R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.
Home Page: https://samuel-marsh.github.io/scCustomize/
License: GNU General Public License v3.0
Hi,
I think this is a known issue, but would it be possible to add functionality for labeling the colors when using split.by in VlnPlots or Stacked_VlnPlots?
Best,
Niko
UPDATE
domino plot on hold for now. potentially revisit if requested or time to add.
Thank you for the Package!
The DotPlot_scCustom is not behaving as expected. Currenlty only genes can be plotted. Not the numeric variables in the meta data such as module scores in a seurat metadata. Seurat's Default DotPlot works same with the same synatx as shown in the code below
Looking at the source code, DotPlot use the Gene_Present function first to cross check the supplied list of 'features'.
features_list <- Gene_Present(data = seurat_object, gene_list = features,omit_warn = TRUE, print_msg = FALSE, case_check_msg = TRUE)[[1]]
> DotPlot_scCustom
function (seurat_object, features, colors_use = viridis_plasma_dark_high,
remove_axis_titles = TRUE, x_lab_rotate = FALSE, y_lab_rotate = FALSE,
facet_label_rotate = FALSE, flip_axes = FALSE, ...)
{
Is_Seurat(seurat_object = seurat_object)
features_list <- Gene_Present(data = seurat_object, gene_list = features,
omit_warn = TRUE, print_msg = FALSE, case_check_msg = TRUE)[[1]]
However, Gene_Present function only checks for rownames of the Default Assay matrix in Seurat and do not look for meta data columns. (possible_features <- rownames(x = GetAssayData(object = data, assay = assay)))
> Gene_Present
function (data, gene_list, case_check = TRUE, case_check_msg = TRUE,
print_msg = TRUE, omit_warn = TRUE, return_none = FALSE,
seurat_assay = NULL)
{
accepted_types <- c("data.frame", "dgCMatrix", "dgTMatrix",
"tibble")
if ((class(x = data)[[1]] == "Seurat")) {
assay <- seurat_assay %||% DefaultAssay(object = data)
possible_features <- rownames(x = GetAssayData(object = data,
assay = assay))
Using FetchData function from Seurat might be better as it already looks at both meta data and the assay matrix for the supplied vars. As to other object classes I do not have experience with them.
> head(pbmc3k.final[[]])
orig.ident nCount_RNA nFeature_RNA seurat_annotations percent.mt RNA_snn_res.0.5 seurat_clusters
AAACATACAACCAC pbmc3k 2419 779 Memory CD4 T 3.0177759 1 1
AAACATTGAGCTAC pbmc3k 4903 1352 B 3.7935958 3 3
AAACATTGATCAGC pbmc3k 3147 1129 Memory CD4 T 0.8897363 1 1
AAACCGTGCTTCCG pbmc3k 2639 960 CD14+ Mono 1.7430845 2 2
AAACCGTGTATGCG pbmc3k 980 521 NK 1.2244898 6 6
AAACGCACTGGTAC pbmc3k 2163 781 Memory CD4 T 1.6643551 1 1
> DotPlot(pbmc3k.final, features = c("nCount_RNA","percent.mt"))
> pbmc3k.final <- AddModuleScore(pbmc3k.final, features = list(c("MRC1","CSF1R","CD68","CD14")), name = "Gene_Sig")
Warning: The following features are not present in the object: MRC1, not searching for symbol synonyms
> DotPlot(pbmc3k.final, features = c("Gene_Sig1","percent.mt"))
> DotPlot_scCustom(pbmc3k.final, features = c("Gene_Sig1","percent.mt"))
Error in Gene_Present(data = seurat_object, gene_list = features, omit_warn = TRUE, :
No requested features found.> sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scCustomize_0.7.0 Seurat_4.1.1 sp_1.4-7 SeuratObject_4.1.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.25 tidyselect_1.1.2 htmlwidgets_1.5.4
[5] grid_4.2.0 Rtsne_0.16 devtools_2.4.3 munsell_0.5.0
[9] codetools_0.2-18 ica_1.0-2 future_1.25.0 miniUI_0.1.1.1
[13] withr_2.5.0 spatstat.random_2.2-0 colorspace_2.0-3 progressr_0.10.0
[17] Biobase_2.56.0 rstudioapi_0.13 stats4_4.2.0 SingleCellExperiment_1.18.0
[21] ROCR_1.0-11 ggsignif_0.6.3 tensor_1.5 listenv_0.8.0
[25] labeling_0.4.2 MatrixGenerics_1.8.0 GenomeInfoDbData_1.2.8 polyclip_1.10-0
[29] farver_2.1.0 pheatmap_1.0.12 rprojroot_2.0.3 parallelly_1.31.1
[33] vctrs_0.4.1 generics_0.1.2 dittoSeq_1.9.0 R6_2.5.1
[37] GenomeInfoDb_1.32.2 ggbeeswarm_0.6.0 bitops_1.0-7 spatstat.utils_2.3-1
[41] cachem_1.0.6 DelayedArray_0.22.0 assertthat_0.2.1 promises_1.2.0.1
[45] scales_1.2.0 rgeos_0.5-9 beeswarm_0.4.0 gtable_0.3.0
[49] globals_0.15.0 processx_3.5.3 goftest_1.2-3 rlang_1.0.2
[53] GlobalOptions_0.1.2 splines_4.2.0 rstatix_0.7.0 lazyeval_0.2.2
[57] spatstat.geom_2.4-0 broom_0.8.0 reshape2_1.4.4 abind_1.4-5
[61] backports_1.4.1 httpuv_1.6.5 tools_4.2.0 usethis_2.1.5
[65] ggplot2_3.3.6 ellipsis_0.3.2 spatstat.core_2.4-4 RColorBrewer_1.1-3
[69] BiocGenerics_0.42.0 sessioninfo_1.2.2 ggridges_0.5.3 Rcpp_1.0.8.3
[73] plyr_1.8.7 zlibbioc_1.42.0 purrr_0.3.4 RCurl_1.98-1.6
[77] ps_1.7.0 prettyunits_1.1.1 ggpubr_0.4.0 rpart_4.1.16
[81] deldir_1.0-6 pbapply_1.5-0 viridis_0.6.2 cowplot_1.1.1
[85] S4Vectors_0.34.0 zoo_1.8-10 SummarizedExperiment_1.26.1 ggrepel_0.9.1
[89] cluster_2.1.3 fs_1.5.2 magrittr_2.0.3 data.table_1.14.2
[93] scattermore_0.8 circlize_0.4.15 lmtest_0.9-40 RANN_2.6.1
[97] fitdistrplus_1.1-8 matrixStats_0.62.0 pkgload_1.2.4 patchwork_1.1.1
[101] mime_0.12 xtable_1.8-4 IRanges_2.30.0 gridExtra_2.3
[105] shape_1.4.6 testthat_3.1.4 compiler_4.2.0 tibble_3.1.7
[109] colorway_0.2.0 KernSmooth_2.23-20 crayon_1.5.1 htmltools_0.5.2
[113] mgcv_1.8-40 later_1.3.0 ggprism_1.0.3.9000 tidyr_1.2.0
[117] lubridate_1.8.0 DBI_1.1.2 MASS_7.3-57 Matrix_1.4-1
[121] car_3.0-13 brio_1.1.3 cli_3.3.0 parallel_4.2.0
[125] igraph_1.3.1 GenomicRanges_1.48.0 forcats_0.5.1 pkgconfig_2.0.3
[129] plotly_4.10.0 spatstat.sparse_2.1-1 paletteer_1.4.0 vipor_0.4.5
[133] XVector_0.36.0 snakecase_0.11.0 stringr_1.4.0 callr_3.7.0
[137] digest_0.6.29 sctransform_0.3.3 RcppAnnoy_0.0.19 janitor_2.1.0
[141] spatstat.data_2.2-0 leiden_0.4.2 uwot_0.1.11 curl_4.3.2
[145] shiny_1.7.1 lifecycle_1.0.1 nlme_3.1-157 jsonlite_1.8.0
[149] carData_3.0-5 desc_1.4.1 viridisLite_0.4.0 fansi_1.0.3
[153] pillar_1.7.0 lattice_0.20-45 fastmap_1.1.0 httr_1.4.3
[157] pkgbuild_1.3.1 survival_3.3-1 glue_1.6.2 remotes_2.4.2
[161] png_0.1-7 stringi_1.7.6 rematch2_2.1.2 memoise_2.0.1
[165] dplyr_1.0.9 irlba_2.3.5 future.apply_1.9.0 ```
</details>Hey Samuel, thank you so much for putting together this tool, I am exploring it at the moment and am already super happy with it!
