scDotPlot
Introduction
Dot plots of single-cell RNA-seq data allow for an examination of the
relationships between cell groupings (e.g. clusters) and marker gene
expression. The scDotPlot package offers a unified approach to perform a
hierarchical clustering analysis and add annotations to the columns
and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment
and Seurat objects as well as data frames. The scDotPlot()
function uses data from scater::plotDots() or
Seurat::DotPlot() along with the aplot package
to add dendrograms from ggtree and optional
annotations.
Installation
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("scDotPlot")To install the development version directly from GitHub:
SingleCellExperiment
Prepare object
First, we normalize the object and then, for the purpose of this example, subset to remove cells without cell-type labels.
Get features
The features argument accepts a character vector with the gene IDs. For this example, we quickly obtain the top markers of for each cell type and then add them to the rowData of the object.
Plot logcounts
Finally, we create the plot. The group arguments utilize
the colData, while the features arguments use the rowData.
The paletteList argument can be used to manually specify
the colors for the annotations specified through groupAnno
and featureAnno. The clustering of the columns shows that
cell the cell sub-types cluster by cell-type, while the clustering of
the rows shows that most of the markers clusters by their cell type.
library(scDotPlot)
library(ggsci)
sce |>
scDotPlot(features = features,
group = "level2class",
groupAnno = "level1class",
featureAnno = "Marker",
groupLegends = FALSE,
annoColors = list("level1class" = pal_d3()(7),
"Marker" = pal_d3()(7)),
annoWidth = 0.02)
scDotPlot of SingleCellExperiment logcounts
Plot Z-scores
Plotting by Z-score through scale = TRUE improves the
clustering result for the rows.
sce |>
scDotPlot(scale = TRUE,
features = features,
group = "level2class",
groupAnno = "level1class",
featureAnno = "Marker",
groupLegends = FALSE,
annoColors = list("level1class" = pal_d3()(7),
"Marker" = pal_d3()(7)),
annoWidth = 0.02)
scDotPlot of SingleCellExperiment Z-scores
Seurat
Get features
After loading the example Seurat object, we find the top markers for each cluster and add them to the assay of interest.
library(Seurat)
library(SeuratObject)
library(tibble)
data("pbmc_small")
features <- pbmc_small |>
FindAllMarkers(only.pos = TRUE, verbose = FALSE) |>
group_by(cluster) |>
dplyr::slice(1:6) |>
dplyr::select(cluster, gene)
pbmc_small[[DefaultAssay(pbmc_small)]][[]] <- pbmc_small[[DefaultAssay(pbmc_small)]][[]] |>
rownames_to_column("gene") |>
left_join(features, by = "gene") |>
column_to_rownames("gene")
features <- features |>
deframe()Plot logcounts
Plotting a Seurat object is similar to plotting a SingleCellExperiment object.
pbmc_small |>
scDotPlot(features = features,
group = "RNA_snn_res.1",
groupAnno = "RNA_snn_res.1",
featureAnno = "cluster",
annoColors = list("RNA_snn_res.1" = pal_d3()(7),
"cluster" = pal_d3()(7)),
groupLegends = FALSE,
annoWidth = 0.075)
scDotPlot of Seurat logcounts
Plot Z-scores
Again, we see that plotting by Z-score improves the clustering result for the rows.
pbmc_small |>
scDotPlot(scale = TRUE,
features = features,
group = "RNA_snn_res.1",
groupAnno = "RNA_snn_res.1",
featureAnno = "cluster",
annoColors = list("RNA_snn_res.1" = pal_d3()(7),
"cluster" = pal_d3()(7)),
groupLegends = FALSE,
annoWidth = 0.075)
scDotPlot of Seurat Z-scores
Package support
The Bioconductor support
site is the preferred method to ask for help. Before posting, it’s
recommended to check previous
posts for the answer and look over the posting
guide. For the post, it’s important to use the
scDotPlot tag and provide both a minimal reproducible
example and session information.
Acknowledgement
This package was inspired by the single-cell example from aplot.
