您好,
近期在尋找single cell scoring相關方式, 發現了這麼方便的整合性package.

但目前使用上有一個問題是, 在irGSEA.density.scatterplot畫出的scatter plot如圖呈現:
https://ibb.co/GfZYsnY

scatterplot <- irGSEA.density.scatterplot(object = da1,
method = "ssgsea",
show.geneset = c("MSC-1","MSC-2","EC-1"),
reduction = "umap")

但如果直接調出ssGSEA data的話, 會如下圖呈現:
https://ibb.co/pbkrL8k

DefaultAssay(da1) <- 'ssgsea'
FeaturePlot(da1, features = c("MSC-1","MSC-2","EC-1"))

想詢問您在這兩張圖是如何進行換算的, 謝謝~

再次感謝您整合了這麼多種scoring方式, 方便讓我們對有興趣的signatures進行比較.

Dear team，irGSEA is a very handy tool，But I found it on my own data that it couldn't be integrated,here is my code:

methods = c("ssgsea")
xx=subset(scRNAh,SCT_snn_res.0.3=="20")
yy <- CreateSeuratObject(xx@assays$RNA@counts, meta.data = [email protected],min.cells = 0,min.features = 0)
yy <- SeuratObject::UpdateSeuratObject(yy)

scRNA_gs=irGSEA.score(object = yy, assay = "RNA",
             slot = "data", seeds = 123, ncores = 8,
             min.cells = 0, min.feature = 0,
             custom = T, geneset = subtype_geneset,   
             subcategory = NULL, geneid = "symbol",
             method = methods,
             aucell.MaxRank = 2000, ucell.MaxRank = 2000,
             kcdf = 'Gaussian')

Seurat::Assays(scRNA_gs)
# > [1] "RNA"


result.dge <- irGSEA.integrate(object = scRNA_gs,
                               group.by = "seurat_clusters",
                               metadata = NULL, col.name = NULL,
                               method = methods)


# Error in irGSEA.integrate(object = scRNA_gs, group.by = "seurat_clusters",  : 
  Please imput correct `method`

irGSEA.score run was successful，However, the results were not saved in the assays slot, resulting in errors in subsequent integration analysis.Is it because which of my software versions is wrong or for some other reason?

here is my sessionInfo :

R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 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=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] parallel  grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] SeuratData_0.2.2      irGSEA_2.1.5          genieclust_1.1.3      cowplot_1.1.1         clustree_0.5.0       
 [6] ggraph_2.1.0          randomcoloR_1.1.0.1   future_1.31.0         enrichplot_1.18.4     clusterProfiler_4.6.2
[11] COSG_0.9.0            harmony_0.1.1         Rcpp_1.0.10           scCustomize_1.1.1     SeuratObject_4.1.3   
[16] Seurat_4.3.0          ComplexHeatmap_2.14.0 GenomicRanges_1.50.2  GenomeInfoDb_1.34.9   IRanges_2.32.0       
[21] S4Vectors_0.36.1      BiocGenerics_0.44.0   ggplotify_0.1.0       purrr_1.0.1           stringr_1.5.0        
[26] reshape2_1.4.4        ggpubr_0.6.0          plotly_4.10.1         RColorBrewer_1.1-3    tibble_3.1.8         
[31] ggsci_2.9             tidyr_1.3.0           data.table_1.14.8     ggplot2_3.4.2         rlang_1.1.0          
[36] dplyr_1.1.0          

loaded via a namespace (and not attached):
  [1] rsvd_1.0.5                  ica_1.0-3                   ps_1.7.2                    foreach_1.5.2              
  [5] lmtest_0.9-40               rprojroot_2.0.3             crayon_1.5.2                V8_4.3.0                   
  [9] MASS_7.3-58.2               rhdf5filters_1.10.1         nlme_3.1-162                backports_1.4.1            
 [13] GOSemSim_2.24.0             XVector_0.38.0              HDO.db_0.99.1               ROCR_1.0-11                
 [17] irlba_2.3.5.1               callr_3.7.3                 limma_3.54.1                BiocParallel_1.32.5        
 [21] rjson_0.2.21                bit64_4.0.5                 glue_1.6.2                  sctransform_0.3.5          
 [25] processx_3.8.0              vipor_0.4.5                 spatstat.sparse_3.0-0       AnnotationDbi_1.60.2       
 [29] DOSE_3.24.2                 spatstat.geom_3.0-6         tidyselect_1.2.0            SummarizedExperiment_1.28.0
 [33] usethis_2.1.6               UCell_2.5.0                 fitdistrplus_1.1-8          XML_3.99-0.14              
 [37] zoo_1.8-11                  xtable_1.8-4                magrittr_2.0.3              cli_3.6.0                  
 [41] zlibbioc_1.44.0             rstudioapi_0.14             miniUI_0.1.1.1              sp_1.6-0                   
 [45] fastmatch_1.1-3             treeio_1.22.0               shiny_1.7.4                 GSVA_1.46.0                
 [49] BiocSingular_1.14.0         clue_0.3-64                 pkgbuild_1.4.0              gson_0.1.0                 
 [53] cluster_2.1.4               tidygraph_1.2.3             KEGGREST_1.38.0             ggrepel_0.9.3              
 [57] ape_5.7                     listenv_0.9.0               Biostrings_2.66.0           png_0.1-8                  
 [61] withr_2.5.0                 bitops_1.0-7                ggforce_0.4.1               plyr_1.8.8                 
 [65] GSEABase_1.60.0             pillar_1.8.1                GlobalOptions_0.1.2         cachem_1.0.6               
 [69] fs_1.6.1                    GetoptLong_1.0.5            paletteer_1.5.0             DelayedMatrixStats_1.20.0  
 [73] vctrs_0.5.2                 ellipsis_0.3.2              generics_0.1.3              devtools_2.4.5             
 [77] tools_4.2.1                 beeswarm_0.4.0              munsell_0.5.0               tweenr_2.0.2               
 [81] fgsea_1.24.0                DelayedArray_0.24.0         fastmap_1.1.0               compiler_4.2.1             
 [85] pkgload_1.3.2               abind_1.4-5                 httpuv_1.6.9                sessioninfo_1.2.2          
 [89] GenomeInfoDbData_1.2.9      gridExtra_2.3               edgeR_3.40.2                lattice_0.20-45            
 [93] deldir_1.0-6                utf8_1.2.3                  later_1.3.0                 jsonlite_1.8.4             
 [97] scales_1.2.1                ScaledMatrix_1.6.0          graph_1.76.0                tidytree_0.4.2             
[101] pbapply_1.7-0               carData_3.0-5               sparseMatrixStats_1.10.0    lazyeval_0.2.2             
[105] promises_1.2.0.1            car_3.1-1                   doParallel_1.0.17           R.utils_2.12.2             
[109] goftest_1.2-3               spatstat.utils_3.0-1        reticulate_1.28             Rtsne_0.16                 
[113] forcats_1.0.0               downloader_0.4              Biobase_2.58.0              uwot_0.1.14                
[117] igraph_1.4.0                HDF5Array_1.26.0            survival_3.5-3              htmltools_0.5.4            
[121] memoise_2.0.1               profvis_0.3.7               locfit_1.5-9.7              graphlayouts_0.8.4         
[125] viridisLite_0.4.1           digest_0.6.31               rappdirs_0.3.3              mime_0.12                  
[129] RSQLite_2.3.1               yulab.utils_0.0.6           future.apply_1.10.0         remotes_2.4.2              
[133] urlchecker_1.0.1            blob_1.2.4                  R.oo_1.25.0                 splines_4.2.1              
[137] rematch2_2.1.2              Rhdf5lib_1.20.0             RCurl_1.98-1.10             broom_1.0.3                
[141] rhdf5_2.42.1                colorspace_2.1-0            BiocManager_1.30.19         ggbeeswarm_0.7.2           
[145] shape_1.4.6                 aplot_0.1.9                 ggrastr_1.0.1               RANN_2.6.1                 
[149] circlize_0.4.15             fansi_1.0.4                 parallelly_1.34.0           R6_2.5.1                   
[153] ggridges_0.5.4              lifecycle_1.0.3             curl_5.0.0                  ggsignif_0.6.4             
[157] leiden_0.4.3                snakecase_0.11.0            Matrix_1.5-3                qvalue_2.30.0              
[161] desc_1.4.2                  RcppAnnoy_0.0.20            iterators_1.0.14            spatstat.explore_3.0-6     
[165] htmlwidgets_1.6.1           beachmat_2.14.0             polyclip_1.10-4             shadowtext_0.1.2           
[169] timechange_0.2.0            gridGraphics_0.5-1          singscore_1.18.0            globals_0.16.2             
[173] patchwork_1.1.2             spatstat.random_3.1-3       progressr_0.13.0            codetools_0.2-19           
[177] matrixStats_0.63.0          lubridate_1.9.2             GO.db_3.16.0                prettyunits_1.1.1          
[181] SingleCellExperiment_1.20.1 R.methodsS3_1.8.2           gtable_0.3.1                DBI_1.1.3                  
[185] ggfun_0.0.9                 tensor_1.5                  httr_1.4.5                  KernSmooth_2.23-20         
[189] stringi_1.7.12              msigdbr_7.5.1               farver_2.1.1                annotate_1.76.0            
[193] viridis_0.6.2               ggtree_3.6.2                BiocNeighbors_1.16.0        AUCell_1.20.2              
[197] scattermore_0.8             bit_4.0.5                   scatterpie_0.2.0            MatrixGenerics_1.10.0      
[201] spatstat.data_3.0-0         janitor_2.2.0               pkgconfig_2.0.3             babelgene_22.9             
[205] ggprism_1.0.4               rstatix_0.7.2