As you noted in the vignette, the FeaturePlot from Seurat makes it sometwhat difficult to interpret the gene expression in a way as the non-expressing cells are sometimes plotted on top of the expressing cells, your solution to by default order them imho makes a lot of sense! However, in this case, you might also be over-emphasizing certain areas in the case that there are more non-expressing cells but the few expressing cells are placed on top?
For this reason, I was wondering if it would be possible to implement the alpha parameter to the function, as it would allow to "see through" the expressors and would additionally highlight areas of high expression.
I have searched this in the Seurat github aswell and it doesn't seem that they are implementing it, but I found this code snippet that makes it somewhat work: plot$layers[[1]]$aes_params$alpha <- 0.6 (my main issue is that this will only affect the last column if split by sample e.g.)
Thanks again for all the work you put into this and for sharing it!
Best, Julia
Thank you so much for developing such a great toolkit to analyze single cell data. I have recently moved from Seurat to bioconductor's SingleCellExperiment to analyze my sc data. While using Seurat I regularly used scCustomize and I was wondering if it is in the books to make scCustomize compatible with SingleCellExperiment objects.
Again, thank you so much for developing such an amazing package!
I wanted to determine if the number of particular cell type in a cluster is changing with the treatment conditions. I have used stimulated vs. control PBMC datasets Seurat vignette for this analysis comparing two conditions.
Question:
I used the code below to get the counts in each cluster and treatment condition. I wanted to know if number of cells from the code below is normalized to the total number of cells sequenced for each samples.
table(Idents(immune.combined), immune.combined$stim)
Does the output of number of cells from scCustomize is normalized to the total number of cells sequenced for each samples? if the cell numbers are not normalized then what scCustomize function should I use?
Thank you.
Build on broadinstitute/CellBender#145.
Create R standalone function to read new CellBender outputs without need for PyTables (R native function).
Built on Seurat Read10X_h5 code.
Loads matrix only.
Merge_Sparse_Data_All fixesUpdate Cluster_Highlight_Plot and Meta_Highlight_Plot
Cluster_Highlight_PlotDimPlot_scCustom under the hood for plot consistencyHi @samuel-marsh! Thanks for this useful package.
Is there anyway to change the border thickness of Stacked_VlnPlot? When I save it with ggsave and arrange it into a figure it looks incredibly thick:
Thanks in advance for looking into this !
VlnPlot_scCustomNew Defaults
cli_progress_along() mainlyHello,
Thank you for your wonderful work!
I used Clustered_Dotplot, to visualize my dataset, but it always gives this error. I update the version to develop version,but difficulties remain.
library(SeuratData)
InstallData("pbmc3k")
data("pbmc3k")
pbmc <- pbmc3k.SeuratData::pbmc3k.final
pbmc <- UpdateSeuratObject(pbmc)
Clustered_DotPlot(seurat_object = pbmc, features = VariableFeatures(pbmc)[1:5])
Error in ComplexHeatmap::Legend(labels = c(0.25, 0.5, 0.75, 1), title = "Percent Expressing", :
unused argument (graphics = list(function(x, y, w, h) grid.circle(x = x, y = y, r = sqrt(0.25) * unit(2, "mm"), gp = gpar(fill = "black")), function(x, y, w, h) grid.circle(x = x, y = y, r = sqrt(0.5) * unit(2, "mm"), gp = gpar(fill = "black")), function(x, y, w, h) grid.circle(x = x, y = y, r = sqrt(0.75) * unit(2, "mm"), gp = gpar(fill = "black")),
> sessionInfo()
R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936 LC_CTYPE=Chinese (Simplified)_China.936 LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C LC_TIME=Chinese (Simplified)_China.936
attached base packages:
[1] parallel stats4 grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] scCustomize_0.7.0.9908 pbmc3k.SeuratData_3.1.4 SeuratData_0.2.2 forcats_0.5.1 purrr_0.3.4
[6] readr_1.4.0 tibble_3.1.6 tidyverse_1.3.1 rlang_1.0.1 clusterProfiler_4.3.1
[11] SeuratWrappers_0.3.0 stringr_1.4.0 Hmisc_4.5-0 Formula_1.2-4 survival_3.2-11
[16] lattice_0.20-44 scales_1.1.1 RColorBrewer_1.1-2 ggsci_2.9 tidyr_1.1.3
[21] slingshot_2.1.1 TrajectoryUtils_1.1.2 SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0 Biobase_2.50.0
[26] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7 IRanges_2.24.1 S4Vectors_0.28.1 BiocGenerics_0.36.1
[31] MatrixGenerics_1.2.1 matrixStats_0.60.0 princurve_2.1.6 datapasta_3.1.0 heatmap3_1.1.9
[36] pvclust_2.2-0 dendextend_1.15.1 dendsort_0.3.4 circlize_0.4.12 ComplexHeatmap_2.4.3
[41] dplyr_1.0.7 plyr_1.8.6 tradeSeq_1.7.07 SeuratObject_4.0.4 Seurat_4.1.0
[46] corrplot_0.88 cowplot_1.1.1 patchwork_1.1.1 ggplot2_3.3.3 reticulate_1.20
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-2 ps_1.6.0 lmtest_0.9-38 rprojroot_2.0.2 crayon_1.5.0
