest_1.2-2
## [59] future_1.19.1 edgeR_3.30.3
## [61] zlibbioc_1.34.0 MASS_7.3-51.6
## [63] zoo_1.8-8 scales_1.1.1
## [65] promises_1.1.1 spatstat.utils_1.17-0
## [67] parallel_4.0.2 SummarizedExperiment_1.18.2
## [69] RColorBrewer_1.1-2 SingleCellExperiment_1.10.1
## [71] reticulate_1.16 pbapply_1.4-3
## [73] gridExtra_2.3 rpart_4.1-15
## [75] reshape_0.8.8 stringi_1.5.3
## [77] highr_0.8 S4Vectors_0.26.1
## [79] BiocGenerics_0.34.0 GenomeInfoDb_1.24.2
## [81] rlang_0.4.7 pkgconfig_2.0.3
## [83] matrixStats_0.57.0 bitops_1.0-6
## [85] evaluate_0.14 lattice_0.20-41
## [87] ROCR_1.0-11 tensor_1.5
## [89] labeling_0.3 patchwork_1.0.1
## [91] htmlwidgets_1.5.2 cowplot_1.1.0
## [93] tidyselect_1.1.0 GGally_2.0.0
## [95] RcppAnnoy_0.0.16 plyr_1.8.6
## [97] R6_2.4.1 IRanges_2.22.2
## [99] generics_0.0.2 DelayedArray_0.14.1
## [101] pillar_1.4.6 withr_2.3.0
## [103] mgcv_1.8-31 fitdistrplus_1.1-1
## [105] survival_3.1-12 abind_1.4-5
## [107] RCurl_1.98-1.2 tibble_3.0.4
## [109] future.apply_1.6.0 crayon_1.3.4
## [111] KernSmooth_2.23-17 utf8_1.1.4
## [113] dittoSeq_1.0.2 plotly_4.9.2.1
## [115] locfit_1.5-9.4 grid_4.0.2
## [117] data.table_1.13.0 digest_0.6.25
## [119] xtable_1.8-4 httpuv_1.5.4
## [121] stats4_4.0.2 munsell_0.5.0
## [123] viridisLite_0.3.0