你好！我的单细胞样本分为三组，对照组，模型组及药物干预组。我想在同一张图片上展示这三组某一信号通路的变化，应该怎么设置参数，谢谢!

尊敬的作者您好！
很感谢您开的这个工具为我提供了帮助与便利，今天我在运行irGSEA.score这个函数的时候发现含多个signature的geneset 会出现报错，如果让geneset只含有一个signature是能够正常运行的.以下是报错内容：
#------------------------------------------------------------------------------------------------------------------------------------------

sce.ssgsea <- irGSEA.score(object = sce, assay = "RNA", slot = "data", seeds = 123, ncores = 60,msigdb=F, custom = T,

•                     geneset = c5gsbp, method = c("ssgsea"), kcdf = 'Gaussian')
  

Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Ensuring feature names don't have underscores or pipes
Object representation is consistent with the most current Seurat version
Calculate ssgsea scores
Error in relist(v, part) :
shape of 'skeleton' is not compatible with 'NROW(flesh)'
此外: Warning messages:
1: In .local(expr, gset.idx.list, ...) :
Using 'dgCMatrix' objects as input is still in an experimental stage.
2: In .filterFeatures(expr, method) :
11 genes with constant expression values throuhgout the samples.
#------------------------------------------------------------------------------------------------------------------------------------------
希望能够得到您的帮助 ！ 感谢

rm(list = ls())# 删除所有已定义对象
getwd()
setwd("/mnt/data/home/tycloud")

library(Seurat)
library(SeuratData)
library(RcppML)
library(irGSEA)
library(doMC)
registerDoMC(cores = 20)

Sys.setenv(RETICULATE_PYTHON = "/mnt/data/tool/miniconda3/envs/irGSEA/bin/python")
reticulate::py_config()

读取gsva.py文件

gsva_path <- "/mnt/data/home/tycloud/R/library/4.3.3/irGSEA/python/gsva.py"
gsva_content <- readLines(gsva_path)

定位到保存结果的部分并修改

modified_content <- gsub("acts.to_csv\('./matrix.py.result.csv'\)",
"pd.DataFrame(acts[0]).to_csv('./matrix.py.result.csv')",
gsva_content)

保存

writeLines(modified_content, gsva_path)

load("Scissor/HCC_endothelium.Rdata") # 加载保存的Seurat对象

【检查Seurat对象版本】【HCC_endothelium 是 Seurat 版本 5.0.1，运行失败，而我的其他Seurat对象是3.2.1，都成功运行了VISION】

check_seurat_version <- function(object_name) {
object <- get(object_name)
if (inherits(object, "Seurat")) {
version <- object@version
cat(paste0(object_name, " 是 Seurat 版本 ", version, "\n"))
} else {
cat(paste0(object_name, " 不是 Seurat 对象\n"))
}
}

检查每个对象

check_seurat_version("HCC_endothelium")

HCC_endothelium 是 Seurat 版本 5.0.1

HCC_endothelium <- irGSEA.score(object = HCC_endothelium , assay = "RNA", slot = "counts", seeds = 123, ncores = 60, min.cells = 3, min.feature = 0, custom = FALSE, geneset = NULL, msigdb = TRUE, species = "Homo sapiens", category = "H", subcategory = NULL, geneid = "symbol",

•                             method = c("VISION"), aucell.MaxRank = NULL, ucell.MaxRank = NULL, kcdf = 'Gaussian')
  

Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Updating matrix keys for DimReduc 'umap'
Updating matrix keys for DimReduc 'pca'
Updating matrix keys for DimReduc 'harmony'
Warning: Assay RNA changing from Assay to Assay
Warning: DimReduc umap changing from DimReduc to DimReduc
Warning: Adding a dimensional reduction (umap) without the associated assay being present
Ensuring keys are in the proper structure
Warning: Adding a dimensional reduction (umap) without the associated assay being present
Ensuring feature names don't have underscores or pipes
Warning: Adding a dimensional reduction (umap) without the associated assay being present
Updating slots in RNA
Updating slots in RNA_nn
Setting default assay of RNA_nn to RNA
Updating slots in RNA_snn
Setting default assay of RNA_snn to RNA
Updating slots in umap
Setting umap DimReduc to global
Warning: Adding a dimensional reduction (umap) without the associated assay being present
Updating slots in pca
Updating slots in harmony
No assay information could be found for FindIntegrationAnchors
Setting assay used for NormalizeData.RNA to RNA
Setting assay used for FindVariableFeatures.RNA to RNA
Setting assay used for ScaleData.RNA to RNA
Setting assay used for RunPCA.RNA to RNA
Setting assay used for Seurat..ProjectDim.RNA.harmony to RNA
Setting assay used for FindNeighbors.RNA.pca to RNA
Validating object structure for Assay 'RNA'
Validating object structure for Graph 'RNA_nn'
Validating object structure for Graph 'RNA_snn'
Validating object structure for DimReduc 'umap'
Validating object structure for DimReduc 'pca'
Validating object structure for DimReduc 'harmony'
Object representation is consistent with the most current Seurat version
Calculate VISION scores
Importing counts from obj[["RNA"]]@CountS ...
Normalizing to counts per 10,000...
Importing Meta Data from [email protected] ...
Importing latent space from Embeddings(obj, "pca") using first 50 components

Using 17514/66590 genes detected in 0.10% of cells for signature analysis.
See the sig_gene_threshold input to change this behavior.

Dropping 'CellName' from meta data as it is of type 'character' and has more than 20 unique values. If you want to include this meta data variable, convert it to a factor before providing the data frame to Vision
Dropping 'PatientID' from meta data as it is of type 'character' and has more than 20 unique values. If you want to include this meta data variable, convert it to a factor before providing the data frame to Vision
Dropping 'Sub_Cluster' from meta data as it is of type 'character' and has more than 20 unique values. If you want to include this meta data variable, convert it to a factor before providing the data frame to Vision
Adding Visualization: Seurat_umap
Adding Visualization: Seurat_pca
Adding Visualization: Seurat_harmony
Beginning Analysis

Clustering cells...
Using latent space to cluster cells...
completed

Projecting data into 2 dimensions...