[7] spatstat.core_2.1-2 rbibutils_2.2.3 MASS_7.3-54 dittoSeq_1.9.0 nlme_3.1-152 backports_1.2.1
[13] reprex_2.0.0 GOSemSim_2.14.2 XVector_0.30.0 ROCR_1.0-11 readxl_1.3.1 irlba_2.3.3
[19] callr_3.7.0 limma_3.46.0 BiocParallel_1.24.1 rjson_0.2.20 bit64_4.0.5 glue_1.6.1
[25] pheatmap_1.0.12 sctransform_0.3.3 processx_3.5.2 vipor_0.4.5 spatstat.sparse_2.0-0 AnnotationDbi_1.52.0
[31] DOSE_3.14.0 spatstat.geom_2.3-2 haven_2.4.1 tidyselect_1.1.1 usethis_2.1.5 rio_0.5.27
[37] fitdistrplus_1.1-5 zoo_1.8-9 ggpubr_0.4.0 xtable_1.8-4 magrittr_2.0.2 Rdpack_2.1.2
[43] cli_3.2.0 zlibbioc_1.36.0 sn_2.0.0 rstudioapi_0.13 miniUI_0.1.1.1 rpart_4.1-15
[49] mathjaxr_1.4-0 fastmatch_1.1-3 shiny_1.6.0 xfun_0.29 clue_0.3-58 multtest_2.46.0
[55] pkgbuild_1.3.1 cluster_2.1.2 tidygraph_1.2.0 ggrepel_0.9.1 listenv_0.8.0 png_0.1-7
[61] future_1.21.0 withr_2.4.3 bitops_1.0-7 ggforce_0.3.3 cellranger_1.1.0 pillar_1.7.0
[67] GlobalOptions_0.1.2 cachem_1.0.6 multcomp_1.4-17 fs_1.5.2 GetoptLong_1.0.5 paletteer_1.4.0
[73] vctrs_0.3.8 ellipsis_0.3.2 generics_0.1.0 devtools_2.4.3 metap_1.4 tools_4.0.5
[79] foreign_0.8-81 beeswarm_0.4.0 munsell_0.5.0 tweenr_1.0.2 fgsea_1.14.0 DelayedArray_0.16.3
[85] fastmap_1.1.0 compiler_4.0.5 pkgload_1.2.4 abind_1.4-5 httpuv_1.6.1 sessioninfo_1.2.2
[91] clipr_0.7.1 plotly_4.9.3 GenomeInfoDbData_1.2.4 gridExtra_2.3 edgeR_3.30.3 colorway_0.2.0
[97] deldir_1.0-6 mutoss_0.1-12 utf8_1.2.2 later_1.2.0 prismatic_1.1.0 jsonlite_1.7.3
[103] pbapply_1.4-3 carData_3.0-4 lazyeval_0.2.2 promises_1.2.0.1 car_3.0-11 latticeExtra_0.6-29
[109] R.utils_2.10.1 goftest_1.2-2 spatstat.utils_2.3-0 checkmate_2.0.0 openxlsx_4.2.4 sandwich_3.0-1
[115] Rtsne_0.15 downloader_0.4 uwot_0.1.10 igraph_1.2.6 plotrix_3.8-1 numDeriv_2016.8-1.1
[121] htmltools_0.5.1.1 memoise_2.0.1 locfit_1.5-9.4 graphlayouts_0.7.1 viridisLite_0.4.0 digest_0.6.29
[127] assertthat_0.2.1 rappdirs_0.3.3 mime_0.12 RSQLite_2.2.7 yulab.utils_0.0.4 future.apply_1.7.0
[133] remotes_2.4.2 data.table_1.14.0 blob_1.2.2 R.oo_1.24.0 splines_4.0.5 labeling_0.4.2
[139] rematch2_2.1.2 RCurl_1.98-1.3 broom_0.7.6 hms_1.1.0 modelr_0.1.8 colorspace_1.4-1
[145] base64enc_0.1-3 BiocManager_1.30.16 mnormt_2.0.2 ggbeeswarm_0.6.0 shape_1.4.5 tmvnsim_1.0-2
[151] ggrastr_0.2.3 nnet_7.3-16 Rcpp_1.0.7 RANN_2.6.1 mvtnorm_1.1-2 enrichplot_1.10.2
[157] fansi_1.0.2 brio_1.1.3 parallelly_1.25.0 R6_2.5.1 ggridges_0.5.3 lifecycle_1.0.1
[163] zip_2.2.0 TFisher_0.2.0 curl_4.3.2 ggsignif_0.6.2 leiden_0.3.8 testthat_3.1.2
[169] snakecase_0.11.0 fastcluster_1.2.3 DO.db_2.9 Matrix_1.4-0 qvalue_2.20.0 TH.data_1.0-10
[175] desc_1.4.0 RcppAnnoy_0.0.18 htmlwidgets_1.5.3 polyclip_1.10-0 shadowtext_0.0.9 rvest_1.0.0
[181] mgcv_1.8-35 globals_0.14.0 htmlTable_2.2.1 codetools_0.2-18 lubridate_1.7.10 GO.db_3.11.4
[187] prettyunits_1.1.1 dbplyr_2.1.1 R.methodsS3_1.8.1 gtable_0.3.0 DBI_1.1.1 tensor_1.5
[193] httr_1.4.2 KernSmooth_2.23-20 stringi_1.7.6 reshape2_1.4.4 farver_2.1.0 viridis_0.6.1
[199] xml2_1.3.3 rvcheck_0.1.8 scattermore_0.7 bit_4.0.4 scatterpie_0.1.6 jpeg_0.1-8.1
[205] spatstat.data_2.1-0 ggraph_2.0.5 janitor_2.1.0 pkgconfig_2.0.3 ggprism_1.0.3.9000 rstatix_0.7.0
[211] knitr_1.37
So I have a UMAP that assigns colors based on cell size, and then I used scCustomize to make a clustered dotplot. However, i notice that the clustered Dotplot order is alphabetical. Any way to make it a custom order (but keep the associated colors), so that i can match my legends? I feel like I can do this in photoshop, but it would be tedious. I read in another thread that the clustered dotplot isn't ggplot? so not sure how to approach it.
I'm trying to match this order from my umap (made with dittoseq):
with this from the dotplot:
Thanks!
I used DimPlot_scCustom and Clustered_Dotplot, to visualize the cell types and markers respectively in my dataset. The two functions do not appear to follow the same color scheme order. For example, the color of astrocytes in the umap is gray and in the clustered dot plot it's magenta. It would be nice to coordinate the cell-type colors across all functions.
Thank you
Check it out!
Hi, first of all, thanks for making the excellent package, it has been extremely helpful. However, I have a suggestion for an improvement. You can pass multiple genes to the nebulosa function plot_density and likewise your custom function that uses plot_density, Plot_Custom_Density in order to make a nice figure panel of nebulosa figures. However, when you pass multiple genes to Plot_Custom_Density with the custom_palette option set to your own bespoke color scheme, the colors are only applied to the last plot. Perhaps there is a way around this to get it to apply to all the plots I am not aware of, but if not, it would be a helpful amendment.
There seems to be a problem when the combine = FALSE flag is set in FeaturePlot_scCustomize(). The function call simply returns NULL. Find mwe below, thanks a lot for your help!
