Hi there,

Great package, I really appreciate the development of this tool for better visualization and figure building of scRNAseq data.

Is there a way to add vertical lines that separate clusters in the heatmap? Currently, they all blend together and the annotation bars are the only divider, I am wondering if this can be extended throughout the heatmap?

See images below for an example provided by seurat heatmap with vertical line divider versus scillus heatmap. thanks!

Thanks for your useful project. When using plot_stat, I've noticed that the stacked barplot is fixed to the 'seurat_clusters' in the metadata. Would it be possible for an additional argument to be added so that we can use other metadata to label the stacked barplot?

Referring to these lines:

How to change fonts size for marker genes in plot_heatmap?
Thanks

Hi, thanks for the excellent package; I encounter following error and am struggling to handle this issue; are there any hints for solving this problem?

> HeatMap_plot <- plot_heatmap(
+   dataset = Sepsis_seurat, 
+   markers = Allmarkers,n=2,
+   sort_var = c("Cell_Type","Condition"),
+   anno_var = c("Cell_Type","Condition","percent.mt","S.Score","G2M.Score"),
+   anno_colors = list(
+     "Set2",# RColorBrewer palette
+     c("red","orange","yellow","purple","blue","green"), # color vector
+                      "Reds",
+     c("blue","white","red"), # Three-color gradient
+     "Greens"))
Warning in set_colors(anno_colors[[i]], length(l)) :
  Number of colors required exceeds palette capacity. RdYlBu spectrum will be used instead.
`use_raster` is automatically set to TRUE for a matrix with more than
2000 columns You can control `use_raster` argument by explicitly
setting TRUE/FALSE to it.

Set `ht_opt$message = FALSE` to turn off this message.
Error in Cairo(width, height, type = "png", file = filename, pointsize = pointsize,  : 
  Failed to create Cairo backend!

Hi,
I am trying to decrease the height of column annotation using annotation_height but this does not seem to work.

Any idea how to do this? Thanks

Thanks for this really nice package !
It is a bit annoying that plot_heatmaps uses assay = "integrated" as the default assay, It would more convenient if it defaults to assay =DefaultAssay(dataset). for ex, my seurat object does not have an integrated assay so I would have to duplicate the RNA assay and rename it as Integrated which will increase memory.
Thanks !

Hi, Looks like a great package! I just wanted to let you know I've encountered an issue with using the plot_heatmap function with a Seurat object that I've appended V(D)J data to with scRepertoire. It throws the error:

Error:
! Column name barcode must not be duplicated.
Caused by error in stop_vctrs():
! Names must be unique.
x These names are duplicated:

• "barcode" at locations 5 and 41.
  Run rlang::last_error() to see where the error occurred.

I had a look back at traceback and the plot_heat map function and confirmed that all barcodes were unique. The function works when I delete the barcode metadata completely. Let me know if you want/need any more info. Thanks!

Hello,

Is it possible to pass a list of markers through the plot_heatmap function? I have been using FindMarkers/FindConservedMarkers and would like to use your annotation options. Thank you.

-Todd

Would it be possible to display the clusters annotation (or ideally the first annotation variable) the same way as in Seurat? Clusters on top of the annotation bar with 45 degree angle.
Sorry if it's too much to ask, but this could also save space on the right of the heatmap for more annotations without overlapping them.

I really liked a lot of the plots generated with Scillus, but I couldn't seem to change the point size when using plot_scdata. This is a bit of an issue as I cannot compare how dense/sparse the clusters are across different samples. I also couldn't seem to plot continuous features like gene levels.
I am wondering if you might have solutions for my problems or if they are features that can be implemented.

Thank you

Barbara

halo anyone......

I used my seurat object to generate 1. heatmap, to analyze 2. GO and 3. GSEA using this package. For point 1 and 2, I have no issue when used codes provided in the vignette. However, for point 3, when I want to generate GSEA plot using plot_GSEA the plot did not pop up.

Any help I really appreciate in advance.

best regards,

Bugie

I am trying to generate a heatmap from the plot_heatmap function but I am running into this error of ERROR in rownames(dataset@assays$[email protected]): "Trying to get slot "scale.data" from an object of a basic class ("NULL") with no slots, although there is a slot named scale.data in my seurat object. How can I solve this issue?

Hi,
For the function plot_heatmap(), is there a way to change the size of the legend and the font size/type? Thanks!

Please have a detailed description regarding the questions about package development
Thanks for the development of the Scillus package. It is easy to use and generate elegant figures.
However, I would like to plot tsne plot, and the plot_scdata plots umap in default. In seurat, I can plot tsne with DimPlot(seu.obj,reduction="tsne",split.by="sample"). I have tried to input DimReduc="tsne" or eductions="tsne" in the plot_scdata, it showed unused argument (DimReduc = "tsne") or unused argument (reductions = "tsne"). Is there a way I can plot tsne using scillus ?

plot_stat(scRNA_int, "group_count") in sample data error.