Evaluating signature scores on cells...

|=========================================================================================================================| 100%, Elapsed 00:01
Evaluating signature-gene importance...

|=========================================================================================================================| 100%, Elapsed 00:31
Creating 5 background signature groups with the following parameters:
sigSize sigBalance
1 37 1
2 92 1
3 144 1
4 175 1
5 192 1
signatures per group: 3000
Computing KNN Cell Graph in the Latent Space...

Evaluating local consistency of signatures in latent space...

|=========================================================================================================================| 100%, Elapsed 00:02
|=========================================================================================================================| 100%, Elapsed 05:36
|=========================================================================================================================| 100%, Elapsed 06:04
|=============================================================================================================== | 89%, ETA 00:01
Error: $ operator is invalid for atomic vectors

您好，很感激您的工作，让我们可以如此方便使用。使用中有以下几个问题，望解答：
1、教程中的“result.dge”格式是list，我有尝试去对它进行一个排序后，作图只显示top的多个HALLMARK，其他没有意义的就不用展示，但是没有成功，请问是否有解决的办法呢。
2、我也尝试导出数据，但是导出的数据格式是混乱的。在作图的时候“show.geneset”这里如果是一个基金集是可以作图，但是多个HALLMARK就不行了，请问如何解决呢，谢谢您。
实在抱歉，英文实在太差了，就直接中文提问了，请您见谅。

Pretty sure this error has come up recently due to an update of some R packages, not sure if it's from AUCell through.
run this:

pbmc3k.final <- irGSEA.score(object = pbmc3k.final, assay = "RNA",
slot = "data", seeds = 123, ncores = 1,
min.cells = 3, min.feature = 0,
custom = F, geneset = NULL, msigdb = T,
species = "Homo sapiens", category = "H",
subcategory = NULL, geneid = "symbol",
method = c("AUCell", "UCell", "singscore",
"ssgsea"),
aucell.MaxRank = NULL, ucell.MaxRank = NULL,
kcdf = 'Gaussian')

get the error below:

Calculate AUCell scores
Error in .AUCell_buildRankings(exprMat = exprMat, featureType = featureType, :
To use a dgCMatrix as input set 'splitByBlocks=TRUE'.

Thanks for building such an amazing package. I would like to know how to input my diy gene set for evaluation? Can you provide a data type requirement or an example for a custom gene set? Thank you!

我用的object是seurat v5+ bpcells做出来的，所以情况特殊，我已经将所有的数据转换成dgCMatrix了，但是依旧不可行。
代码如下：

library(irGSEA)
CD4 = JoinLayers(obj.sp.t.list$CD4)
CD4@assays$RNA$counts = as(object = CD4@assays$RNA$counts, Class = "dgCMatrix")
CD4@assays$RNA$data = as(object = CD4@assays$RNA$data, Class = "dgCMatrix")
CD4@assays$RNA$scale.data = as(object = CD4@assays$RNA$scale.data, Class = "dgCMatrix")
CD4 = irGSEA.score(object = CD4, species = "Mus musculus", assay = "RNA", slot = "data", method = "ssgsea")

报错如下：

Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Updating matrix keys for DimReduc 'pca'
Updating matrix keys for DimReduc 'umap.unintegrated'
Updating matrix keys for DimReduc 'harmony'
Updating matrix keys for DimReduc 'umap.harmony'
Updating matrix keys for DimReduc 're.harmony'
Updating matrix keys for DimReduc 'umap.re.harmony'
Updating matrix keys for DimReduc 're2.harmony'
Updating matrix keys for DimReduc 'umap.re2.harmony'
Ensuring keys are in the proper structure
Ensuring feature names don't have underscores or pipes
Updating slots in RNA
Updating slots in RNA_nn
Setting default assay of RNA_nn to RNA
Updating slots in RNA_snn
Setting default assay of RNA_snn to RNA
Updating slots in pca
Updating slots in umap.unintegrated
Setting umap.unintegrated DimReduc to global
Updating slots in harmony
Updating slots in umap.harmony
Setting umap.harmony DimReduc to global
Updating slots in re.harmony
Updating slots in umap.re.harmony
Setting umap.re.harmony DimReduc to global
Updating slots in re2.harmony
Updating slots in umap.re2.harmony
Setting umap.re2.harmony DimReduc to global
Setting assay used for RunUMAP.RNA.pca to RNA
Setting assay used for FindNeighbors.RNA.pca to RNA
Setting assay used for RunUMAP.RNA.harmony to RNA
Setting assay used for FindNeighbors.RNA.harmony to RNA
Setting assay used for RunUMAP.RNA.re.harmony to RNA
Setting assay used for FindNeighbors.RNA.re.harmony to RNA
Setting assay used for NormalizeData.RNA to RNA
Setting assay used for FindVariableFeatures.RNA to RNA
Setting assay used for ScaleData.RNA to RNA
Setting assay used for RunPCA.RNA to RNA
Setting assay used for RunUMAP.RNA.re2.harmony to RNA
Setting assay used for FindNeighbors.RNA.re2.harmony to RNA
No assay information could be found for FindClusters
Validating object structure for Assay5 'RNA'
Validating object structure for Graph 'RNA_nn'
Validating object structure for Graph 'RNA_snn'
Validating object structure for DimReduc 'pca'
Validating object structure for DimReduc 'umap.unintegrated'
Validating object structure for DimReduc 'harmony'
Validating object structure for DimReduc 'umap.harmony'
Validating object structure for DimReduc 're.harmony'
Validating object structure for DimReduc 'umap.re.harmony'
Validating object structure for DimReduc 're2.harmony'
Validating object structure for DimReduc 'umap.re2.harmony'
Object representation is consistent with the most current Seurat version
Error in validObject(.Object) : 
  invalid class "Assay" object: invalid object for slot "scale.data" in class "Assay": got class "dgCMatrix", should be or extend class "matrix"
In addition: Warning message:
Adding a command log without an assay associated with it 

sessioninfo如下：

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=Chinese (Simplified)_China.utf8  LC_CTYPE=Chinese (Simplified)_China.utf8    LC_MONETARY=Chinese (Simplified)_China.utf8
[4] LC_NUMERIC=C                                LC_TIME=Chinese (Simplified)_China.utf8    

time zone: Asia/Shanghai
tzcode source: internal

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

other attached packages:
 [1] shinyBS_0.61.1              irGSEA_3.1.7                ComplexHeatmap_2.18.0       scRepertoire_1.12.0         BiocParallel_1.36.0        
 [6] SingleR_2.4.0               SummarizedExperiment_1.32.0 Biobase_2.62.0              GenomicRanges_1.54.1        GenomeInfoDb_1.38.1        
[11] IRanges_2.36.0              S4Vectors_0.40.2            BiocGenerics_0.48.1         MatrixGenerics_1.14.0       matrixStats_1.1.0          
[16] future_1.33.0               dplyr_1.1.4                 RColorBrewer_1.1-3          cowplot_1.1.1               patchwork_1.1.3            
[21] ggplot2_3.4.4               BPCells_0.1.0               SeuratWrappers_0.3.2        pbmcsca.SeuratData_3.0.0    pbmcref.SeuratData_1.0.0   
[26] ifnb.SeuratData_3.1.0       SeuratData_0.2.2.9001       Seurat_5.0.1                SeuratObject_5.0.1          sp_2.1-2                   