Best, Niko
library(Seurat)
library(SeuratData)
library(scCustomize)
library(dplyr)
InstallData("pbmc3k")
data("pbmc3k")
pbmc = pbmc3k %>%
NormalizeData() %>%
ScaleData() %>%
FindVariableFeatures() %>%
RunPCA() %>%
RunUMAP(dims = 1:10)
FeaturePlot(pbmc,features = c('NRG1','ERBB2'), combine = FALSE)
FeaturePlot_scCustom(pbmc,features = c('NRG1','ERBB2'), combine = FALSE)Output:
> x
[[1]]
[[2]]
> y
NULL
SessionInfo:
R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.2.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] pbmc3k.SeuratData_3.1.4 scCustomize_0.7.0 SeuratData_0.2.1 SeuratObject_4.0.4 Seurat_4.1.0
[6] dplyr_1.0.8
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.24 tidyselect_1.1.2 htmlwidgets_1.5.4
[5] grid_4.1.2 Rtsne_0.15 devtools_2.4.3 munsell_0.5.0
[9] codetools_0.2-18 ica_1.0-2 future_1.24.0 miniUI_0.1.1.1
[13] withr_2.5.0 spatstat.random_2.2-0 colorspace_2.0-3 Biobase_2.54.0
[17] rstudioapi_0.13 stats4_4.1.2 SingleCellExperiment_1.16.0 ROCR_1.0-11
[21] ggsignif_0.6.3 tensor_1.5 listenv_0.8.0 labeling_0.4.2
[25] MatrixGenerics_1.6.0 GenomeInfoDbData_1.2.7 polyclip_1.10-0 farver_2.1.0
[29] pheatmap_1.0.12 rprojroot_2.0.3 parallelly_1.30.0 vctrs_0.4.0
[33] generics_0.1.2 dittoSeq_1.7.0 R6_2.5.1 GenomeInfoDb_1.30.1
[37] ggbeeswarm_0.6.0 bitops_1.0-7 spatstat.utils_2.3-0 cachem_1.0.6
[41] DelayedArray_0.20.0 promises_1.2.0.1 scales_1.1.1 beeswarm_0.4.0
[45] gtable_0.3.0 globals_0.14.0 processx_3.5.3 goftest_1.2-3
[49] rlang_1.0.2 GlobalOptions_0.1.2 splines_4.1.2 rstatix_0.7.0
[53] lazyeval_0.2.2 spatstat.geom_2.4-0 broom_0.7.12 reshape2_1.4.4
[57] abind_1.4-5 backports_1.4.1 httpuv_1.6.5 tools_4.1.2
[61] usethis_2.1.5 ggplot2_3.3.5 ellipsis_0.3.2 spatstat.core_2.4-2
[65] RColorBrewer_1.1-3 BiocGenerics_0.40.0 sessioninfo_1.2.2 ggridges_0.5.3
[69] Rcpp_1.0.8.3 plyr_1.8.7 zlibbioc_1.40.0 purrr_0.3.4
[73] RCurl_1.98-1.6 ps_1.6.0 prettyunits_1.1.1 ggpubr_0.4.0
[77] rpart_4.1.16 deldir_1.0-6 pbapply_1.5-0 viridis_0.6.2
[81] cowplot_1.1.1 S4Vectors_0.32.4 zoo_1.8-9 SummarizedExperiment_1.24.0
[85] ggrepel_0.9.1 cluster_2.1.2 fs_1.5.2 magrittr_2.0.3
[89] RSpectra_0.16-0 data.table_1.14.2 scattermore_0.8 circlize_0.4.14
[93] lmtest_0.9-40 RANN_2.6.1 fitdistrplus_1.1-8 matrixStats_0.61.0
[97] pkgload_1.2.4 patchwork_1.1.1 mime_0.12 xtable_1.8-4
[101] IRanges_2.28.0 gridExtra_2.3 shape_1.4.6 testthat_3.1.3
[105] compiler_4.1.2 tibble_3.1.6 colorway_0.2.0 KernSmooth_2.23-20
[109] crayon_1.5.1 htmltools_0.5.2 mgcv_1.8-39 later_1.3.0
[113] ggprism_1.0.3.9000 tidyr_1.2.0 lubridate_1.8.0 MASS_7.3-55
[117] rappdirs_0.3.3 Matrix_1.4-0 car_3.0-12 brio_1.1.3
[121] cli_3.2.0 parallel_4.1.2 igraph_1.3.0 GenomicRanges_1.46.1
[125] forcats_0.5.1 pkgconfig_2.0.3 plotly_4.10.0 spatstat.sparse_2.1-0
[129] paletteer_1.4.0 vipor_0.4.5 XVector_0.34.0 snakecase_0.11.0
[133] stringr_1.4.0 callr_3.7.0 digest_0.6.29 sctransform_0.3.3
[137] RcppAnnoy_0.0.19 janitor_2.1.0 spatstat.data_2.1-4 leiden_0.3.9
[141] uwot_0.1.11 curl_4.3.2 shiny_1.7.1 lifecycle_1.0.1
[145] nlme_3.1-155 jsonlite_1.8.0 carData_3.0-5 desc_1.4.1
[149] viridisLite_0.4.0 fansi_1.0.3 pillar_1.7.0 lattice_0.20-45
[153] fastmap_1.1.0 httr_1.4.2 pkgbuild_1.3.1 survival_3.3-1
[157] glue_1.6.2 remotes_2.4.2 png_0.1-7 stringi_1.7.6
[161] rematch2_2.1.2 memoise_2.0.1 renv_0.15.4 irlba_2.3.5
[165] future.apply_1.8.1
Other
Figure out how to deal with parent child function error messaging
Having an unknown issue with pbmcapply functionality.
Potentially shift usage to future framework using either future_lapply or furrr with progressr for progress bars.
Help wanted. If you are interested in helping please leave message here with your contact info.
Hello,
When I try to run Clustered_dotplot function, I get this error message:
Clustered_DotPlot(seurat_object = seu_MCBneu7Ksct_60dims_n110, features = Or_shortlist, colors_use_exp = viridis_pal(direction = -1, option = "D"), plot_km_elbow = F)
Error in scCustomize_Palette(num_groups = group_by_length, ggplot_default_colors = ggplot_default_colors, :
object 'color_seed' not found
I successfully run the command with another smaller dataset that has 25 clusters instead of 60. I believe the problem has to do with the generating enough colors to label clusters but I am not sure.
Hello. I came across your very nice tool of boxplots with significant comparisons of quantitative measures. I saw it at the Basic Statistics section, the boxplots.
I wonder if it can be repurposed for visualizing expression features.
For example, can I use it to visualize Pathway Module scores of a cluster/subset, split.by the 3 groups (Healthy, Mild, Severe), to see the differences in pathway activation across these conditions?
Thank you in advance.
Current function doesn’t respect provided name parameters. And can accidentally overwrite if defaults are changed even when overwrite = F.
Hello, I do get this error "meta_sample_list_test' not found" any time I set these two features to (feature 1= nCount_RNA & feature2 = nFeature_RNA). It did work for me after changing it to meta_sample_list. Thanks
r command:
Split_FeatureScatter(seurat_object = data_raw_control_seurat_obj, feature1 = "nFeature_RNA",
feature2 = "nCount_RNA", pt.size=0.1, split.by = "orig.ident", colors_use = "red")
error message:
Error in lapply(1:length(x = meta_sample_list_test), function(i) { :
object 'meta_sample_list_test' not found
Iterative Functions
Thank you for this amazing tool!
Hi,
I am using a Seurat object with EnsIDs as rownames. Unfortunately, Seurat is very bad at handling gene-level metadata. I can put the symbols and other information about each feature manually in the gene level metadata slot, but all of the Seurat functions lack an option to retrieve them upon plotting. Therefore I have to manually change the labels of each plot by scale_x_discrete(labels = df$symbols) . This works fine for DotPlots and I managed to write a function for multiple-feature plots as well. But I can't get it to work with stacked VlnPlots (as these are technically multiple wrapped plots, I assume?)
Would be great if there was a feature to supply alternative feature names to Stacked_VlnPlot. Of course, this would be very handy for all other enhanced scCustomize plotting functions, too. Maybe even with an option to just specify the slot in the feature level metadata that is to be used for plotting instead of rownames()
Thanks a lot for your help,
best Niko
Can you move ggrastr from imports to suggests? ggrastr requires Cairo which was cool 15 years ago and now is just a pain to install.
Hi! I have a color palette:
paletteer_d("tidyquant::tq_light")
that produces a colors like this:
but it doesn't override the default colors of the identity with this code:
Clustered_DotPlot(seurat_object = seurat, features = features, exp_color_min = -1, exp_color_max = 2, k=9, plot_km_elbow = FALSE, colors_use_idents = paletteer_d("tidyquant::tq_light"), raster = TRUE)
it's probably something simple i'm not catching. any thoughts?
