Currently granule average scores are stored as not factor:

> is.factor(combined.obj$RNA_snn_res.6_cl.av_GO.0042063)
[1] TRUE
> is.numeric(combined.obj$RNA_snn_res.6_cl.av_GO.0042063)
[1] FALSE

Describe the solution you'd like
Keep them as numeric, since we compute with them and code could be much simpler

Requires a set of plots

umaps

• Final stress classification
• Individual stress classification
• Individual stress scores

histograms

• For each score

Hi,
I'm very excited about trying gruffi as I think it could resolve some issues I've been observing with the pseudotime analysis. Unfortunately I've been following your installation and it is still unclear for me how to make the package work. I get a similar warning as in the resolved issue #2 but I don't understand what the solution actually is. I'm not a trained computational biologist, more an advanced amateur so an implied answer didn't really help me.
This is the warning I get:

devtools::load_all("/Library/Frameworks/R.framework/Versions/4.2/Resources/library/gruffi/")

ℹ Loading gruffi
Warning messages:
1: S3 method ‘plot.clust.size.distr’ was declared in NAMESPACE but not found
2: Objects listed as exports, but not present in namespace:
• AddCustomScore
• AddGOGeneList.manual
• AddGOScore
• CalcTranscriptomePercentage
• CalcTranscriptomePercentageGO
• CleanDuplicateScorenames
• CustomScoreEvaluation
• FeaturePlotSaveCustomScore
• FeaturePlotSaveGO
• FilterStressedCells
• GO_score_evaluation
• GetAllGOTerms
• GetGOTerms
• GetNamedClusteringRuns
• IntersectWithExpressed
• PasteUniqueGeneList
• PlotGoTermScores
• PlotNormAndSkew
• Shiny.GO.thresh
• UMAP.3d.cubes
• aut.res.clustering
• calc.cluster.averages.gruffi
• clUMAP.thresholding
• fix.metad.Colname.rm.trailing.1
• reassign.small.clusters
• saveData
• saveRDS.compress.in.BG
• sparse.cor
• stand_dev_skewed
• ww.convert.GO_term.2.score
• ww.convert.score.2.GO_term

The install_github functions like devtools::install_github(repo = "vertesy/Stringendo", upgrade = F) install them in the same folder "/Library/Frameworks/R.framework/Versions/4.2/Resources/library/", so I really don't know what I'm supposed to "load_all".
I'd appreciate any help. Thank you!
Michal

Hi,
I would please appreciate help installing Gruffi. I get an error message during the installation that I try to describe below. (note: I have limited R knowledge). After all packages have been installed and loaded, including also the Seurat object, I run the following code extracted from GitHub edited to include the the name of my Seurat object ('all samples.combined')

combined.obj <- Seurat.utils::SetupReductionsNtoKdimensions(obj = allsamples.combined, nPCs = 30, dimensions=1:20, reduction="umap")
ensembl <- biomaRt::useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl")
go1 <- "GO:0006096" # Glycolysis
go2 <- "GO:0034976" # ER-stress
go3 <- "GO:0042063" # Gliogenesis, negative filtering
combined.obj <- aut.res.clustering(obj = combined.obj)
granule.res.4.gruffi <- combined.obj@misc$gruffi$'optimal.granule.res'
combined.obj <- reassign.small.clusters(combined.obj, ident = granule.res.4.gruffi)
granule.res.4.gruffi <- paste0(granule.res.4.gruffi, '.reassigned')
combined.obj <- GO_score_evaluation(obj = combined.obj, GO_term = go1, save.UMAP = TRUE, new_GO_term_computation = T, clustering = granule.res.4.gruffi, plot.each.gene = F)

At this point, I get an error message:
Error in Idents<-:
! 'value' must be a factor or vector
Run rlang::last_trace() to see where the error occurred.

As suggested, I run the code to see the error:

rlang::last_trace()
<error/rlang_error>
Error in Idents<-:
! 'value' must be a factor or vector

Backtrace:
▆

1. └─gruffi::GO_score_evaluation(...)
2. ├─Seurat::Idents<-(*tmp*, value = [email protected][clustering])
3. └─SeuratObject:::Idents<-.Seurat(*tmp*, value = [email protected][clustering])
   Run rlang::last_trace(drop = FALSE) to see 1 hidden frame.