loaded via a namespace (and not attached):
  [1] R.methodsS3_1.8.2                 progress_1.2.3                    poweRlaw_0.70.6                   goftest_1.2-3                    
  [5] DT_0.31                           Biostrings_2.70.1                 vctrs_0.6.5                       spatstat.random_3.2-2            
  [9] digest_0.6.33                     png_0.1-8                         shape_1.4.6                       ggrepel_0.9.4                    
 [13] deldir_2.0-2                      parallelly_1.36.0                 permute_0.9-7                     MASS_7.3-60                      
 [17] Signac_1.12.0                     reshape2_1.4.4                    httpuv_1.6.13                     foreach_1.5.2                    
 [21] withr_2.5.2                       xfun_0.41                         ggpubr_0.6.0.999                  ellipsis_0.3.2                   
 [25] survival_3.5-7                    EnsDb.Hsapiens.v86_2.99.0         memoise_2.0.1                     gtools_3.9.5                     
 [29] zoo_1.8-12                        GlobalOptions_0.1.2               pbapply_1.7-2                     R.oo_1.25.0                      
 [33] prettyunits_1.2.0                 KEGGREST_1.42.0                   promises_1.2.1                    evmix_2.12                       
 [37] httr_1.4.7                        rstatix_0.7.2                     restfulr_0.0.15                   rhdf5filters_1.14.1              
 [41] globals_0.16.2                    fitdistrplus_1.1-11               rhdf5_2.46.1                      rstudioapi_0.15.0                
 [45] miniUI_0.1.1.1                    generics_0.1.3                    ggalluvial_0.12.5                 babelgene_22.9                   
 [49] curl_5.2.0                        zlibbioc_1.48.0                   ScaledMatrix_1.10.0               ggraph_2.1.0                     
 [53] polyclip_1.10-6                   GenomeInfoDbData_1.2.11           SparseArray_1.2.2                 pracma_2.4.4                     
 [57] xtable_1.8-4                      stringr_1.5.1                     doParallel_1.0.17                 evaluate_0.23                    
 [61] S4Arrays_1.2.0                    BiocFileCache_2.10.1              hms_1.1.3                         irlba_2.3.5.1                    
 [65] colorspace_2.1-0                  filelock_1.0.3                    hdf5r_1.3.8                       ROCR_1.0-11                      
 [69] reticulate_1.34.0                 spatstat.data_3.0-3               readr_2.1.4                       magrittr_2.0.3                   
 [73] lmtest_0.9-40                     later_1.3.2                       viridis_0.6.4                     lattice_0.22-5                   
 [77] spatstat.geom_3.2-7               future.apply_1.11.0               SparseM_1.81                      scattermore_1.2                  
 [81] XML_3.99-0.16                     RcppAnnoy_0.0.21                  pillar_1.9.0                      nlme_3.1-164                     
 [85] iterators_1.0.14                  caTools_1.18.2                    compiler_4.3.1                    beachmat_2.18.0                  
 [89] RSpectra_0.16-1                   stringi_1.8.2                     tensor_1.5                        GenomicAlignments_1.38.0         
 [93] plyr_1.8.9                        msigdbr_7.5.1                     crayon_1.5.2                      abind_1.4-5                      
 [97] BiocIO_1.12.0                     googledrive_2.1.1                 powerTCR_1.22.0                   graphlayouts_1.0.2               
[101] bit_4.0.5                         fastmatch_1.1-4                   codetools_0.2-19                  BiocSingular_1.18.0              
[105] GetoptLong_1.0.5                  plotly_4.10.3                     mime_0.12                         splines_4.3.1                    
[109] circlize_0.4.15                   Rcpp_1.0.11                       fastDummies_1.7.3                 dbplyr_2.4.0                     
[113] sparseMatrixStats_1.14.0          cellranger_1.1.0                  knitr_1.45                        blob_1.2.4                       
[117] utf8_1.2.4                        seqLogo_1.68.0                    clue_0.3-65                       AnnotationFilter_1.26.0          
[121] fs_1.6.3                          listenv_0.9.0                     evd_2.3-6.1                       DelayedMatrixStats_1.24.0        
[125] gsl_2.1-8                         ggsignif_0.6.4                    tibble_3.2.1                      Matrix_1.6-4                     
[129] tzdb_0.4.0                        tweenr_2.0.2                      pkgconfig_2.0.3                   tools_4.3.1                      
[133] cachem_1.0.8                      RSQLite_2.3.4                     viridisLite_0.4.2                 DBI_1.1.3                        
[137] fastmap_1.1.1                     rmarkdown_2.25                    scales_1.3.0                      ica_1.0-3                        
[141] shinydashboard_0.7.2              Rsamtools_2.18.0                  broom_1.0.5                       BiocManager_1.30.22              
[145] dotCall64_1.1-1                   carData_3.0-5                     RANN_2.6.1                        farver_2.1.1                     
[149] tidygraph_1.2.3                   mgcv_1.9-0                        yaml_2.3.7                        VGAM_1.1-9                       
[153] rtracklayer_1.62.0                cli_3.6.2                         purrr_1.0.2                       leiden_0.4.3.1                   
[157] lifecycle_1.0.4                   uwot_0.1.16                       presto_1.0.0                      backports_1.4.1                  
[161] BSgenome.Hsapiens.UCSC.hg38_1.4.5 annotate_1.80.0                   gtable_0.3.4                      rjson_0.2.21                     
[165] ggridges_0.5.4                    progressr_0.14.0                  cubature_2.1.0                    parallel_4.3.1                   
[169] jsonlite_1.8.8                    RcppHNSW_0.5.0                    TFBSTools_1.40.0                  bitops_1.0-7                     
[173] bit64_4.0.5                       Rtsne_0.17                        vegan_2.6-4                       spatstat.utils_3.0-4             
[177] CNEr_1.38.0                       shinyjs_2.1.0                     SeuratDisk_0.0.0.9021             R.utils_2.12.3                   
[181] truncdist_1.0-2                   lazyeval_0.2.2                    shiny_1.8.0                       Azimuth_0.5.0                    
[185] htmltools_0.5.7                   GO.db_3.18.0                      sctransform_0.4.1                 rappdirs_0.3.3                   
[189] ensembldb_2.26.0                  glue_1.6.2                        TFMPvalue_0.0.9                   googlesheets4_1.1.1              
[193] spam_2.10-0                       XVector_0.42.0                    RCurl_1.98-1.13                   BSgenome_1.70.1                  
[197] gridExtra_2.3                     JASPAR2020_0.99.10                igraph_1.5.1                      R6_2.5.1                         
[201] tidyr_1.3.0                       SingleCellExperiment_1.24.0       RcppRoll_0.3.0                    GenomicFeatures_1.54.1           
[205] cluster_2.1.6                     Rhdf5lib_1.24.0                   gargle_1.5.2                      stringdist_0.9.12                
[209] DirichletMultinomial_1.44.0       DelayedArray_0.28.0               tidyselect_1.2.0                  ProtGenerics_1.34.0              
[213] ggforce_0.4.1                     xml2_1.3.6                        car_3.1-2                         AnnotationDbi_1.64.1             
[217] rsvd_1.0.5                        munsell_0.5.0                     KernSmooth_2.23-22                data.table_1.14.10               
[221] htmlwidgets_1.6.4                 biomaRt_2.58.0                    rlang_1.1.2                       spatstat.sparse_3.0-3            
[225] spatstat.explore_3.2-5            remotes_2.4.2.1                   fansi_1.0.6                      

Hello,

Very helpful tool! Thank you for your work.

I've encounted an issue when using plotting functions from the package. The pathways showed in the plot seem to be chosen by their names rather than significance or degree of difference. Like in GOBP, I can always find pathways like GOBP_Acid and GOBP_3UTR in the plot. Besides, if I specified top = 5, I only found pathways that begin with numbers following "GOBP". Is this how the parament designed to be, or maybe it is a bug?

Thank you!