R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] paletteer_1.4.0 UpSetR_1.4.0
[3] patchwork_1.1.1 RColorBrewer_1.1-2
[5] qs_0.25.3 viridis_0.6.2
[7] viridisLite_0.4.0 clustree_0.4.4
[9] ggraph_2.0.5 scCustomize_0.7.0
[11] xlsx_0.6.5 SeuratWrappers_0.3.0
[13] ggrepel_0.9.1 ggpubr_0.4.0
[15] monocle3_1.0.0 SingleCellExperiment_1.16.0
[17] SummarizedExperiment_1.24.0 GenomicRanges_1.46.1
[19] GenomeInfoDb_1.30.1 IRanges_2.28.0
[21] S4Vectors_0.32.3 MatrixGenerics_1.6.0
[23] matrixStats_0.61.0 Biobase_2.54.0
[25] BiocGenerics_0.40.0 dittoSeq_1.7.0
[27] cowplot_1.1.1 ggthemes_4.2.4
[29] webshot_0.5.2 clipr_0.7.1
[31] gt_0.3.1 plotly_4.10.0
[33] DT_0.20 forcats_0.5.1
[35] stringr_1.4.0 dplyr_1.0.8
[37] purrr_0.3.4 readr_2.1.2
[39] tidyr_1.2.0 tibble_3.1.6
[41] ggplot2_3.3.5 tidyverse_1.3.1
[43] Seurat_4.1.0 SeuratObject_4.0.4
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-2 ps_1.6.0
[4] rprojroot_2.0.2 foreach_1.5.2 lmtest_0.9-39
[7] crayon_1.5.0 rhdf5filters_1.6.0 spatstat.core_2.4-0
[10] rbibutils_2.2.7 MASS_7.3-55 nlme_3.1-155
[13] backports_1.4.1 reprex_2.0.1 rlang_1.0.2
[16] XVector_0.34.0 ROCR_1.0-11 readxl_1.3.1
[19] irlba_2.3.5 callr_3.7.0 limma_3.50.0
[22] stringfish_0.15.5 BiocParallel_1.28.3 rjson_0.2.21
[25] bit64_4.0.5 glue_1.6.2 pheatmap_1.0.12
[28] sctransform_0.3.3 processx_3.5.2 parallel_4.1.0
[31] vipor_0.4.5 AnnotationDbi_1.56.2 spatstat.sparse_2.1-0
[34] spatstat.geom_2.3-2 haven_2.4.3 tidyselect_1.1.2
[37] usethis_2.1.5 fitdistrplus_1.1-8 XML_3.99-0.8
[40] zoo_1.8-9 xtable_1.8-4 magrittr_2.0.2
[43] evaluate_0.15 Rdpack_2.1.3 scuttle_1.4.0
[46] cli_3.2.0 zlibbioc_1.40.0 sn_2.0.1
[49] rstudioapi_0.13 miniUI_0.1.1.1 rpart_4.1.16
[52] mathjaxr_1.4-0 GSVA_1.42.0 shiny_1.7.1
[55] BiocSingular_1.10.0 xfun_0.30 clue_0.3-60
[58] pkgbuild_1.3.1 multtest_2.48.0 cluster_2.1.2
[61] tidygraph_1.2.0 KEGGREST_1.34.0 listenv_0.8.0
[64] Biostrings_2.62.0 xlsxjars_0.6.1 png_0.1-7
[67] future_1.24.0 withr_2.5.0 bitops_1.0-7
[70] ggforce_0.3.3 plyr_1.8.6 cellranger_1.1.0
[73] GSEABase_1.56.0 dqrng_0.3.0 pillar_1.7.0
[76] RcppParallel_5.1.5 cachem_1.0.6 GlobalOptions_0.1.2
[79] multcomp_1.4-18 fs_1.5.2 GetoptLong_1.0.5
[82] DelayedMatrixStats_1.16.0 vctrs_0.3.8 ellipsis_0.3.2
[85] generics_0.1.2 devtools_2.4.3 RApiSerialize_0.1.0
[88] metap_1.7 tools_4.1.0 beeswarm_0.4.0
[91] munsell_0.5.0 tweenr_1.0.2 DelayedArray_0.20.0
[94] pkgload_1.2.4 fastmap_1.1.0 compiler_4.1.0
[97] abind_1.4-5 httpuv_1.6.5 sessioninfo_1.2.2
[100] rJava_1.0-6 GenomeInfoDbData_1.2.7 gridExtra_2.3
[103] edgeR_3.36.0 colorway_0.2.0 lattice_0.20-45
[106] deldir_1.0-6 sceasy_0.0.6 mutoss_0.1-12
[109] utf8_1.2.2 later_1.3.0 prismatic_1.1.0
[112] jsonlite_1.8.0 scales_1.1.1 graph_1.72.0
[115] ScaledMatrix_1.2.0 pbapply_1.5-0 carData_3.0-5
[118] sparseMatrixStats_1.6.0 lazyeval_0.2.2 promises_1.2.0.1
[121] car_3.0-12 doParallel_1.0.17 R.utils_2.11.0
[124] goftest_1.2-3 spatstat.utils_2.3-0 reticulate_1.24
[127] checkmate_2.0.0 openxlsx_4.2.5 rmarkdown_2.13
[130] sandwich_3.0-1 textshaping_0.3.6 statmod_1.4.36
[133] Rtsne_0.15 glmGamPoi_1.4.0 uwot_0.1.11
[136] igraph_1.2.11 HDF5Array_1.22.1 survival_3.2-13
[139] numDeriv_2016.8-1.1 yaml_2.3.5 plotrix_3.8-2
[142] systemfonts_1.0.4 memoise_2.0.1 htmltools_0.5.2
[145] locfit_1.5-9.4 graphlayouts_0.8.0 here_1.0.1
[148] digest_0.6.29 assertthat_0.2.1 mime_0.12
[151] RSQLite_2.2.9 future.apply_1.8.1 remotes_2.4.2
[154] data.table_1.14.2 blob_1.2.2 R.oo_1.24.0
[157] ragg_1.2.2 splines_4.1.0 labeling_0.4.2
[160] Rhdf5lib_1.16.0 rematch2_2.1.2 Cairo_1.5-14
[163] RCurl_1.98-1.6 broom_0.7.12 hms_1.1.1
[166] modelr_0.1.8 rhdf5_2.38.0 colorspace_2.0-3
[169] BiocManager_1.30.16 mnormt_2.0.2 ggbeeswarm_0.6.0
[172] shape_1.4.6 tmvnsim_1.0-2 ggrastr_1.0.1
[175] sass_0.4.0 Rcpp_1.0.8.2 RANN_2.6.1
[178] mvtnorm_1.1-3 circlize_0.4.14 fansi_1.0.2
[181] tzdb_0.2.0 brio_1.1.3 parallelly_1.30.0
[184] R6_2.5.1 grid_4.1.0 ggridges_0.5.3
[187] lifecycle_1.0.1 zip_2.2.0 TFisher_0.2.0
[190] bluster_1.4.0 ggsignif_0.6.3 testthat_3.1.2
[193] leiden_0.3.9 snakecase_0.11.0 Matrix_1.4-0
[196] desc_1.4.1 RcppAnnoy_0.0.19 TH.data_1.1-0
[199] iterators_1.0.14 htmlwidgets_1.5.4 beachmat_2.10.0
[202] polyclip_1.10-0 rvest_1.0.2 ComplexHeatmap_2.11.1
[205] mgcv_1.8-38 globals_0.14.0 spatstat.random_2.1-0
[208] codetools_0.2-18 lubridate_1.8.0 metapod_1.2.0
[211] prettyunits_1.1.1 dbplyr_2.1.1 RSpectra_0.16-0
[214] R.methodsS3_1.8.1 gtable_0.3.0 DBI_1.1.2
[217] tensor_1.5 httr_1.4.2 KernSmooth_2.23-20
[220] stringi_1.7.6 vroom_1.5.7 reshape2_1.4.4
[223] farver_2.1.0 annotate_1.72.0 magick_2.7.3
[226] xml2_1.3.3 BiocNeighbors_1.12.0 scattermore_0.8
[229] scran_1.22.1 bit_4.0.4 spatstat.data_2.1-2
[232] janitor_2.1.0 pkgconfig_2.0.3 ggprism_1.0.3.9000
[235] rstatix_0.7.0 knitr_1.37 Hi,
I'm using Clustered_DotPlot to make clustered dotplot. My plot has gene_ID on x-axis, which I want to change to gene_symbol. I've tried this by using DotPlot_scCustom and it worked as follows:
topN <- c("ENSRNOG00000016928", "ENSRNOG00000069361", "ENSRNOG00000017589", "ENSRNOG00000003633", "ENSRNOG00000047225")
> head(geneTable)
geneID geneSymbol
1 ENSRNOG00000066169 ENSRNOG00000066169
2 ENSRNOG00000070168 Olr56
3 ENSRNOG00000070901 Irgq
4 ENSRNOG00000018029 Doc2g
5 ENSRNOG00000031391 Ceacam16
6 ENSRNOG00000050129 AABR07000137.1
pdf(paste0(outdir, "test1.pdf"), width = 20, height = 10)
p1 <- DotPlot_scCustom(scrna.combined, features = topN, x_lab_rotate = TRUE, colors_use = "blue") + scale_x_discrete(breaks=topN, labels=geneTable$geneSymbol[match(topN, geneTable$geneID)])
print(p1)
dev.off()
However, it doesn't work for Clustered_DotPlot (the label is not changed):
pdf(paste0(outdir, "test2.pdf"), width = 10, height = 15)
p1 <- Clustered_DotPlot(scrna.combined, features = topN, x_lab_rotate = F) + scale_y_discrete(breaks=topN, labels=geneTable$geneSymbol[match(topN, geneTable$geneID)])
print(p1)
dev.off()
Thank you for helping.
sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] plyr_1.8.6 gridExtra_2.3 qs_0.25.3 scCustomize_0.7.0
[5] viridis_0.6.2 viridisLite_0.4.0 forcats_0.5.1 purrr_0.3.4
[9] readr_2.1.2 tidyr_1.2.0 tibble_3.1.6 tidyverse_1.3.1
[13] cowplot_1.1.1 FlexDotPlot_0.2.1 monocle3_1.0.0 SingleCellExperiment_1.16.0
[17] SummarizedExperiment_1.24.0 GenomicRanges_1.46.1 GenomeInfoDb_1.30.1 IRanges_2.28.0
[21] S4Vectors_0.32.3 MatrixGenerics_1.6.0 matrixStats_0.61.0 Biobase_2.54.0
[25] BiocGenerics_0.40.0 scry_1.6.0 SeuratWrappers_0.3.0 glmpca_0.2.0
[29] reticulate_1.24 SoupX_1.5.2 stringr_1.4.0 ggplot2_3.3.5
[33] patchwork_1.1.1 SeuratObject_4.0.4 Seurat_4.1.0 dplyr_1.0.8
loaded via a namespace (and not attached):
[1] ggprism_1.0.3.9000 scattermore_0.8 R.methodsS3_1.8.1 ragg_1.2.2 bit64_4.0.5
[6] knitr_1.37 multcomp_1.4-18 irlba_2.3.5 sisal_0.48 DelayedArray_0.20.0
[11] R.utils_2.11.0 data.table_1.14.2 rpart_4.1.16 doParallel_1.0.17 RCurl_1.98-1.6
[16] generics_0.1.2 metap_1.8 ScaledMatrix_1.2.0 TH.data_1.1-0 RApiSerialize_0.1.0
[21] RANN_2.6.1 future_1.24.0 bit_4.0.4 tzdb_0.2.0 mutoss_0.1-12
[26] spatstat.data_2.1-2 xml2_1.3.3 lubridate_1.8.0 httpuv_1.6.5 assertthat_0.2.1
[31] xfun_0.30 hms_1.1.1 jquerylib_0.1.4 promises_1.2.0.1 fansi_1.0.2
[36] dendextend_1.15.2 dbplyr_2.1.1 readxl_1.3.1 tmvnsim_1.0-2 igraph_1.2.11
[41] DBI_1.1.2 htmlwidgets_1.5.4 spatstat.geom_2.3-2 paletteer_1.4.0 ellipsis_0.3.2
[46] RSpectra_0.16-0 ggpubr_0.4.0 backports_1.4.1 FactoMineR_2.4 prismatic_1.1.0
[51] grImport2_0.2-0 RcppParallel_5.1.5 deldir_1.0-6 vctrs_0.3.8 remotes_2.4.2
[56] ROCR_1.0-11 abind_1.4-5 withr_2.5.0 ggforce_0.3.3 sctransform_0.3.3
[61] dittoSeq_1.7.0 mnormt_2.0.2 goftest_1.2-3 cluster_2.1.2 lazyeval_0.2.2
[66] crayon_1.5.0 hdf5r_1.3.5 labeling_0.4.2 pkgconfig_2.0.3 qqconf_1.2.1
[71] tweenr_1.0.2 nlme_3.1-155 vipor_0.4.5 rlang_1.0.2 globals_0.14.0
[76] lifecycle_1.0.1 miniUI_0.1.1.1 sandwich_3.0-1 colourpicker_1.1.1 mathjaxr_1.6-0
[81] modelr_0.1.8 rsvd_1.0.5 cellranger_1.1.0 polyclip_1.10-0 lmtest_0.9-39
[86] Matrix_1.4-0 carData_3.0-5 boot_1.3-28 zoo_1.8-9 reprex_2.0.1
[91] base64enc_0.1-3 beeswarm_0.4.0 ggridges_0.5.3 GlobalOptions_0.1.2 pheatmap_1.0.12
[96] rjson_0.2.21 stringfish_0.15.5 png_0.1-7 bitops_1.0-7 shinydashboard_0.7.2
[101] R.oo_1.24.0 KernSmooth_2.23-20 shape_1.4.6 parallelly_1.30.0 spatstat.random_2.1-0
[106] jpeg_0.1-9 rstatix_0.7.0 ggsignif_0.6.3 beachmat_2.10.0 scales_1.1.1
[111] leaps_3.1 magrittr_2.0.2 ica_1.0-2 zlibbioc_1.40.0 compiler_4.1.2
[116] RColorBrewer_1.1-2 clue_0.3-60 plotrix_3.8-2 fitdistrplus_1.1-6 snakecase_0.11.0
[121] cli_3.2.0 XVector_0.34.0 listenv_0.8.0 pbapply_1.5-0 MASS_7.3-55
[126] mgcv_1.8-39 tidyselect_1.1.2 stringi_1.7.6 textshaping_0.3.6 BiocSingular_1.10.0
[131] ggrepel_0.9.1 grid_4.1.2 sass_0.4.0 tools_4.1.2 future.apply_1.8.1
[136] parallel_4.1.2 circlize_0.4.14 rstudioapi_0.13 foreach_1.5.2 janitor_2.1.0
[141] scatterplot3d_0.3-41 farver_2.1.0 Rtsne_0.15 digest_0.6.29 BiocManager_1.30.16
[146] shiny_1.7.1 Rcpp_1.0.8 car_3.0-12 broom_0.7.12 later_1.3.0
[151] RcppAnnoy_0.0.19 shinyWidgets_0.6.4 httr_1.4.2 ggdendro_0.1.23 ComplexHeatmap_2.10.0
[156] Rdpack_2.1.4 colorspace_2.0-3 rvest_1.0.2 XML_3.99-0.9 fs_1.5.2
[161] tensor_1.5 splines_4.1.2 uwot_0.1.11 sn_2.0.1 rematch2_2.1.2
[166] spatstat.utils_2.3-0 multtest_2.50.0 systemfonts_1.0.4 plotly_4.10.0 xtable_1.8-4
[171] jsonlite_1.8.0 flashClust_1.01-2 R6_2.5.1 TFisher_0.2.0 pillar_1.7.0
[176] htmltools_0.5.2 mime_0.12 glue_1.6.2 fastmap_1.1.0 DT_0.21
[181] BiocParallel_1.28.3 codetools_0.2-18 mvtnorm_1.1-3 utf8_1.2.2 lattice_0.20-45
[186] bslib_0.3.1 spatstat.sparse_2.1-0 numDeriv_2016.8-1.1 bsplus_0.1.3 ggbeeswarm_0.6.0
[191] leiden_0.3.9 colorway_0.2.0 limma_3.50.1 survival_3.3-1 munsell_0.5.0
[196] GetoptLong_1.0.5 R.matlab_3.6.2 GenomeInfoDbData_1.2.7 iterators_1.0.14 haven_2.4.3
[201] reshape2_1.4.4 gtable_0.3.0 rbibutils_2.2.7 spatstat.core_2.4-0
Hi,
I installed the version from dev branch
Clustered_DotPlot(seurat_object = GBM_myeloid_obj, features = unique(myeloid_features), k = 5)Error: number of cluster centres must lie between 1 and nrow(x)
I guess the k-means has some problem here. Maybe better to have an option to skip k-means.
