powellgenomicslab / nebulosa Goto Github PK

Dear Nebulosa group:
Thanks for your wonderful work in single cell visualization!
I've found that for well expressed markers, there's great performance.
But for some obviously wrong combinations, it will show a bad result,because of the joint density value.
What is the best threshold? Can I adjust the maximum value manually?

plot_density(data_combined, "EPCAM") Error in as.vector(x, "character") :
cannot coerce type 'environment' to vector of type 'character'

Hi, I have a question I can only get plot_density pictures, but how to get the feature data matrix the plot_density plots for?

Hi there,

Thanks for a great tool.

I plot the joint density of a vector of genes that were scored using AddModuleScore.

When I plotted this score for each cell using FeaturePlot and plot_density. The results are entirely opposite of each other. Would you mind helping me to correct this?

Thank you again.
With FeaturePlot:

With plot_density:

I get this error while plotting densities-
Error in scCustomize::Plot_Density_Custom():
! Due to error in Nebulosa package and ggplot2 v3.5.0 Plot_Density_Custom functionality is currently
restricted to ggplot v3.4.4 or lower.
Run rlang::last_trace() to see where the error occurred.

I assume the Nebulosa package is not updated with latest version of ggplot2?

This seems to be new:

R> library(Nebulosa)
Loading required package: patchwork
Registered S3 method overwritten by 'spatstat.geom':
  method     from
  print.boxx cli 
Error: package or namespace load failed for 'Nebulosa':
 object 'markvario' is not exported by 'namespace:spatstat'

It can be overcome by installing an old version of spatstat, which is (perhaps obviously) not a sustainable approach.

Hi,
Thanks for this very handy tool for cluster exploration. It would be most useful if a "split.by" function could be added to Nebulosa, with behaviour similar to Seurat's split.by options for featureplot & dimplot, where plots can be partitioned by the factors/levels that are provided.
ggplot2 facet_wrap() will take plot_density output and split by factor:
plot_density(someSeuratObject, "CD4") + facet_wrap(someSeuratObject$orig.ident)
but as Nebulosa density is calculated feature-wise for all cells in the object, the Nebulosa viridus overlay is duplicated and overlayed on each facet.
So, something like:
plot_density(someSeuratObject, "CD4", split.by = "orig.ident") would be nice, with densities calculated for each "orig.ident" and overlayed accordingly.
This would be a really nice addition and provide a naming convention from Seurat.

Dear Nebulosa developers,

Nice tool. I work with Nebulosa_1.8.0. One suggestion for improvement: If you run the function plot_density on a gene that has only zero counts, it currently throws a rather cryptic error and doesn't produce any plot:

Error in linbin2D.ks(x, gpoints[[1]], gpoints[[2]], w = w) :
NA/NaN/Inf in foreign function call (arg 10)
In addition: Warning message:
In ks.defaults(x = x, w = w, binned = binned, bgridsize = bgridsize, :
Weights don't sum to sample size - they have been scaled accordingly

Instead, the function could check the read counts and in case they are all zero generate a simple plot with all dots of color = zero counts omitting the density estimation in this case.

Background: I want to use Nebulosa in a script that runs automatically over genes and it may happen in a few cases that all their read counts are zero.

Will you also develop a python implementation (compatible with Scanpy)?

Hi ,
Thanks to develop the great tools to visulize the scRNA data.
I hope the plot_density function can add the split_by parameter in it to display different group data.
Besides, i hope the plot_density's legend can set the real value not the converted value ?
Hope the package best,
hanhuihong

I've used plot_density several times but today I ran into this error and couldn't find more details about it. Is it possible related to the recent Seurat update?

library(Nebulosa)
Loading required package: ggplot2
Loading required package: patchwork
data <- SeuratObject::pbmc_small
plot_density(data,"CD3E")
Error in as.vector(x, "character") :
cannot coerce type 'environment' to vector of type 'character'
sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3.1

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] Nebulosa_1.12.0 patchwork_1.2.0 ggplot2_3.5.0

loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0 jsonlite_1.8.8
[4] magrittr_2.0.3 spatstat.utils_3.0-4 farver_2.1.1
[7] zlibbioc_1.48.0 vctrs_0.6.5 ROCR_1.0-11
[10] spatstat.explore_3.2-6 RCurl_1.98-1.14 S4Arrays_1.2.0
[13] htmltools_0.5.7 SparseArray_1.2.3 pracma_2.4.4
[16] sctransform_0.4.1 parallelly_1.37.0 KernSmooth_2.23-22
[19] htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9
[22] plotly_4.10.4 zoo_1.8-12 igraph_2.0.2
[25] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
[28] Matrix_1.6-5 R6_2.5.1 fastmap_1.1.1
[31] GenomeInfoDbData_1.2.11 MatrixGenerics_1.14.0 fitdistrplus_1.1-11
[34] future_1.33.1 shiny_1.8.0 digest_0.6.34
[37] colorspace_2.1-0 S4Vectors_0.40.2 Seurat_5.0.1
[40] tensor_1.5 RSpectra_0.16-1 irlba_2.3.5.1
[43] GenomicRanges_1.54.1 labeling_0.4.3 progressr_0.14.0
[46] fansi_1.0.6 spatstat.sparse_3.0-3 httr_1.4.7
[49] polyclip_1.10-6 abind_1.4-5 compiler_4.3.2
[52] withr_3.0.0 fastDummies_1.7.3 MASS_7.3-60.0.1
[55] DelayedArray_0.28.0 tools_4.3.2 lmtest_0.9-40
[58] httpuv_1.6.14 future.apply_1.11.1 goftest_1.2-3
[61] glue_1.7.0 nlme_3.1-164 promises_1.2.1
[64] grid_4.3.2 Rtsne_0.17 cluster_2.1.6
[67] reshape2_1.4.4 generics_0.1.3 gtable_0.3.4
[70] spatstat.data_3.0-4 tidyr_1.3.1 data.table_1.15.0
[73] sp_2.1-3 utf8_1.2.4 XVector_0.42.0
[76] BiocGenerics_0.48.1 spatstat.geom_3.2-8 RcppAnnoy_0.0.22
[79] ggrepel_0.9.5 RANN_2.6.1 pillar_1.9.0
[82] stringr_1.5.1 spam_2.10-0 RcppHNSW_0.6.0
[85] later_1.3.2 splines_4.3.2 dplyr_1.1.4
[88] lattice_0.22-5 survival_3.5-8 deldir_2.0-2
[91] ks_1.14.2 tidyselect_1.2.0 SingleCellExperiment_1.24.0
[94] miniUI_0.1.1.1 pbapply_1.7-2 gridExtra_2.3
[97] IRanges_2.36.0 SummarizedExperiment_1.32.0 scattermore_1.2
[100] stats4_4.3.2 Biobase_2.62.0 matrixStats_1.2.0
[103] stringi_1.8.3 lazyeval_0.2.2 codetools_0.2-19
[106] tibble_3.2.1 cli_3.6.2 uwot_0.1.16
[109] xtable_1.8-4 reticulate_1.35.0 munsell_0.5.0
[112] Rcpp_1.0.12 GenomeInfoDb_1.38.5 globals_0.16.2
[115] spatstat.random_3.2-2 png_0.1-8 parallel_4.3.2
[118] ellipsis_0.3.2 mclust_6.1 dotCall64_1.1-1
[121] bitops_1.0-7 listenv_0.9.1 mvtnorm_1.2-4
[124] viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6
[127] crayon_1.5.2 SeuratObject_5.0.1 leiden_0.4.3.1
[130] purrr_1.0.2 rlang_1.1.3 cowplot_1.1.3

Hello,

Thank you for publishing this tool! I've really enjoyed how easily this package can visualize feature expression based on kernel density estimation.

Could you add a function to layer the nebulosa and clustering visualizations? While this can be accomplished using ggplot2, the code tends to be a little clunky (definitely my fault) and not readily scalable when trying to annotate datasets that are large and/or relatively complex (in that multiple cell types are present == many clusters). Having a function that easily generates this layered visualization would help cut down ggplot2 troubleshooting and better align the nebulosa and clustering results.

Thank you for your time,
Marc

Hi Jose,

Thank you for creating this wonderful new package! I was wondering if it could be used to visualize the marker gene and antibody expression data from CITE-seq simultaneously, just like the FeaturePlot function in Seurat? I tried to read the source code but was not sure how plot_density() can search both RNA and ADT assay for expression data at the same time.

Thanks,
Lei

I am opening this issue as a notification because Nebulosa is listed here as a package that relies (depends/imports/suggests) on Seurat. As you may know, we recently released Seurat v5 as a beta in March of this year, with new updates for spatial, multimodal, and massively scalable analysis. For more information on updates and improvements, check out our website https://satijalab.org/seurat/.
We are now preparing to release Seurat v5 to CRAN, and plan to submit it on October 23rd. While we have tried our best to keep things backward-compatible, it is possible that updates to Seurat and SeuratObject might break your existing functionality or tests. We wanted to reach out before the new version is on CRAN, so that there's time to report issues/incompatibilities and prepare you for any changes in your code base that might be necessary.

We apologize for any disruption or inconvenience, but hope that the improvements to Seurat v5 will benefit your users going forward.
To test the upcoming release, you can install Seurat from the seurat5 branch using the instructions available on this page: https://satijalab.org/seurat/articles/install.