As suggested, I run the code to see the error:

rlang::last_trace(drop = FALSE)
<error/rlang_error>
Error in Idents<-:
! 'value' must be a factor or vector

Backtrace:
▆

1. └─gruffi::GO_score_evaluation(...)
2. ├─Seurat::Idents<-(*tmp*, value = [email protected][clustering])
3. └─SeuratObject:::Idents<-.Seurat(*tmp*, value = [email protected][clustering])

4. └─rlang::abort(message = "'value' must be a factor or vector")
   

I do not know how to fix the issue.
Thank you in advance.
Ivan

Additional information: The following are the open packages and I am using a saved session in which I created the Seurat object
library(pdist)
library(raster)
library(DOSE)
library(org.Hs.eg.db)
library(sparseMatrixStats)
library(biomaRt)
library(Stringendo)
library(CodeAndRoll2)
library(ReadWriter)
library(MarkdownHelpers)
library(MarkdownReports)
library(ggExpress)
library(Seurat.utils)
library(gruffi)

Hi,

I've been trying to use this on my samples. It works fine until thresholding for GO term where I got this error:

In file(filename, "r", encoding = encoding) :
cannot open file '/ui.R': No such file or directory

I had a look and I couldn't find the ui.R and server.R from the package. Can you please advise?

ensembl version is not fixed and not specified in GetGoTerms()

ensembl <<- biomaRt::useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", version="107")

@vertesy

The Gruffi tool is very useful, but I need it in a completely automatic fashion, leading to the following question. Would it be possible to set the thresholds for the three GO terms without the Shiny app (e.g., just push the button save thresholds immediately after start of the shiny app)?

Hi,
I had a question, im using the gruffi package to identify stressed vs non-stressed cells in my human iPSC DaNs 10x data.
I have read the pre-print for gruffi and i see why negative filtering was gliogenesis was implemented.
Since in my case neurogenesis or cell differentiation would be a better negative filter I included those.

However, i was wondering do i definitely need to have a negative filter?
If i decided to not use a negative filter for neurogenesis, true i might be identifying real neurons which have difference in the stressed pathways because of my treatment, but in your experience how much difference does not including a negative filter make.
Or rather how should I then interpret the results?

Best,
Devika

When showing stressed cells, split Dimplot by stressed vs no stressed to deal with overplotting.

Describe the bug
GOScoreEvaluation fails with unused argument (mirror = NULL). seems like this argument is passed on to FeaturePlot

To Reproduce

> combined.obj <- GOscoreEvaluation(obj = combined.obj, GO_term = 'GO:0006096',
+                                   save.UMAP = TRUE, new_GO_term_computation = T, 
+                                   clustering = granule.res.4.gruffi, plot.each.gene = F)
[1] "Score.GO.0006096"
[1] "Score.GO.0006096 not found. Need to call GetGOTerms(). set only.draw.plot = FALSE."
[1] "GENE SCORE WILL NOW BE SET / OVERWRITTEN"
[1] "90 gene symbols downloaded via biomaRt::getBM(): SLC2A6 LDHA HTR2A PFKL FOXK1 PGK2 INSR EIF6 PGAM1 KAT2B"
[1] "90 Gene symbols downloaded: SLC2A6 LDHA HTR2A PFKL FOXK1 PGK2 INSR EIF6 PGAM1 KAT2B"
[1] "IntersectWithExpressed()"
[1] "3 genes (of 90 ) are MISSING from the Seurat object: PRXL2C TREX1 IFNG"
[1] "Genes in GO:0006096 are saved under obj@misc$GO$ GO.0006096"
[1] "ENO1"   "MTOR"   "PRKAA2" "PGM1"   "ARNT"   "PKLR"  
[1] "AddGOScore()"
[1] "Trailing '1' in metadata column name is removed. Column name: Score.GO.0006096"
Error in Seurat::FeaturePlot(obj, features = GO.score, min.cutoff = "q05",  : 
  unused argument (mirror = NULL)
> traceback()
3: FeaturePlotSaveGO(obj = obj, GO.score = ScoreName, save.plot = save.UMAP, 
       name_desc = desc, ...)
2: PlotGoTermScores(GO = GO_term, save.UMAP = save.UMAP, obj = obj, 
       desc = description, plot.each.gene = plot.each.gene, mirror = mirror, 
       ...)
1: GOscoreEvaluation(obj = combined.obj, GO_term = go_id_glycolysis, 
       save.UMAP = TRUE, new_GO_term_computation = T, clustering = granule.res.4.gruffi, 
       plot.each.gene = F)

> sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /opt/R/4.3.2/lib/R/lib/libRblas.so 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Berlin
tzcode source: system (glibc)

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

other attached packages:
 [1] gruffi_1.2.6          MarkdownReports_4.7.0 MarkdownHelpers_1.0.7 CodeAndRoll2_2.5.6   
 [5] Stringendo_0.6.0      magrittr_2.0.3        tidyr_1.3.1           Matrix_1.6-5         
 [9] igraph_2.0.1.1        cowplot_1.1.3         ggplot2_3.4.4         dplyr_1.1.4          
[13] SeuratObject_4.1.4    Seurat_4.4.0          reticulate_1.35.0    

hi!
I manage to install the package fine , but when i do
devtools::load_all(path = "/Library/Frameworks/R.framework/Versions/Current/Resources/library/gruffi/")

i get the same error as #2 . i am sorry but what should I do to solve this?