Thanks again for making this!
> devtools::session_info()
─ Session info ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.1.1 (2021-08-10)
os Ubuntu 20.04.3 LTS
system x86_64, linux-gnu
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Etc/UTC
date 2022-01-03
─ Packages ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date lib source
abind 1.4-5 2016-07-21 [2] RSPM (R 4.1.0)
assertthat 0.2.1 2019-03-21 [2] RSPM (R 4.1.0)
backports 1.2.1 2020-12-09 [2] RSPM (R 4.1.0)
beeswarm 0.4.0 2021-06-01 [1] RSPM (R 4.1.0)
Biobase 2.52.0 2021-05-19 [1] Bioconductor
BiocGenerics 0.38.0 2021-05-19 [1] Bioconductor
bitops 1.0-7 2021-04-24 [2] RSPM (R 4.1.0)
broom 0.7.9 2021-07-27 [2] RSPM (R 4.1.0)
cachem 1.0.6 2021-08-19 [2] RSPM (R 4.1.0)
Cairo 1.5-12.2 2020-07-07 [1] RSPM (R 4.1.1)
callr 3.7.0 2021-04-20 [2] RSPM (R 4.1.0)
car 3.0-12 2021-11-06 [1] RSPM (R 4.1.0)
carData 3.0-4 2020-05-22 [1] RSPM (R 4.1.0)
cellranger 1.1.0 2016-07-27 [2] RSPM (R 4.1.0)
circlize 0.4.13 2021-06-09 [1] RSPM (R 4.1.0)
cli 3.0.1 2021-07-17 [2] RSPM (R 4.1.0)
clue 0.3-60 2021-10-11 [1] RSPM (R 4.1.0)
cluster 2.1.2 2021-04-17 [3] CRAN (R 4.1.1)
codetools 0.2-18 2020-11-04 [3] CRAN (R 4.1.1)
colorspace 2.0-2 2021-06-24 [2] RSPM (R 4.1.0)
colorway 0.2.0 2021-11-19 [1] Github (hypercompetent/colorway@8ba8f00)
ComplexHeatmap 2.8.0 2021-05-19 [1] Bioconductor
cowplot 1.1.1 2020-12-30 [2] RSPM (R 4.1.0)
crayon 1.4.1 2021-02-08 [2] RSPM (R 4.1.0)
curl 4.3.2 2021-06-23 [2] RSPM (R 4.1.0)
data.table 1.14.2 2021-09-27 [2] RSPM (R 4.1.0)
DBI 1.1.1 2021-01-15 [2] RSPM (R 4.1.0)
dbplyr 2.1.1 2021-04-06 [2] RSPM (R 4.1.0)
DelayedArray 0.18.0 2021-05-19 [1] Bioconductor
deldir 0.2-10 2021-02-16 [2] RSPM (R 4.1.0)
desc 1.3.0 2021-03-05 [2] RSPM (R 4.1.0)
devtools 2.4.2 2021-06-07 [2] RSPM (R 4.1.0)
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distributional 0.2.2 2021-02-02 [1] RSPM (R 4.1.0)
dittoSeq 1.7.0 2021-11-19 [1] Github (dtm2451/dittoSeq@b158b29)
doParallel 1.0.16 2020-10-16 [1] RSPM (R 4.1.0)
dplyr * 1.0.7 2021-06-18 [2] RSPM (R 4.1.0)
ellipsis 0.3.2 2021-04-29 [2] RSPM (R 4.1.0)
evaluate 0.14 2019-05-28 [2] RSPM (R 4.1.0)
fansi 0.5.0 2021-05-25 [2] RSPM (R 4.1.0)
farver 2.1.0 2021-02-28 [2] RSPM (R 4.1.0)
fastmap 1.1.0 2021-01-25 [2] RSPM (R 4.1.0)
fitdistrplus 1.1-5 2021-05-28 [2] RSPM (R 4.1.0)
forcats * 0.5.1 2021-01-27 [2] RSPM (R 4.1.0)
foreach 1.5.1 2020-10-15 [1] RSPM (R 4.1.0)
fs 1.5.0 2020-07-31 [2] RSPM (R 4.1.0)
future 1.22.1 2021-08-25 [2] RSPM (R 4.1.0)
future.apply 1.8.1 2021-08-10 [2] RSPM (R 4.1.0)
generics 0.1.0 2020-10-31 [2] RSPM (R 4.1.0)
GenomeInfoDb 1.28.4 2021-09-05 [1] Bioconductor
GenomeInfoDbData 1.2.6 2021-11-19 [1] Bioconductor
GenomicRanges 1.44.0 2021-05-19 [1] Bioconductor
GetoptLong 1.0.5 2020-12-15 [1] RSPM (R 4.1.0)
ggbeeswarm 0.6.0 2017-08-07 [1] RSPM (R 4.1.0)
ggdist 3.0.1 2021-11-30 [1] RSPM (R 4.1.0)
ggplot2 * 3.3.5 2021-06-25 [2] RSPM (R 4.1.0)
ggprism 1.0.3.9000 2021-11-19 [1] Github (csdaw/ggprism@e21c3ee)
ggpubr 0.4.0 2020-06-27 [1] RSPM (R 4.1.0)
ggrastr 1.0.1 2021-12-08 [1] RSPM (R 4.1.1)
ggrepel 0.9.1 2021-01-15 [2] RSPM (R 4.1.0)
ggridges 0.5.3 2021-01-08 [2] RSPM (R 4.1.0)
ggsignif 0.6.3 2021-09-09 [1] RSPM (R 4.1.0)
GlobalOptions 0.1.2 2020-06-10 [1] RSPM (R 4.1.0)
globals 0.14.0 2020-11-22 [2] RSPM (R 4.1.0)
glue 1.4.2 2020-08-27 [2] RSPM (R 4.1.0)
goftest 1.2-2 2019-12-02 [2] RSPM (R 4.1.0)
gridExtra 2.3 2017-09-09 [2] RSPM (R 4.1.0)
gtable 0.3.0 2019-03-25 [2] RSPM (R 4.1.0)
harmony * 0.1.0 2021-06-02 [1] RSPM (R 4.1.0)
haven 2.4.3 2021-08-04 [2] RSPM (R 4.1.0)
here * 1.0.1 2020-12-13 [2] RSPM (R 4.1.0)
hms 1.1.1 2021-09-26 [2] RSPM (R 4.1.0)
htmltools 0.5.2 2021-08-25 [2] RSPM (R 4.1.0)
htmlwidgets 1.5.4 2021-09-08 [2] RSPM (R 4.1.0)
httpuv 1.6.3 2021-09-09 [2] RSPM (R 4.1.0)
httr 1.4.2 2020-07-20 [2] RSPM (R 4.1.0)
ica 1.0-2 2018-05-24 [2] RSPM (R 4.1.0)
igraph 1.2.6 2020-10-06 [2] RSPM (R 4.1.0)
IRanges 2.26.0 2021-05-19 [1] Bioconductor
irlba 2.3.3 2019-02-05 [2] RSPM (R 4.1.0)
iterators 1.0.13 2020-10-15 [1] RSPM (R 4.1.0)
janitor 2.1.0 2021-01-05 [1] RSPM (R 4.1.0)
jsonlite 1.7.2 2020-12-09 [2] RSPM (R 4.1.0)
KernSmooth 2.23-20 2021-05-03 [3] CRAN (R 4.1.1)
knitr 1.34 2021-09-09 [2] RSPM (R 4.1.0)
labeling 0.4.2 2020-10-20 [2] RSPM (R 4.1.0)
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lazyeval 0.2.2 2019-03-15 [2] RSPM (R 4.1.0)
leiden 0.3.9 2021-07-27 [2] RSPM (R 4.1.0)
lifecycle 1.0.1 2021-09-24 [2] RSPM (R 4.1.0)