I ran irGSEA.upset using example data. The command was "irGSEA.upset(object = result.dge, method = "UCell")".
The error was as follows:

Error in if (as.character(ta_call[[1]]) == "upset_top_annotation") { :
the condition has length > 1

ps. irGSEA v1.1.2 and UCell 1.99.6 and R v4.2.0α on win10

Dear developer,
It is nice to see such an amazing tools created , but there is something disturbing me .
There is an error happened when I run irGSEA.integrate; here is my code :
result.dge <- irGSEA.integrate(object = malignant,metadata = NULL,group.by = 'group', col.name = NULL,method = c("AUCell","UCell","singscore","ssgsea"))
Calculate differential gene set : AUCell
Calculate differential gene set : UCell
Calculate differential gene set : singscore
Calculate differential gene set : ssgsea
Error in UseMethod("distinct") :
no applicable method for 'distinct' applied to an object of class "NULL"
In addition: Warning messages:
1: In FindMarkers.default(object = data.use, cells.1 = cells.1, cells.2 = cells.2, :
No features pass logfc.threshold threshold; returning empty data.frame
2: In FindMarkers.default(object = data.use, cells.1 = cells.1, cells.2 = cells.2, :
No features pass logfc.threshold threshold; returning empty data.frame
How can I solve this problem?

hi developer, thank you so much for your helpful tools.
I tried to use irGSEA to calculate the GO -BP geneset enrichment scores. But when I calculated the enrichment score as shown below:
skin.GO.BP <- irGSEA.score(object = skin.combined, assay = "RNA", slot = "data", seeds = 123, ncores = 1, min.cells = 3, min.feature = 0, custom = F, geneset = NULL, msigdb = T, species = "Mus musculus", category = "C5", subcategory = "GO:BP", geneid = "symbol", method = c("AUCell", "UCell", "singscore", "ssgsea"), aucell.MaxRank = NULL, ucell.MaxRank = NULL, kcdf = 'Gaussian')
the reaction was:

Error in calculate_Uscore(m, features = features, maxRank = maxRank, chunk.size = chunk.size, : One or more signatures contain more genes than maxRank parameter.
Increase maxRank parameter or make shorter signatures
Traceback:

1. irGSEA.score(object = skin.combined, assay = "RNA", slot = "data",
   . seeds = 123, ncores = 1, min.cells = 3, min.feature = 0,
   . custom = F, geneset = NULL, msigdb = T, species = "Mus musculus",
   . category = "C5", subcategory = "BP", geneid = "symbol", method = c("AUCell",
   . "UCell", "singscore", "ssgsea"), aucell.MaxRank = NULL,
   . ucell.MaxRank = NULL, kcdf = "Gaussian")
2. UCell::ScoreSignatures_UCell(matrix = my.matrix, features = h.gsets.list,
   . maxRank = ucell.MaxRank, w_neg = 1, ncores = ncores, force.gc = T)
3. calculate_Uscore(m, features = features, maxRank = maxRank, chunk.size = chunk.size,
   . w_neg = w_neg, ties.method = ties.method, ncores = ncores,
   . BPPARAM = BPPARAM, force.gc = force.gc, name = name)
4. stop("One or more signatures contain more genes than maxRank parameter.\n Increase maxRank parameter or make shorter signatures")

I am confused because I used a similar code to get the hallmark and KEGG results.
Could you give me any suggestions?
Or does anyone have an idea as to why this occurs?

Dear. Drs,

I am having trouble with the irGSEA.integrate for DEG calculation. The irGSEA.integrate function suddenly spit out
Error in UseMethod("filter") :
no applicable method for 'filter' applied to an object of class "NULL" even if I didn't change anything on my working environment. Would you kindly help me get through this issue?

I have checked

1. I have group.by element in the [email protected]
2. I increased the memory option in the global ( options(future.globals.maxSize=51200000*1024^2)
3. I reinstalled the tidyverse & dplyr
4. I reinstalled irGSEA package along with the all the required attachments according to the tutorial
5. as suggested in other Q&A, I have tried FindAllMarkers function and it gaves DEG result without errors.

a <- Seurat::FindAllMarkers(object = seurat_integrated, assay = "AUCell",
slot = "scale.data",
test.use = "wilcox", min.pct = -Inf, logfc.threshold = 0,
min.cells.group = 0, min.diff.pct = -Inf, verbose = F,
min.cells.feature = 0)

Here is my sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3; LAPACK version 3.10.0

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 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 LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: America/Los_Angeles
tzcode source: system (glibc)

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

other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.0
[10] tidyverse_2.0.0 irGSEA_3.2.3 Biobase_2.62.0 BiocGenerics_0.48.1

loaded via a namespace (and not attached):
[1] ProtGenerics_1.34.0 matrixStats_1.2.0 spatstat.sparse_3.0-3 bitops_1.0-7 httr_1.4.7 RColorBrewer_1.1-3
[7] doParallel_1.0.17 tools_4.3.2 sctransform_0.4.1 backports_1.4.1 utf8_1.2.4 R6_2.5.1
[13] rgdal_1.6-7 uwot_0.1.16 lazyeval_0.2.2 GetoptLong_1.0.5 withr_3.0.0 sp_2.1-3
[19] prettyunits_1.2.0 gridExtra_2.3 progressr_0.14.0 cli_3.6.2 spatstat.explore_3.2-6 fastDummies_1.7.3
[25] network_1.18.2 Seurat_5.0.2 spatstat.data_3.0-4 ggridges_0.5.6 pbapply_1.7-2 Rsamtools_2.18.0
[31] systemfonts_1.0.6 svglite_2.1.3 parallelly_1.37.1 limma_3.58.1 rstudioapi_0.15.0 RSQLite_2.3.5
[37] FNN_1.1.4 generics_0.1.3 shape_1.4.6.1 BiocIO_1.12.0 spatstat.random_3.2-3 ica_1.0-3
[43] car_3.1-2 Matrix_1.6-5 fansi_1.0.6 S4Vectors_0.40.2 abind_1.4-5 terra_1.7-71
[49] lifecycle_1.0.4 yaml_2.3.8 carData_3.0-5 SummarizedExperiment_1.32.0 SparseArray_1.2.4 BiocFileCache_2.10.1
[55] Rtsne_0.17 grid_4.3.2 blob_1.2.4 promises_1.2.1 crayon_1.5.2 miniUI_0.1.1.1
[61] lattice_0.22-5 cowplot_1.1.3 GenomicFeatures_1.54.3 KEGGREST_1.42.0 sna_2.7-2 pillar_1.9.0
[67] ComplexHeatmap_2.18.0 GenomicRanges_1.54.1 rjson_0.2.21 CellChat_1.6.1 future.apply_1.11.1 codetools_0.2-19
[73] leiden_0.4.3.1 glue_1.7.0 data.table_1.15.2 vctrs_0.6.5 png_0.1-8 spam_2.10-0
[79] gtable_0.3.4 cachem_1.0.8 S4Arrays_1.2.1 mime_0.12 coda_0.19-4.1 survival_3.5-8
[85] SingleCellExperiment_1.24.0 iterators_1.0.14 statmod_1.5.0 ellipsis_0.3.2 fitdistrplus_1.1-11 ROCR_1.0-11
[91] nlme_3.1-164 bit64_4.0.5 progress_1.2.3 filelock_1.0.3 RcppAnnoy_0.0.22 GenomeInfoDb_1.38.7
[97] irlba_2.3.5.1 KernSmooth_2.23-22 colorspace_2.1-0 DBI_1.2.2 raster_3.6-26 tidyselect_1.2.0
[103] bit_4.0.5 compiler_4.3.2 curl_5.2.1 BiocNeighbors_1.20.2 xml2_1.3.6 DelayedArray_0.28.0
[109] plotly_4.10.4 rtracklayer_1.62.0 checkmate_2.3.1 scales_1.3.0 lmtest_0.9-40 NMF_0.27
[115] rappdirs_0.3.3 goftest_1.2-3 digest_0.6.34 presto_1.0.0 spatstat.utils_3.0-4 XVector_0.42.0
[121] htmltools_0.5.7 pkgconfig_2.0.3 MatrixGenerics_1.14.0 dbplyr_2.4.0 fastmap_1.1.1 ensembldb_2.26.0
[127] rlang_1.1.3 GlobalOptions_0.1.2 htmlwidgets_1.6.4 shiny_1.8.0 zoo_1.8-12 jsonlite_1.8.8
[133] BiocParallel_1.36.0 statnet.common_4.9.0 RCurl_1.98-1.14 magrittr_2.0.3 GenomeInfoDbData_1.2.11 ggnetwork_0.5.13
[139] dotCall64_1.1-1 patchwork_1.2.0 munsell_0.5.0 Rcpp_1.0.12 reticulate_1.35.0 stringi_1.8.3
[145] ggalluvial_0.12.5 zlibbioc_1.48.0 MASS_7.3-60.0.1 plyr_1.8.9 parallel_4.3.2 listenv_0.9.1
[151] ggrepel_0.9.5 deldir_2.0-4 Biostrings_2.70.2 splines_4.3.2 tensor_1.5 hms_1.1.3
[157] circlize_0.4.16 locfit_1.5-9.9 igraph_2.0.2 ggpubr_0.6.0 spatstat.geom_3.2-9 ggsignif_0.6.4
[163] RcppHNSW_0.6.0 rngtools_1.5.2 reshape2_1.4.4 biomaRt_2.58.2 stats4_4.3.2 XML_3.99-0.16.1
[169] SeuratObject_5.0.1 tzdb_0.4.0 foreach_1.5.2 httpuv_1.6.14 polyclip_1.10-6 RANN_2.6.1
[175] future_1.33.1 clue_0.3-65 scattermore_1.2 gridBase_0.4-7 broom_1.0.5 xtable_1.8-4
[181] restfulr_0.0.15 AnnotationFilter_1.26.0 RSpectra_0.16-1 splatter_1.26.0 rstatix_0.7.2 later_1.3.2
[187] viridisLite_0.4.2 memoise_2.0.1 AnnotationDbi_1.64.1 registry_0.5-1 GenomicAlignments_1.38.2 IRanges_2.36.0
[193] cluster_2.1.6 timechange_0.3.0 globals_0.16.3