Session info
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## 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
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] tibble_3.2.1 Seurat_5.1.0
## [3] SeuratObject_5.0.2 sp_2.1-4
## [5] ggsci_3.2.0 scDotPlot_1.1.0
## [7] AnnotationDbi_1.69.0 dplyr_1.1.4
## [9] purrr_1.0.2 scran_1.35.0
## [11] scuttle_1.17.0 scRNAseq_2.20.0
## [13] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
## [15] Biobase_2.67.0 GenomicRanges_1.59.0
## [17] GenomeInfoDb_1.43.1 IRanges_2.41.1
## [19] S4Vectors_0.45.2 BiocGenerics_0.53.3
## [21] generics_0.1.3 MatrixGenerics_1.19.0
## [23] matrixStats_1.4.1 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.5 ProtGenerics_1.39.0 spatstat.sparse_3.1-0
## [4] bitops_1.0-9 httr_1.4.7 RColorBrewer_1.1-3
## [7] tools_4.4.2 sctransform_0.4.1 alabaster.base_1.7.2
## [10] utf8_1.2.4 R6_2.5.1 HDF5Array_1.35.1
## [13] uwot_0.2.2 lazyeval_0.2.2 rhdf5filters_1.19.0
## [16] withr_3.0.2 gridExtra_2.3 progressr_0.15.0
## [19] cli_3.6.3 spatstat.explore_3.3-3 fastDummies_1.7.4
## [22] labeling_0.4.3 alabaster.se_1.7.0 sass_0.4.9
## [25] spatstat.data_3.1-4 ggridges_0.5.6 pbapply_1.7-2
## [28] yulab.utils_0.1.8 Rsamtools_2.23.0 scater_1.35.0
## [31] parallelly_1.39.0 limma_3.63.2 RSQLite_2.3.8
## [34] gridGraphics_0.5-1 BiocIO_1.17.0 spatstat.random_3.3-2
## [37] ica_1.0-3 Matrix_1.7-1 ggbeeswarm_0.7.2
## [40] fansi_1.0.6 abind_1.4-8 lifecycle_1.0.4
## [43] yaml_2.3.10 edgeR_4.5.0 rhdf5_2.51.0
## [46] SparseArray_1.7.2 BiocFileCache_2.15.0 Rtsne_0.17
## [49] grid_4.4.2 blob_1.2.4 promises_1.3.0
## [52] dqrng_0.4.1 ExperimentHub_2.15.0 crayon_1.5.3
## [55] miniUI_0.1.1.1 lattice_0.22-6 beachmat_2.23.1
## [58] cowplot_1.1.3 GenomicFeatures_1.59.1 KEGGREST_1.47.0
## [61] sys_3.4.3 maketools_1.3.1 pillar_1.9.0
## [64] knitr_1.49 metapod_1.15.0 rjson_0.2.23
## [67] future.apply_1.11.3 codetools_0.2-20 leiden_0.4.3.1
## [70] glue_1.8.0 ggfun_0.1.7 spatstat.univar_3.1-1
## [73] data.table_1.16.2 treeio_1.31.0 vctrs_0.6.5
## [76] png_0.1-8 gypsum_1.3.0 spam_2.11-0
## [79] gtable_0.3.6 cachem_1.1.0 xfun_0.49
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## [94] RcppAnnoy_0.0.22 bslib_0.8.0 irlba_2.3.5.1
## [97] vipor_0.4.7 KernSmooth_2.23-24 colorspace_2.1-1
## [100] DBI_1.2.3 tidyselect_1.2.1 bit_4.5.0
## [103] compiler_4.4.2 curl_6.0.1 httr2_1.0.6
## [106] BiocNeighbors_2.1.0 DelayedArray_0.33.2 plotly_4.10.4
## [109] rtracklayer_1.67.0 scales_1.3.0 lmtest_0.9-40
## [112] rappdirs_0.3.3 goftest_1.2-3 stringr_1.5.1
## [115] digest_0.6.37 spatstat.utils_3.1-1 alabaster.matrix_1.7.0
## [118] rmarkdown_2.29 XVector_0.47.0 htmltools_0.5.8.1
## [121] pkgconfig_2.0.3 dbplyr_2.5.0 fastmap_1.2.0
## [124] ensembldb_2.31.0 rlang_1.1.4 htmlwidgets_1.6.4
## [127] UCSC.utils_1.3.0 shiny_1.9.1 farver_2.1.2
## [130] jquerylib_0.1.4 zoo_1.8-12 jsonlite_1.8.9
## [133] BiocParallel_1.41.0 BiocSingular_1.23.0 RCurl_1.98-1.16
## [136] magrittr_2.0.3 ggplotify_0.1.2 GenomeInfoDbData_1.2.13
## [139] dotCall64_1.2 patchwork_1.3.0 Rhdf5lib_1.29.0
## [142] munsell_0.5.1 Rcpp_1.0.13-1 viridis_0.6.5
## [145] ape_5.8 reticulate_1.40.0 stringi_1.8.4
## [148] alabaster.schemas_1.7.0 zlibbioc_1.52.0 MASS_7.3-61
## [151] AnnotationHub_3.15.0 plyr_1.8.9 parallel_4.4.2
## [154] listenv_0.9.1 ggrepel_0.9.6 deldir_2.0-4
## [157] Biostrings_2.75.1 splines_4.4.2 tensor_1.5
## [160] locfit_1.5-9.10 igraph_2.1.1 spatstat.geom_3.3-3
## [163] RcppHNSW_0.6.0 buildtools_1.0.0 reshape2_1.4.4
## [166] ScaledMatrix_1.15.0 BiocVersion_3.21.1 XML_3.99-0.17
## [169] evaluate_1.0.1 BiocManager_1.30.25 httpuv_1.6.15
## [172] RANN_2.6.2 tidyr_1.3.1 polyclip_1.10-7
## [175] future_1.34.0 scattermore_1.2 alabaster.sce_1.7.0
## [178] ggplot2_3.5.1 rsvd_1.0.5 xtable_1.8-4
## [181] restfulr_0.0.15 AnnotationFilter_1.31.0 tidytree_0.4.6
## [184] RSpectra_0.16-2 later_1.3.2 viridisLite_0.4.2
## [187] aplot_0.2.3 beeswarm_0.4.0 memoise_2.0.1
## [190] GenomicAlignments_1.43.0 cluster_2.1.6 globals_0.16.3