Devika

Describe the bug
AssignGranuleAverageScoresFromGOterm fails at DOSE::geneInCategory() if obj@misc$gruffi$GO$GO.XXX is alredy present

> r$gruffi()
ℹ Loading gruffi
> combined.obj <- AssignGranuleAverageScoresFromGOterm(obj = combined.obj, GO_term = go1, save.UMAP = TRUE,
+                                                  new_GO_term_computation = T, clustering = granule.res.4.gruffi, plot.each.gene = F)
[1] "Score.GO.0006096"
Warning in CalculateAndPlotGoTermScores(GO = GO_term, save.UMAP = save.UMAP,  :
  Gene score Score.GO.0006096 will now be overwritten
Running GetGOTerms()
Pre-existing GO_genes results found in obj@misc$gruffi$GO[[make.names(GO)]].
Error in UseMethod("geneInCategory", x) : 
  no applicable method for 'geneInCategory' applied to an object of class "character"
[1] "GO.gene.symbol.slot ENO1 MTOR PRKAA2 PGM1 ARNT PKLR"

Current Workaround
Overwrite that slot by AssignGranuleAverageScoresFromGOterm( ... overwrite.misc.GO_genes = T, ...)

Hello,

Thank you for a great package. I'm trying to run the following code:

AD3 <- aut.res.clustering(obj = AD3)
granule.res.4.gruffi <- AD3@misc$gruffi$'optimal.granule.res'
AD3 <- reassign.small.clusters(AD3, ident = granule.res.4.gruffi)
granule.res.4.gruffi <- paste0(granule.res.4.gruffi, '.reassigned')

AD3 <- GO_score_evaluation(obj = AD3, GO_term = go2, save.UMAP = TRUE, new_GO_term_computation = T, clustering = granule.res.4.gruffi),
but am getting this error:
Error in Idents<-:
! 'value' must be a factor or vector
Run rlang::last_trace() to see where the error occurred.

ER stress GO:0034976

A <- GO_score_evaluation(obj = AD3, GO_term = go2, save.UMAP = TRUE, new_GO_term_computation = T, clustering = granule.res.4.gruffi)
Error in Idents<-:
! 'value' must be a factor or vector
Run rlang::last_trace() to see where the error occurred.
rlang::last_trace()
<error/rlang_error>
Error in Idents<-:
! 'value' must be a factor or vector

Backtrace:
▆

1. └─gruffi::GO_score_evaluation(...)
2. ├─Seurat::Idents<-(*tmp*, value = [email protected][clustering])
3. └─SeuratObject:::Idents<-.Seurat(*tmp*, value = [email protected][clustering])
   Run rlang::last_trace(drop = FALSE) to see 1 hidden frame.

rlang::last_trace(drop = FALSE)
<error/rlang_error>
Error in Idents<-:
! 'value' must be a factor or vector

Backtrace:
▆

1. └─gruffi::GO_score_evaluation(...)
2. ├─Seurat::Idents<-(*tmp*, value = [email protected][clustering])
3. └─SeuratObject:::Idents<-.Seurat(*tmp*, value = [email protected][clustering])

4. └─rlang::abort(message = "'value' must be a factor or vector")
   

Please advise on how to fix this.

here are my package versions:
sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.3.1