and traceback()

4: dplyr::filter(., p_val_adj <= 0.05)
3: dplyr::select(., c("avg_diff", "cluster", "gene", "methods"))
2: deg.geneset %>% dplyr::filter(p_val_adj <= 0.05) %>% dplyr::select(c("avg_diff",
"cluster", "gene", "methods"))
1: irGSEA.integrate(seurat_integrated, group.by = "diet", metadata = NULL,
col.name = NULL, method = c("AUCell", "UCell", "singscore"))

Thank you so much for your great work! The problem is shown in the image:

Hello,
thank you very much for developing such a nice tool!
I was wondering if you think it's ok to use it on a 1000 gene panel dataset such as a CosMX nanostring experiment.
I'm looking forward for your comments.
Thanks, Aurora

Dr. Fan,

Maybe this is irrelevant to irGSEA, probably more related to the Heatmap package but is there anyway that I can keep the pathway order? It seems like even if I use cluster_row=FALSE parameter, irGSEA packages changes the order of the pathways.

So I tried to factor it as below :
pathway_figure <- factor(pathway_figure, levels= c("GOBP-PANCREATIC-A-CELL-DIFFERENTIATION",
"GOMF-LIGAND-GATED-SODIUM-CHANNEL-ACTIVITY",
"GOBP-REGULATION-OF-CHRONIC-INFLAMMATORY-RESPONSE",
"GOMF-PROTEIN-SEQUESTERING-ACTIVITY",
"GOBP-TRANSDIFFERENTIATION",
"GOBP-AMYLIN-RECEPTOR-SIGNALING-PATHWAY"))

But the result of the heatmap is not in this order, even if I set up cluster_row=FALSE. Is there anyway that I can keep the order as intended? I generally use NULL and set up the top anyway but sometimes I needed to select relevant pathway for the sake of publication.

Thanks a ton in advance!!

运行代码如下
test是一个seurat object
test <- irGSEA.score(object = test, assay = "RNA",
slot = "data", seeds = 123, ncores = 1,
min.cells = 3, min.feature = 0,
custom = F, geneset = NULL, msigdb = T,
species = "Mus musculus", category = "H",
subcategory = NULL, geneid = "symbol",
method = c("AUCell", "UCell", "singscore",
"ssgsea"),
aucell.MaxRank = NULL, ucell.MaxRank = NULL,
kcdf = 'Gaussian')

运行报错：

最后生成的结果

无法没有计算出AUCell的值，查看了一下AUCell是安装上了的，请问是为什么出现这个问题，应该怎么解决呢

版本信息
AUCell 1.16.0
irGSEA 3.1.7
R 4.1.3

When I ran irGSEA.integrate, I got an error.

object = JoinLayers(object)
  object@assays$RNA$data = as(object = object@assays$RNA$data, Class = "dgCMatrix")
  object@assays$RNA$scale.data = as(object = object@assays$RNA$scale.data, Class = "matrix")
  object = irGSEA.score(object = object, species = "Mus musculus", assay = "RNA", slot = "data", category = "H", method = c("AUCell", "UCell", "singscore"))
  result.deg = irGSEA.integrate(object = object, group.by = resolution, method = c("AUCell", "UCell", "singscore"))

The error is listed below:

>   result.deg = irGSEA.integrate(object = object, group.by = resolution, method = c("AUCell", "UCell", "singscore"))
Error in irGSEA.integrate(object = object, group.by = resolution, method = c("AUCell",  : 
  Please imput correct `method`.

Idents(sObj) <- sObj$monaco.main
sObj <- irGSEA.score(object = sObj, assay = "RNA", 
                             slot = "data", seeds = 123, ncores = 1,
                             min.cells = 3, min.feature = 0,
                             custom = F, geneset = NULL, msigdb = T, 
                             species = "Homo sapiens", category = "H",  
                             subcategory = NULL, geneid = "symbol",
                             method = c("UCell"),
                             aucell.MaxRank = NULL, ucell.MaxRank = NULL, 
                             kcdf = 'Gaussian')

ridgeplot <- irGSEA.ridgeplot(object = sObj,
                              method = "UCell",
                              show.geneset = "HALLMARK-INFLAMMATORY-RESPONSE")
ridgeplot

这是我的代码，当使用 seurat 计算的 cluster 作为 idents 时出图是正常的，但是 idents 设定为细胞类型后出图报错

Picking joint bandwidth of 0.00332
Error in `[[<-.data.frame`(`*tmp*`, i, value = c("#5050FFFF", "#CE3D32FF",  : 
  替换数据里有10行，但数据有11

pbmc3k.final <- irGSEA.score(object = pbmc3k.final, assay = "RNA",

•                          slot = "data", seeds = 123, ncores = 5,
  

•                          min.cells = 3, min.feature = 0,
  

•                          custom = F, geneset = NULL, msigdb = T,
  

•                          species = "Homo sapiens", category = "H",  
  

•                          subcategory = NULL, geneid = "symbol",
  

•                          method = c("AUCell", "UCell", "singscore","ssgsea"),
  

•                          aucell.MaxRank = NULL, ucell.MaxRank = NULL,
  

•                          kcdf = 'Gaussian')
  

Validating object structure
Updating object slots
Ensuring keys are in the proper strucutre
Ensuring feature names don't have underscores or pipes
Object representation is consistent with the most current Seurat version
Calculate AUCell scores
Error in .local(exprMat, plotStats, nCores, mctype, keepZeroesAsNA, verbose, :
unused argument (splitByBlocks = TRUE)