listenv 0.8.0 2019-12-05 [2] RSPM (R 4.1.0)
lmtest 0.9-38 2020-09-09 [2] RSPM (R 4.1.0)
lubridate 1.7.10 2021-02-26 [2] RSPM (R 4.1.0)
magrittr 2.0.1 2020-11-17 [2] RSPM (R 4.1.0)
MASS 7.3-54 2021-05-03 [3] CRAN (R 4.1.1)
Matrix 1.3-4 2021-06-01 [3] CRAN (R 4.1.1)
MatrixGenerics 1.4.3 2021-08-26 [1] Bioconductor
matrixStats 0.61.0 2021-09-17 [2] RSPM (R 4.1.0)
memoise 2.0.0 2021-01-26 [2] RSPM (R 4.1.0)
mgcv 1.8-37 2021-09-23 [3] RSPM (R 4.1.0)
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miniUI 0.1.1.1 2018-05-18 [2] RSPM (R 4.1.0)
modelr 0.1.8 2020-05-19 [2] RSPM (R 4.1.0)
munsell 0.5.0 2018-06-12 [2] RSPM (R 4.1.0)
nlme 3.1-153 2021-09-07 [3] RSPM (R 4.1.0)
paletteer 1.4.0 2021-07-20 [1] RSPM (R 4.1.0)
parallelly 1.28.1 2021-09-09 [2] RSPM (R 4.1.0)
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pbapply 1.5-0 2021-09-16 [2] RSPM (R 4.1.0)
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pillar 1.6.3 2021-09-26 [2] RSPM (R 4.1.0)
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plyr 1.8.6 2020-03-03 [2] RSPM (R 4.1.0)
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Polychrome 1.3.1 2021-07-16 [1] RSPM (R 4.1.0)
polyclip 1.10-0 2019-03-14 [2] RSPM (R 4.1.0)
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purrr * 0.3.4 2020-04-17 [2] RSPM (R 4.1.0)
R6 2.5.1 2021-08-19 [2] RSPM (R 4.1.0)
RANN 2.6.1 2019-01-08 [2] RSPM (R 4.1.0)
RColorBrewer 1.1-2 2014-12-07 [2] RSPM (R 4.1.0)
Rcpp * 1.0.7 2021-07-07 [2] RSPM (R 4.1.0)
RcppAnnoy 0.0.19 2021-07-30 [2] RSPM (R 4.1.0)
RCurl 1.98-1.5 2021-09-17 [1] RSPM (R 4.1.0)
readr * 2.0.2 2021-09-27 [2] RSPM (R 4.1.0)
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remotes 2.4.0 2021-06-02 [2] RSPM (R 4.1.0)
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reshape2 1.4.4 2020-04-09 [2] RSPM (R 4.1.0)
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rpart 4.1-15 2019-04-12 [3] CRAN (R 4.1.1)
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rvest 1.0.1 2021-07-26 [2] RSPM (R 4.1.0)
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sessioninfo 1.1.1 2018-11-05 [2] RSPM (R 4.1.0)
Seurat * 4.0.6 2021-12-16 [1] RSPM (R 4.1.0)
SeuratObject * 4.0.4 2021-11-23 [1] RSPM (R 4.1.0)
shape 1.4.6 2021-05-19 [1] RSPM (R 4.1.0)
shiny 1.7.0 2021-09-22 [2] RSPM (R 4.1.0)
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snakecase 0.11.0 2019-05-25 [1] RSPM (R 4.1.0)
spatstat.core 2.3-0 2021-07-16 [2] RSPM (R 4.1.0)
spatstat.data 2.1-0 2021-03-21 [2] RSPM (R 4.1.0)
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spatstat.sparse 2.0-0 2021-03-16 [2] RSPM (R 4.1.0)
spatstat.utils 2.2-0 2021-06-14 [2] RSPM (R 4.1.0)
stringi 1.7.4 2021-08-25 [2] RSPM (R 4.1.0)
stringr * 1.4.0 2019-02-10 [2] RSPM (R 4.1.0)
SummarizedExperiment 1.22.0 2021-05-19 [1] Bioconductor
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tensor 1.5 2012-05-05 [2] RSPM (R 4.1.0)
testthat 3.0.4 2021-07-01 [2] RSPM (R 4.1.0)
tibble * 3.1.4 2021-08-25 [2] RSPM (R 4.1.0)
tidyr * 1.1.4 2021-09-27 [2] RSPM (R 4.1.0)
tidyselect 1.1.1 2021-04-30 [2] RSPM (R 4.1.0)
tidyverse * 1.3.1 2021-04-15 [2] RSPM (R 4.1.0)
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usethis 2.0.1 2021-02-10 [2] RSPM (R 4.1.0)
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vctrs 0.3.8 2021-04-29 [2] RSPM (R 4.1.0)
vipor 0.4.5 2017-03-22 [1] RSPM (R 4.1.0)
viridis 0.6.2 2021-10-13 [1] RSPM (R 4.1.0)
viridisLite 0.4.0 2021-04-13 [2] RSPM (R 4.1.0)
withr 2.4.2 2021-04-18 [2] RSPM (R 4.1.0)
xfun 0.26 2021-09-14 [2] RSPM (R 4.1.0)
xml2 1.3.2 2020-04-23 [2] RSPM (R 4.1.0)
xtable 1.8-4 2019-04-21 [2] RSPM (R 4.1.0)
XVector 0.32.0 2021-05-19 [1] Bioconductor
yaml 2.2.1 2020-02-01 [2] RSPM (R 4.1.0)
zlibbioc 1.38.0 2021-05-19 [1] Bioconductor
zoo 1.8-9 2021-03-09 [2] RSPM (R 4.1.0)
[1] /home/rstudio/R/x86_64-pc-linux-gnu-library/4.1-3.13
[2] /usr/local/lib/R/site-library
[3] /usr/local/lib/R/libraryHey,
sorry for already another feature request, but here it comes: When splitting DimPlots using split.by the number of levels of the grouping variable for group.by gets reduced to only the levels that are actually in the cells being plotted in every split. This is very distracting for example when u split a clustering result over many subplots and the colors for the clusters change in every subplot due to some of the clusters being specific for one of the covariates split by.
Thanks a lot,
Niko
Cell bender diff example
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