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] ggExpress_0.7.2 ggpubr_0.6.0 ggplot2_3.4.3 gruffi_0.7.4 Seurat_4.9.9.9059 SeuratObject_4.9.9.9091 sp_2.0-0
[8] MarkdownReports_4.5.9 MarkdownHelpers_0.2.2 CodeAndRoll2_2.3.4 Stringendo_0.3.6 magrittr_2.0.3 rgl_1.1.3 AUCell_1.22.0
[15] biomaRt_2.56.1

loaded via a namespace (and not attached):
[1] fs_1.6.3 matrixStats_1.0.0 spatstat.sparse_3.0-2 bitops_1.0-7 devtools_2.4.5 httr_1.4.7
[7] RColorBrewer_1.1-3 doParallel_1.0.17 backports_1.4.1 profvis_0.3.8 tools_4.3.1 sctransform_0.3.5
[13] utf8_1.2.3 R6_2.5.1 sm_2.2-5.7.1 lazyeval_0.2.2 uwot_0.1.16 GetoptLong_1.0.5
[19] withr_2.5.0 urlchecker_1.0.1 tictoc_1.2 prettyunits_1.1.1 gridExtra_2.3 progressr_0.14.0
[25] cli_3.6.1 Biobase_2.60.0 spatstat.explore_3.2-1 fastDummies_1.7.3 spatstat.data_3.0-1 readr_2.1.4
[31] ggridges_0.5.4 pbapply_1.7-2 slingshot_2.8.0 R.utils_2.12.2 vioplot_0.4.0 parallelly_1.36.0
[37] sessioninfo_1.2.2 rstudioapi_0.15.0 RSQLite_2.3.1 generics_0.1.3 shape_1.4.6 vroom_1.6.3
[43] gtools_3.9.4 ica_1.0-3 spatstat.random_3.1-5 car_3.1-2 zip_2.3.0 dplyr_1.1.3
[49] Matrix_1.6-1 fansi_1.0.4 S4Vectors_0.38.1 SCENIC_1.3.1 clipr_0.8.0 abind_1.4-5
[55] terra_1.7-39 R.methodsS3_1.8.2 lifecycle_1.0.3 SoupX_1.6.2 carData_3.0-5 SummarizedExperiment_1.30.2
[61] BiocFileCache_2.8.0 Rtsne_0.16 grid_4.3.1 blob_1.2.4 gdata_2.19.0 promises_1.2.1
[67] crayon_1.5.2 miniUI_0.1.1.1 lattice_0.21-8 cowplot_1.1.1 annotate_1.78.0 KEGGREST_1.40.0
[73] knitr_1.43 pillar_1.9.0 ComplexHeatmap_2.16.0 GenomicRanges_1.52.0 rjson_0.2.21 future.apply_1.11.0
[79] codetools_0.2-19 leiden_0.4.3 glue_1.6.2 data.table_1.14.8 remotes_2.4.2.1 vctrs_0.6.3
[85] png_0.1-8 spam_2.9-1 gtable_0.3.4 cachem_1.0.8 xfun_0.40 openxlsx_4.2.5.2
[91] princurve_2.1.6 S4Arrays_1.0.6 mime_0.12 ReadWriter_0.3.0 tidyverse_2.0.0 survival_3.5-7
[97] pheatmap_1.0.12 SingleCellExperiment_1.22.0 iterators_1.0.14 ellipsis_0.3.2 fitdistrplus_1.1-11 ROCR_1.0-11
[103] nlme_3.1-163 Seurat.utils_1.9.5 usethis_2.2.2 bit64_4.0.5 progress_1.2.2 filelock_1.0.2
[109] RcppAnnoy_0.0.21 GenomeInfoDb_1.36.2 irlba_2.3.5.1 KernSmooth_2.23-22 colorspace_2.1-0 BiocGenerics_0.46.0
[115] DBI_1.1.3 raster_3.6-23 tidyselect_1.2.0 processx_3.8.2 bit_4.0.5 compiler_4.3.1
[121] curl_5.0.2 graph_1.78.0 hdf5r_1.3.8 xml2_1.3.5 DelayedArray_0.26.7 plotly_4.10.2
[127] scales_1.2.1 lmtest_0.9-40 callr_3.7.3 rappdirs_0.3.3 stringr_1.5.0 digest_0.6.33
[133] goftest_1.2-3 spatstat.utils_3.0-3 XVector_0.40.0 base64enc_0.1-3 htmltools_0.5.6 pkgconfig_2.0.3
[139] sparseMatrixStats_1.12.2 MatrixGenerics_1.12.3 dbplyr_2.3.3 fastmap_1.1.1 rlang_1.1.1 GlobalOptions_0.1.2
[145] htmlwidgets_1.6.2 shiny_1.7.5 DelayedMatrixStats_1.22.6 zoo_1.8-12 jsonlite_1.8.7 BiocParallel_1.34.2
[151] R.oo_1.25.0 RCurl_1.98-1.12 GenomeInfoDbData_1.2.10 dotCall64_1.0-2 patchwork_1.1.3 munsell_0.5.0
[157] Rcpp_1.0.11 viridis_0.6.4 reticulate_1.31 TrajectoryUtils_1.8.0 stringi_1.7.12 zlibbioc_1.46.0
[163] MASS_7.3-60 org.Hs.eg.db_3.17.0 plyr_1.8.8 pkgbuild_1.4.2 parallel_4.3.1 listenv_0.9.0
[169] ggrepel_0.9.3 deldir_1.0-9 Biostrings_2.68.1 splines_4.3.1 tensor_1.5 hms_1.1.3
[175] circlize_0.4.15 ps_1.7.5 igraph_1.5.1 spatstat.geom_3.2-4 ggsignif_0.6.4 RcppHNSW_0.4.1
[181] reshape2_1.4.4 stats4_4.3.1 pkgload_1.3.2.1 XML_3.99-0.14 BiocManager_1.30.22 tzdb_0.4.0
[187] foreach_1.5.2 httpuv_1.6.11 RANN_2.6.1 tidyr_1.3.0 purrr_1.0.2 polyclip_1.10-4
[193] future_1.33.0 clue_0.3-64 scattermore_1.2 broom_1.0.5 xtable_1.8-4 RSpectra_0.16-1
[199] rstatix_0.7.2 ggcorrplot_0.1.4 later_1.3.1 viridisLite_0.4.2 tibble_3.2.1 memoise_2.0.1
[205] AnnotationDbi_1.62.2 IRanges_2.34.1 cluster_2.1.4 HGNChelper_0.8.1 globals_0.16.2 GSEABase_1.62.0