运行result.dge<- irGSEA.integrate(object = scRNAsub,
group.by = "celltype",
metadata = scRNAsub$celltype, col.name = "ident",
method = c("UCell", "singscore",
'ssgsea'))
后出现Calculate differential gene set : UCell
Calculate differential gene set : singscore
Calculate differential gene set : ssgsea
Error in UseMethod("filter") :
no applicable method for 'filter' applied to an object of class "NULL"。 检查数据没有数据是空的。
使用的dplyr 是1.1.3。是不是需要降版本？

Seurat==5.1.0, SeuratObject==5.0.2, irGESA==3.2.2, dplyr==1.1.4

library(Seurat)
library(SeuratData)
library(RcppML)
library(irGSEA)
data("pbmc3k.final")
# 基因集打分
pbmc3k.final <- irGSEA.score(object = pbmc3k.final,assay = "RNA", 
                             slot = "data", seeds = 123, ncores = 1,
                             min.cells = 3, min.feature = 0,
                             custom = F, geneset = NULL, msigdb = T, 
                             species = "Homo sapiens", category = "H",  
                             subcategory = NULL, geneid = "symbol",
                             method = c("AUCell"),
                             aucell.MaxRank = NULL, ucell.MaxRank = NULL, 
                             kcdf = 'Gaussian')
result.dge <- irGSEA.integrate(object = pbmc3k.final,
                               group.by = "seurat_annotations",
                               method = c("AUCell"))

with error:

Calculate differential gene set : AUCell
Error in UseMethod("distinct") : 
  no applicable method for 'distinct' applied to an object of class "NULL"

Why did encounter an issue？What can I do？

After updating to R 4.4.0 and using irGSEA.score with method = "ssGSEA", I get the following error:
Error: Calling gsva(expr=., gset.idx.list=., method=., ...) is defunct; use a method-specific parameter object (see '?gsva').

HI Chuiqin,
I met a problem like below when I run your tutorial, could you help me debug this issue? Thanks a ton!

irGSEA.density.scatterplot(object = pbmc3k.final, method = "UCell", show.geneset = "HALLMARK-INFLAMMATORY-RESPONSE", reduction = "umap")
Error in as.vector(x, "character") :
cannot coerce type 'environment' to vector of type 'character'

Hi!

I want to run the pipeline for my data with multiple conditions. Eg. my data has like fibroblast, adipocytes, etc. but also treated vs. control. Where is the parameter to input an additional condition?

Heres my code so far:

test <- irGSEA.score(object = Ma_2023, assay = "SCT", 
                             slot = "data", seeds = 123, ncores = 1,
                             min.cells = 3, min.feature = 0,
                             custom = F, geneset = NULL, msigdb = T, 
                             species = "Homo sapiens", category = "H",  
                             subcategory = NULL, geneid = "symbol",
                             method = c("AUCell", "UCell", "singscore", 
                                        "ssgsea"),
                             aucell.MaxRank = NULL, ucell.MaxRank = NULL, 
                             kcdf = 'Gaussian')

result.dge <- irGSEA.integrate(object = test, 
                               group.by = "Author_Provided_Clusters_LVL1", # this is the cluster names
                               metadata = NULL, col.name = NULL,
                               method = c("AUCell","UCell","singscore",
                                          "ssgsea"))

您好，很感谢能有这么一个整合好的R包直接使用，我有几个问题。1.irGSEA.score中的category = "H"，其中H是否可以修改为C2,C5等以便分析GO/KEGG通路，能提供一下应该如何修改？2.我看简书中提到了9种方法，包括AddModuleScore，Z-score等，后期是否因为需要考虑样本组成而被排除了，目前仅使用"AUCell", "UCell", "singscore", "ssgsea"4种方法，谢谢

I have a strange error that I cannot seem to fix myself. I calculate irGSEA.score on a seurat object with 4 clusters. My problem is that when I change identity of the seurat object to labels from a metadata column, where two clusters have the same label, I no longer see the direction in the heatmap.
I use the following code:

`seu_obj <- irGSEA.score(object = seu_obj, assay = "RNA",
slot = "data", seeds = 123, ncores = 1,
min.cells = 3, min.feature = 0,
custom = T, geneset = metabolism.go, geneid = "symbol",
method = c("AUCell", "UCell", "singscore",
"ssgsea"),
aucell.MaxRank = NULL, ucell.MaxRank = NULL,
kcdf = 'Gaussian')

result.dge <- irGSEA.integrate(object = seu_obj,
group.by = NULL,
metadata = NULL, col.name = NULL,
method = c("AUCell","UCell", "singscore", "ssgsea"))

irGSEA.heatmap.plot <- irGSEA.heatmap(object = result.dge,
method = "UCell",
top = 50,
show.geneset = NULL,
cluster_rows = FALSE)
irGSEA.heatmap.plot`

This is with 4 clusters:

This is with 3 groups:

The only difference is whether I use Idents() to change identity.

Does anyone have an idea to why this occurs?

window中运行时报错停止

> pbmc3k.final <- irGSEA.score(object = sc, assay = "RNA", 
+                              slot = "data", seeds = 123, ncores = 10,
+                              min.cells = 3, min.feature = 0,
+                              custom = F, geneset = NULL, msigdb = T, 
+                              species = "Homo sapiens", category = "C2", subcategory="CP:KEGG", geneid = "symbol",
+                              method = c("JASMINE"),
+                              aucell.MaxRank = NULL, ucell.MaxRank = NULL, 
+                              kcdf = 'Gaussian')
Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Ensuring keys are in the proper structure
Ensuring feature names don't have underscores or pipes
Updating slots in RNA
Validating object structure for Assay5 ‘RNA’
Object representation is consistent with the most current Seurat version
Calculate JASMINE scores
Warning messages:
1: Layer ‘data’ is empty 
2: In BiocParallel::MulticoreParam(workers = ncores) :
  MulticoreParam() not supported on Windows, use SnowParam()

I encountered an issure when I used this r package. No assay information could be found for ScoreJackStraw
Setting assay used for FindNeighbors.RNA.pca to RNA
No assay information could be found for FindClusters

devtools::install_github('satijalab/seurat-data')

library(SeuratData)
-- Installed datasets ----------------------------------------------------------------------------- SeuratData v0.2.2.9001 --
v pbmc3k 3.1.4

------------------------------------------------------------ Key ------------------------------------------------------------
v Dataset loaded successfully

Dataset built with a newer version of Seurat than installed
(?) Unknown version of Seurat installed

# view all available datasets

View(AvailableData())

download 3k PBMCs from 10X Genomics

InstallData("pbmc3k")
Warning: The following packages are already installed and will not be reinstalled: pbmc3k

the details of pbmc3k.final

?pbmc3k.final

library(Seurat)
载入需要的程辑包：SeuratObject
载入需要的程辑包：sp

载入程辑包：‘SeuratObject’

The following object is masked from ‘package:base’:

intersect

library(SeuratData)

loading dataset

data("pbmc3k.final")
pbmc3k.final <- UpdateSeuratObject(pbmc3k.final)
Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Updating matrix keys for DimReduc ‘pca’
Updating matrix keys for DimReduc ‘umap’
Warning: Assay RNA changing from Assay to Assay
Warning: Graph RNA_nn changing from Graph to Graph
Warning: Graph RNA_snn changing from Graph to Graph
Warning: DimReduc pca changing from DimReduc to DimReduc
Warning: DimReduc umap changing from DimReduc to DimReduc
Ensuring keys are in the proper structure
Ensuring feature names don't have underscores or pipes
Updating slots in RNA
Updating slots in RNA_nn
Setting default assay of RNA_nn to RNA
Updating slots in RNA_snn
Setting default assay of RNA_snn to RNA
Updating slots in pca
Updating slots in umap
Setting umap DimReduc to global
Setting assay used for NormalizeData.RNA to RNA
Setting assay used for FindVariableFeatures.RNA to RNA
Setting assay used for ScaleData.RNA to RNA
Setting assay used for RunPCA.RNA to RNA
Setting assay used for JackStraw.RNA.pca to RNA
No assay information could be found for ScoreJackStraw
Setting assay used for FindNeighbors.RNA.pca to RNA
No assay information could be found for FindClusters
Setting assay used for RunUMAP.RNA.pca to RNA
Validating object structure for Assay ‘RNA’
Validating object structure for Graph ‘RNA_nn’
Validating object structure for Graph ‘RNA_snn’
Validating object structure for DimReduc ‘pca’
Validating object structure for DimReduc ‘umap’
Object representation is consistent with the most current Seurat version
Warning messages:
1: Adding a command log without an assay associated with it
2: Adding a command log without an assay associated with it
sessionInfo()
R version 4.1.3 (2022-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936 LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936