Hi,

Thank you for the wonderful package.
I have got this plot for two conditions that I am comparing (untreated and treated). I wonder what is the order of this enrichment? It shows that different pathways are enriched in different clusters but when we are trying to look at the effects of treatment, I expect different pathways if the drug is causing any change. Any thoughts?

Thank you!

Hi,
I have a strange error when I input specific colors in anno_colors .

if : anno_colors = list(c("Set1","Set3","Set2"), "Set2",pal1),  # works fine
if:  anno_colors = list(c("Set1","Set3","Set2"),pal1, "Set2") # error: Error: elements in `col` should be named vectors.

Any suggestion?
Thanks

Hi,
It would be really nice to add a function to plot let's say proportions of tumor vs normal tissue for each cluster but after mormalizing by group count (tissue) as there is usually a bias in the number of samples for each group and/or in the number of cells.
Hope it is clear
Thanks

Hey,

This is a nice visualization package you have made. I was hoping you could update it to work with the new Seurat V5 data structure and to work with SCTransformed objects.

This is especially important because they have also changed the way integrated objects behave and one of the key features of your package was making heatmaps of integrated objects.

Hope you can update this package to work with those changes.

Thank you.

Hi, I recently found your package and love it but I ran into an error when trying to call plot_measure_dim. Example below uses the tutorial data.

> plot_measure_dim(dataset = scRNA_int, 
+          measures = c("nFeature_RNA","nCount_RNA","percent.mt","KRT14"))
Error in `plot_measure_dim()`:
! Can't subset `.data` outside of a data mask context.
Run `rlang::last_error()` to see where the error occurred.
> rlang::last_error()
<error/rlang_error>
Error in `plot_measure_dim()`:
! Can't subset `.data` outside of a data mask context.
---
Backtrace:
 1. Scillus::plot_measure_dim(...)
Run `rlang::last_trace()` to see the full context.
> rlang::last_trace()
<error/rlang_error>
Error in `plot_measure_dim()`:
! Can't subset `.data` outside of a data mask context.
---
Backtrace:
    ▆
 1. └─**Scillus**::plot_measure_dim(...)
 2.   ├─ggplot2::scale_color_viridis_c(...)
 3.   │ └─ggplot2::continuous_scale(...)
 4.   ├─**stats**::quantile(.data[[measures[i]]], probs = 0.1)
 5.   ├─<unknown>
 6.   └─**rlang**:::`[[.rlang_fake_data_pronoun`(.data, measures[i])
 7.     └─rlang:::stop_fake_data_subset(call)
 8.       └─rlang::abort(...)

Here's my sessionInfo

> sessionInfo()
R version 4.1.3 (2022-03-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.3

Matrix products: default
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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] magrittr_2.0.3     SeuratObject_4.0.4 Seurat_4.1.0       forcats_0.5.1     
 [5] stringr_1.4.0      dplyr_1.0.8        purrr_0.3.4        readr_2.1.2       
 [9] tidyr_1.2.0        tibble_3.1.6       ggplot2_3.3.5      tidyverse_1.3.1   
[13] Scillus_0.5.0     