Hello,

I am trying to install gruffi in my computer and while installing the dependencies, it keeps complaining that some functions are not available. I cannot figure out the proper versions, could someone share the R and the packages/dependencies versions? I believe this could be the issue.

Thank you for your time,
Paula

Hello,

Firstly thank you for the tool. I've previously managed to run gruffi fine and it was helpful in better defining pseudotime for my dataset. However, recently I tried to run this again on another dataset but I was unable to load the package. Below is the error I'm getting:

Warning messages:
1: S3 method ‘plot.clust.size.distr’ was declared in NAMESPACE but not found
2: In setup_ns_exports(path, export_all, export_imports) :
Objects listed as exports, but not present in namespace: AddCustomScore, AddGOGeneList.manual, AddGOScore, CalcTranscriptomePercentage, CalcTranscriptomePercentageGO, CleanDuplicateScorenames, CustomScoreEvaluation, FeaturePlotSaveCustomScore, FeaturePlotSaveGO, FilterStressedCells, GO_score_evaluation, GetAllGOTerms, GetGOTerms, GetNamedClusteringRuns, IntersectWithExpressed, PasteUniqueGeneList, PlotGoTermScores, PlotNormAndSkew, Shiny.GO.thresh, UMAP.3d.cubes, aut.res.clustering, calc.cluster.averages.gruffi, clUMAP.thresholding, fix.metad.Colname.rm.trailing.1, reassign.small.clusters, saveData, saveRDS.compress.in.BG, sparse.cor, stand_dev_skewed, ww.convert.GO_term.2.score, ww.convert.score.2.GO_term

Investigate if this is correctly adressed:

i saw that u are using AnnotationDbi anyways- i would strongly be in favor for the clusterprofiler::bitr part be replaced with a simple AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db, "GO:0006096", c("SYMBOL"), "GO")$SYMBOL

intersect(biomaRt::getBM(attributes = c('hgnc_symbol'),filters = "go_parent_term", uniqueRows = TRUE,values = "GO:0006096", mart = ensembl)[,1],AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db, "GO:0006096", c("SYMBOL"), "GOALL")$SYMBOL %>% unique())

AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db, "GO:0006096", c("SYMBOL"), "GOALL")$SYMBOL %>% unique()

# so please instead of

genes <- clusterProfiler::bitr("GO:0006096",fromType="GO",toType="SYMBOL",OrgDb = org.Hs.eg.db::org.Hs.eg.db)$SYMBOL

genes <- AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg.db, GO, c("SYMBOL"), "GOALL")$SYMBOL %>% unique()

# GOALL is for using the equivalent of go_parent_term

Problem

 ensembl <- biomaRt::useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl")
Error in `collect()`:
! Failed to collect lazy table.
Caused by error in `db_collect()`:
! Arguments in `...` must be used.
✖ Problematic argument:
• ..1 = Inf
ℹ Did you misspell an argument name?

Solution is related to dbplyr

https://stackoverflow.com/questions/77370659/error-failed-to-collect-lazy-table-caused-by-error-in-db-collect-using