Thank you!
Seurat v5 team

Hi,

I tried to use plot_density function but I didn't get any plot or error.

Code:

`p4 <- plot_density(SeuOs, c("Il21r", "Il2rb"), joint = TRUE)

p4 + plot_layout(ncol = 1)
SeuOs
An object of class Seurat
31061 features across 51442 samples within 2 assays
Active assay: RNA (31053 features, 2000 variable features)
3 layers present: counts, data, scale.data
1 other assay present: HTO
3 dimensional reductions calculated: pca, tsne, umap`

What do you suspect?

Thank you,
Aastha

I have recently been using Nebulosa to show the density of gene set enrichment module scores.
Although it doesn't appear the tool was designed specifically for this, when given module scores the tool returns a useful map often more obviously highlighting regions of enrichment (when compared to FeaturesPlots in Seurat).

What are the suggestions for using the Nebulosa tool for module scores?

data <- SeuratObject::pbmc_small
plot_density(data,"CD3E")
Error in plot_density(data, "CD3E")

sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
[1] LC_CTYPE=zh_CN.UTF-8 LC_NUMERIC=C LC_TIME=zh_CN.UTF-8 LC_COLLATE=zh_CN.UTF-8 LC_MONETARY=zh_CN.UTF-8
[6] LC_MESSAGES=zh_CN.UTF-8 LC_PAPER=zh_CN.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base

other attached packages:
[1] Nebulosa_1.4.0 SeuratObject_4.0.4 Seurat_4.1.0 patchwork_1.1.1 ggplot2_3.3.5
[6] clusterProfiler_4.3.2.991 RcisTarget_1.10.0 DOSE_3.16.0 SummarizedExperiment_1.20.0 Biobase_2.50.0
[11] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7 IRanges_2.24.1 S4Vectors_0.28.1 BiocGenerics_0.36.1
[16] MatrixGenerics_1.2.1 matrixStats_0.61.0

loaded via a namespace (and not attached):
[1] utf8_1.2.2 ks_1.13.3 reticulate_1.24 R.utils_2.11.0 tidyselect_1.1.1
[6] RSQLite_2.2.9 AnnotationDbi_1.52.0 htmlwidgets_1.5.4 grid_4.0.0 BiocParallel_1.24.1
[11] Rtsne_0.15 devtools_2.4.3 scatterpie_0.1.7 munsell_0.5.0 codetools_0.2-16
[16] ica_1.0-2 future_1.23.0 miniUI_0.1.1.1 withr_2.4.3 colorspace_2.0-2
[21] GOSemSim_2.16.1 rstudioapi_0.13 SingleCellExperiment_1.12.0 ROCR_1.0-11 tensor_1.5
[26] listenv_0.8.0 labeling_0.4.2 GenomeInfoDbData_1.2.4 polyclip_1.10-0 bit64_4.0.5
[31] farver_2.1.0 rprojroot_2.0.2 downloader_0.4 parallelly_1.30.0 vctrs_0.3.8
[36] generics_0.1.2 R6_2.5.1 graphlayouts_0.8.0 hdf5r_1.3.5 bitops_1.0-7
[41] spatstat.utils_2.3-0 cachem_1.0.6 fgsea_1.16.0 DelayedArray_0.16.3 promises_1.2.0.1
[46] scales_1.1.1 ggraph_2.0.5 enrichplot_1.10.2 gtable_0.3.0 globals_0.14.0
[51] processx_3.5.2 goftest_1.2-3 tidygraph_1.2.0 rlang_1.0.0 splines_4.0.0
[56] lazyeval_0.2.2 spatstat.geom_2.3-1 BiocManager_1.30.16 reshape2_1.4.4 abind_1.4-5
[61] httpuv_1.6.5 SeuratDisk_0.0.0.9019 qvalue_2.22.0 usethis_2.1.5 tools_4.0.0
[66] feather_0.3.5 ellipsis_0.3.2 spatstat.core_2.3-2 RColorBrewer_1.1-2 sessioninfo_1.2.2
[71] ggridges_0.5.3 Rcpp_1.0.8 plyr_1.8.6 zlibbioc_1.36.0 purrr_0.3.4
[76] RCurl_1.98-1.5 ps_1.6.0 prettyunits_1.1.1 rpart_4.1-15 deldir_1.0-6
[81] pbapply_1.5-0 viridis_0.6.2 cowplot_1.1.1 zoo_1.8-9 ggrepel_0.9.1
[86] cluster_2.1.0 fs_1.5.2 magrittr_2.0.2 data.table_1.14.2 scattermore_0.7
[91] DO.db_2.9 lmtest_0.9-39 RANN_2.6.1 mvtnorm_1.1-3 fitdistrplus_1.1-6
[96] pkgload_1.2.4 hms_1.1.1 mime_0.12 xtable_1.8-4 XML_3.99-0.8
[101] mclust_5.4.9 AUCell_1.12.0 gridExtra_2.3 testthat_3.1.2 compiler_4.0.0
[106] tibble_3.1.6 KernSmooth_2.23-16 crayon_1.4.2 shadowtext_0.1.1 R.oo_1.24.0
[111] htmltools_0.5.2 ggfun_0.0.5 mgcv_1.8-31 later_1.3.0 tidyr_1.2.0
[116] DBI_1.1.2 tweenr_1.0.2 MASS_7.3-51.5 Matrix_1.4-0 brio_1.1.3
[121] cli_3.1.1 R.methodsS3_1.8.1 igraph_1.2.11 pkgconfig_2.0.3 plotly_4.10.0
[126] spatstat.sparse_2.1-0 annotate_1.68.0 XVector_0.30.0 yulab.utils_0.0.4 stringr_1.4.0
[131] callr_3.7.0 digest_0.6.29 pracma_2.3.6 sctransform_0.3.3 RcppAnnoy_0.0.19
[136] graph_1.68.0 spatstat.data_2.1-2 leiden_0.3.9 fastmatch_1.1-3 uwot_0.1.11
[141] GSEABase_1.52.1 curl_4.3.2 shiny_1.7.1 lifecycle_1.0.1 nlme_3.1-147
[146] jsonlite_1.7.3 desc_1.4.0 viridisLite_0.4.0 fansi_1.0.2 pillar_1.6.5
[151] lattice_0.20-41 fastmap_1.1.0 httr_1.4.2 pkgbuild_1.3.1 survival_3.1-12
[156] GO.db_3.12.1 remotes_2.4.2 glue_1.6.1 png_0.1-7 bit_4.0.4
[161] ggforce_0.3.3 stringi_1.7.6 blob_1.2.2 memoise_2.0.1 dplyr_1.0.7
[166] irlba_2.3.5 future.apply_1.8.1