loaded via a namespace (and not attached):
  [1] readxl_1.4.0          backports_1.4.1       circlize_0.4.14      
  [4] plyr_1.8.7            igraph_1.3.0          lazyeval_0.2.2       
  [7] splines_4.1.3         listenv_0.8.0         scattermore_0.8      
 [10] digest_0.6.29         foreach_1.5.2         htmltools_0.5.2      
 [13] formattable_0.2.1     fansi_1.0.3           tensor_1.5           
 [16] cluster_2.1.3         doParallel_1.0.17     ROCR_1.0-11          
 [19] tzdb_0.3.0            ComplexHeatmap_2.10.0 globals_0.14.0       
 [22] modelr_0.1.8          matrixStats_0.61.0    R.utils_2.11.0       
 [25] spatstat.sparse_2.1-0 colorspace_2.0-3      rvest_1.0.2          
 [28] ggrepel_0.9.1         haven_2.4.3           xfun_0.30            
 [31] crayon_1.5.1          jsonlite_1.8.0        spatstat.data_2.1-4  
 [34] survival_3.3-1        zoo_1.8-9             iterators_1.0.14     
 [37] glue_1.6.2            polyclip_1.10-0       gtable_0.3.0         
 [40] leiden_0.3.9          GetoptLong_1.0.5      future.apply_1.8.1   
 [43] shape_1.4.6           BiocGenerics_0.40.0   abind_1.4-5          
 [46] scales_1.1.1          DBI_1.1.2             spatstat.random_2.2-0
 [49] miniUI_0.1.1.1        Rcpp_1.0.8.3          viridisLite_0.4.0    
 [52] xtable_1.8-4          clue_0.3-60           reticulate_1.24      
 [55] spatstat.core_2.4-2   stats4_4.1.3          htmlwidgets_1.5.4    
 [58] httr_1.4.2            RColorBrewer_1.1-3    ellipsis_0.3.2       
 [61] ica_1.0-2             farver_2.1.0          R.methodsS3_1.8.1    
 [64] pkgconfig_2.0.3       uwot_0.1.11           dbplyr_2.1.1         
 [67] deldir_1.0-6          utf8_1.2.2            labeling_0.4.2       
 [70] tidyselect_1.1.2      rlang_1.0.2           reshape2_1.4.4       
 [73] later_1.3.0           cellranger_1.1.0      munsell_0.5.0        
 [76] tools_4.1.3           cli_3.2.0             generics_0.1.2       
 [79] broom_0.8.0           ggridges_0.5.3        evaluate_0.15        
 [82] fastmap_1.1.0         goftest_1.2-3         fs_1.5.2             
 [85] knitr_1.38            fitdistrplus_1.1-8    RANN_2.6.1           
 [88] pbapply_1.5-0         future_1.24.0         nlme_3.1-157         
 [91] mime_0.12             R.oo_1.24.0           xml2_1.3.3           
 [94] rstudioapi_0.13       compiler_4.1.3        plotly_4.10.0        
 [97] png_0.1-7             spatstat.utils_2.3-0  reprex_2.0.1         
[100] stringi_1.7.6         RSpectra_0.16-0       lattice_0.20-45      
[103] Matrix_1.4-1          vctrs_0.4.1           pillar_1.7.0         
[106] lifecycle_1.0.1       spatstat.geom_2.4-0   lmtest_0.9-40        
[109] GlobalOptions_0.1.2   RcppAnnoy_0.0.19      data.table_1.14.2    
[112] cowplot_1.1.1         irlba_2.3.5           httpuv_1.6.5         
[115] patchwork_1.1.1       R6_2.5.1              promises_1.2.0.1     
[118] KernSmooth_2.23-20    gridExtra_2.3         IRanges_2.28.0       
[121] parallelly_1.31.0     codetools_0.2-18      MASS_7.3-56          
[124] assertthat_0.2.1      rjson_0.2.21          withr_2.5.0          
[127] sctransform_0.3.3     S4Vectors_0.32.4      mgcv_1.8-40          
[130] parallel_4.1.3        hms_1.1.1             grid_4.1.3           
[133] rpart_4.1.16          rmarkdown_2.13        Rtsne_0.15           
[136] shiny_1.7.1           lubridate_1.8.0

Hi,

Thank you for such a great tool - it allows visualization of lots of interesting metadata on a heatmap.

One slightly annoying feature is that it is sometimes difficult to differentiate columns between different clusters when there is a lot of them (10+ clusters). The default of plot_heatmap starts using continuous color palettes such as RdYlBu which makes it difficult to find where one cluster starts and another ends. Moreover, since there is no number at the top of the heatmap, I have to count the columns each time I am analyzing the columns in the middle of the heatmap.

Here is an example:

Solution:
Would it be possible to add additional parameters that allow for separation of clusters with a white/black line and numbering of clusters? I am attaching an example from DoHeatmap that has those features by default:

Alternatively: could you suggest how I could modify your code to make the ComplexHeatmap object have these features?

Regards,
Matas

Hello

Using plot_heatmap I'm getting several 'too huge' errors with the PNG rastering via Cairo. I was wondering if it would be possible to add ... to the functions so arguments can be passed to underlying complex heatmap functions so things like rastering can be switched off or tweaked easily?

(not just rastering but example case! Would like to be able to tweak complexheatmap function options)

Could the helper functions also be exported to make this a tad easier?

Thank you !

I hope this package will let me display violin plots the way I want to. But I am getting a weird error with plot_measure: "Error in dataset[[group_by]]: subscript out of bounds. Here is my code:

plot_measure(dataset = oligosnew2@assays$[email protected],
measures = "Neat1", group_by = "seurat_clusters")

Hi, thank you for your excellent package and I really enjoying many downstream analysis with the package. However, I found in the GSEA plot, the -log10(Adj. p) values can take negative values which is not feasible to me. p value is always less than 1, and -log10(p) should always > 0.

Thank you for your excellent package for single cell visualization, However I encounter a feature that is not included in the package for plot_heatmap function. Is that possible to added annotation bar for genes (row annotation).

The limitation of RColorBrewer palettes to max 12 colors is kind of annoying. Do you think you might be able to create support for custom color lists or color palettes with more variations?
Or is there a workaround that I don't see?

Describe the bug
I get some genes out of the marker genes as NA

thank you!

Julio

Hi, thanks a lot for this package.

I am working with multiomic data sc-ATACseq + sc-RNAseq.

The package worked great for the RNAseq part of the data.

I am now trying to display the data from the peaks assays calculated from ATACseq data.
Using seurat function: DoHeatmap(seurat_object, features = features, assay = 'peaks', slot = 'data'), I can access the data that I want to display.

Is there a way to use these data with your function ?

Thanks