make_azimuth_QC_heatmaps() compatibility with BPCells

[koetjen]

> obj <- PrepLabel(obj, "azimuth_label", "azimuth_fine_filtered", cutoff = 20)
> plots <- make_azimuth_QC_heatmaps(obj, final_name = "azimuth_fine_filtered")
Calculating cluster Other
Warning: No DE genes identified
Warning: The following tests were not performed: 
Warning: When testing Other versus all:
	Cells in one or both identity groups are not present in the data requested
Error in processing Adipocyte_1 : Must group by variables found in `.data`.
Calculating cluster Venous EC
Calculating cluster Other
Calculating cluster Arterial EC
Calculating cluster Capillary EC
Calculating cluster Lymphatic EC
Warning: Different features in new layer data than already exists for scale.data
Error in processing Endothelial cell_1 : cannot coerce class 'structure("RenameDims", package = "BPCells")' to a data.frame
Calculating cluster Other
Warning: No DE genes identified
Warning: The following tests were not performed: 
Warning: When testing Other versus all:
	Cells in one or both identity groups are not present in the data requested
Error in processing Epithelial cell_1 : Must group by variables found in `.data`.
Calculating cluster Fibroblast
Calculating cluster CFD+MGP+ fibroblast
Calculating cluster C2orf40+ fibroblast
Calculating cluster Myofibroblast
Calculating cluster IGFBP6+SFRP4+ fibroblast
Calculating cluster IGFBP6+APOD+ fibroblast
Calculating cluster Other
Calculating cluster Metallothionein+ Collagen- fibroblast
Calculating cluster THY1+ fibroblast
Calculating cluster CCL2+SFRP2+ fibroblast
Calculating cluster CD55+SEMA3C+ fibroblast
Calculating cluster GREB1+ fibroblast
Calculating cluster APOE+ fibroblast
Calculating cluster PTGDS+CXCL14+ fibroblast
Calculating cluster NR4A3 fibroblast
Error in processing Fibroblast_1 : In function [: "i" must not have duplicated values
     ▆
  1. └─AzimuthAPI::make_azimuth_QC_heatmaps(obj, final_name = "azimuth_fine_filtered")
  2.   ├─base::tryCatch(...)
  3.   │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
  4.   │   └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
  5.   │     └─base (local) doTryCatch(return(expr), name, parentenv, handler)
  6.   └─AzimuthAPI::make_QC_heatmap(...)
  7.     ├─Seurat::ScaleData(seurat_obj, features = top_markers$gene)
  8.     └─Seurat:::ScaleData.Seurat(seurat_obj, features = top_markers$gene)
  9.       ├─Seurat::ScaleData(...)
 10.       └─Seurat:::ScaleData.StdAssay(...)
 11.         ├─SeuratObject::LayerData(object = object, layer = layer, features = features)
 12.         └─SeuratObject:::LayerData.Assay5(...)
 13.           ├─...[]
 14.           └─...[]
 15.             └─BPCells:::selection_index(i, nrow(x), rownames(x))

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS Monterey 12.7.6

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0

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/Chicago
tzcode source: internal

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

other attached packages:
[1] scclusteval_0.0.0.9000 BoneMarrowMap_0.1.0 ggpubr_0.6.0 symphony_0.1.1 lubridate_1.9.4
[6] forcats_1.0.0 stringr_1.5.1 purrr_1.0.4 readr_2.1.5 tidyr_1.3.1
[11] tibble_3.2.1 tidyverse_2.0.0 BiocManager_1.30.25 AzimuthAPI_0.1.0 bonemarrowref.SeuratData_1.0.0
[16] future_1.49.0 dplyr_1.1.4 BPCells_0.3.0 patchwork_1.3.0 Azimuth_0.5.0
[21] shinyBS_0.61.1 SeuratWrappers_0.3.2 bmcite.SeuratData_0.3.0 SeuratData_0.2.2.9001 ggplot2_3.5.2
[26] Seurat_5.3.0 SeuratObject_5.1.0 sp_2.2-0

[edridgedsouza]

Similar issues with BPCells: #10

My current workaround is to simply create a subset that can be held in memory and then convert to a regular matrix:

my_subset <- subset(my_obj, subset=cluster==12)

my_subset[['RNA']]$data <- as.matrix(GetAssayData(object = my_subset, assay='RNA', layer = "data"))

my_subset <- CloudAzimuth(my_subset, assay='RNA', layer='data')

Not sure if this will fix your issue but it could potentially help with some indexing errors.