Hello,

Thanks so much for publishing this tool, very cool way to look at gene expression!

Is there a way to look at metadata instead? I have a large integrated seurat object and I would like to create a figure with the density plot indicating where the cells from each of the samples/experiments falls on the UMAP.

Thanks!

Carly

Hello! From this tutorial, the steps start with creating the seurat object, where the normalization step is via SCTransform.

Can I use a seurat object that is already normalized, clustered, cell-identified, and fully processed? When I skip the pre-processing and go directly to use plot_density(), the plot shows a PCA plot, whereas if I use the FeaturePlot, it shows a UMAP plot.

How can I make it so that the already-processed seurat object will show the density plot with already-clustered UMAP visualization?

https://bioconductor.org/packages/devel/bioc/vignettes/Nebulosa/inst/doc/nebulosa_seurat.html#

Hi

Thanks to develop the great tools to visulize the scRNA data.

Is there a way to visualize CITE-seq data and mRNA data with Nebulosa? I did not find it.

best

Felipe

Hello,
I was wondering whether it is possible to get "Nebulosa" installed in R version 3.6.3. thanks

I want to generate a Kernel Density plot with my scRNAseq data for a particular list of genes. The vignette of this package shows you an example plotting the join density of 2 genes. I have been checking the documentation and it doesn't seem that you have a limit of genes to plot.

However, when I try to add more genes (<2)... I am having problems (it doesn't generate anything).

Example:

library(Seurat)
library(Nebulosa)
data <- SeuratObject::pbmc_small
data <- RunPCA(data)
data <- RunUMAP(data, dims = 1:10)
data <- FindNeighbors(data, dims = 1:10)
data <- FindClusters(data)

# it works
p4 <- plot_density(data, c("CD8A", "RUFY1"), joint = TRUE)
p4 + plot_layout(ncol = 1)

If I add more genes,, it generates an empty plot (it doesn't give me any error)

p4 <- plot_density(data, c("CD8A", "CCR7", "VDAC3", "TREML1", "CA2", "SDPR", "PPBP", "IGLL5", "GNLY", "CD1C", "S100A9", "RUFY1"), joint = TRUE)
p4 + plot_layout(ncol = 1)

My environment:

• R version 4.2.2 (2022-10-31 ucrt)
• Nebulosa_1.0.1
• patchwork_1.2.0
• ggplot2_3.5.1
• SeuratObject_4.1.3
• Seurat_4.3.0

Could anybody help me, please? Any feedback will be well received.

Thanks very much in advance!

Hello,
I'm using Seurat v5 with an RDS object obtained by using the BBKNN integration protocol. The Plot_density function does not generate a plot for more than one gene, and for a single gene, it produces a t-SNE projection instead of a UMAP projection. Please advise on how to resolve this issue? Thank you for any help!