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

other attached packages:
[1] Seurat_5.0.1 SeuratObject_5.0.1 sp_1.6-0 pbmc3k.SeuratData_3.1.4 SeuratData_0.2.2.9001

loaded via a namespace (and not attached):
[1] Rtsne_0.16 ggbeeswarm_0.7.1 colorspace_2.1-0 deldir_1.0-6 ellipsis_0.3.2
[6] ggridges_0.5.4 snakecase_0.11.1 circlize_0.4.15 GlobalOptions_0.1.2 RcppHNSW_0.4.1
[11] rstudioapi_0.15.0 spatstat.data_3.0-1 scCustomize_2.0.1 leiden_0.4.3 listenv_0.9.0
[16] ggrepel_0.9.3 lubridate_1.9.2 RSpectra_0.16-1 fansi_1.0.4 codetools_0.2-19
[21] splines_4.1.3 polyclip_1.10-4 spam_2.9-1 jsonlite_1.8.4 ica_1.0-3
[26] cluster_2.1.4 png_0.1-8 uwot_0.1.14 ggprism_1.0.4 shiny_1.7.4.1
[31] sctransform_0.4.1 spatstat.sparse_3.0-1 compiler_4.1.3 httr_1.4.7 Matrix_1.6-4
[36] fastmap_1.1.1 lazyeval_0.2.2 cli_3.6.1 later_1.3.0 htmltools_0.5.4
[41] tools_4.1.3 igraph_1.4.2 dotCall64_1.0-2 gtable_0.3.4 glue_1.6.2
[46] RANN_2.6.1 reshape2_1.4.4 dplyr_1.1.2 rappdirs_0.3.3 Rcpp_1.0.10
[51] scattermore_1.2 vctrs_0.6.5 spatstat.explore_3.1-0 nlme_3.1-162 progressr_0.13.0
[56] lmtest_0.9-40 spatstat.random_3.1-4 stringr_1.5.1 globals_0.16.2 timechange_0.2.0
[61] mime_0.12 miniUI_0.1.1.1 lifecycle_1.0.4 irlba_2.3.5.1 goftest_1.2-3
[66] future_1.33.0 MASS_7.3-58.3 zoo_1.8-11 scales_1.2.1 promises_1.2.0.1
[71] spatstat.utils_3.0-2 parallel_4.1.3 rematch2_2.1.2 RColorBrewer_1.1-3 reticulate_1.28
[76] pbapply_1.7-2 gridExtra_2.3 ggplot2_3.4.4 ggrastr_1.0.1 stringi_1.7.12
[81] paletteer_1.5.0 fastDummies_1.7.3 shape_1.4.6 rlang_1.1.2 pkgconfig_2.0.3
[86] matrixStats_0.63.0 lattice_0.20-45 ROCR_1.0-11 purrr_1.0.2 tensor_1.5
[91] patchwork_1.1.3 htmlwidgets_1.6.2 cowplot_1.1.1 tidyselect_1.2.0 parallelly_1.36.0
[96] RcppAnnoy_0.0.20 plyr_1.8.8 magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[101] pillar_1.9.0 fitdistrplus_1.1-11 survival_3.5-5 abind_1.4-5 tibble_3.2.1
[106] future.apply_1.11.0 crayon_1.5.2 janitor_2.2.0 KernSmooth_2.23-20 utf8_1.2.3
[111] spatstat.geom_3.1-0 plotly_4.10.1 grid_4.1.3 data.table_1.14.8 forcats_1.0.0
[116] digest_0.6.31 xtable_1.8-4 tidyr_1.3.0 httpuv_1.6.9 munsell_0.5.0
[121] beeswarm_0.4.0 viridisLite_0.4.2 vipor_0.4.5

what's wrong for it? Thank you!!!!

I tried to use "scatterplot <- irGSEA.density.scatterplot(object,split.by = "orig.ident",
method = "UCell",
show.geneset = "HALLMARK-INFLAMMATORY-RESPONSE",
reduction = "umap")"
but i failed.
what can i do to improve?
Thanks!

I tried to use "scatterplot <- irGSEA.density.scatterplot(object,split.by = "orig.ident",
method = "UCell",
show.geneset = "HALLMARK-INFLAMMATORY-RESPONSE",
reduction = "umap")"
but i failed.
what can i do to improve?
Thanks!

Hi, I found the default comparison is to FindAllMarkers, but I want to compare two certain groups or compare some groups to one same group.
For example, there are five groups of cells in my single-cell Seurat dataset, i.e., A, B, C, D, and E. I want to compare E vs. A, E vs. B, E vs. C, and E vs. D. Is it possible to do this comparison, and get the consensus p-values/adjusted p-values? Thank you.

在跑irGSEA.score过程中，AUcell评分这步因为某个参数而直接跳过了，加入报错提示的参数又提示不存在splitByBlocks=T这种参数设置，输出结果当然就只有UCell、singscore和ssGSEA，虽然所得结果已经够用，但还是想搞清楚为什么会出现这个报错。

版本：
服务器R4.1.0
irGSEA 3.2.6
dplyr 1.1.6

作者你好，很感谢你开发的包。在使用的时候出现一个问题，想请教一下。
method里添加了ssgsea一直报错，R4.2版本，GSVA1.44报错，1.45报错。1.4报错。取消了ssgsea就好了。
想请教一下如何修复这个问题，谢谢谢！
pbmc3k.final1 <- irGSEA.score(object =pbmc3k.final, assay = "RNA",

•                          slot = "data", seeds = 123, ncores = 1,
  

•                          min.cells = 3, min.feature = 0,
  

•                          custom = F, geneset = NULL, msigdb = T,
  

•                          species = "Homo sapiens", category = "H",
  

•                          subcategory = NULL, geneid = "symbol",
  

•                          method = c( "AUCell","UCell", "singscore","ssgsea"
  

•                                     ),
  

•                          aucell.MaxRank = NULL, ucell.MaxRank = NULL,
  

•                          kcdf = 'Gaussian')
  

Validating object structure
Updating object slots
Ensuring keys are in the proper strucutre
Ensuring feature names don't have underscores or pipes
Object representation is consistent with the most current Seurat version
Calculate AUCell scores
Warning: Feature names cannot have underscores (''), replacing with dashes ('-')
Warning: Feature names cannot have underscores (''), replacing with dashes ('-')
Finish calculate AUCell scores
Calculate UCell scores
Warning: Feature names cannot have underscores (''), replacing with dashes ('-')
Warning: Feature names cannot have underscores (''), replacing with dashes ('-')
Finish calculate UCell scores
Calculate singscore scores
Warning: Feature names cannot have underscores (''), replacing with dashes ('-')
Warning: Feature names cannot have underscores (''), replacing with dashes ('-')
Finish calculate singscore scores
Calculate ssgsea scores
[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
Error in as(es, "dMatrix") :
没有可以用来強制转换“matrix”成“dMatrix”的方法或默认函数 #主要的报错代码
此外: Warning messages:
1: In .AUCell_buildRankings(exprMat = exprMat, featureType = featureType, :
nCores is no longer used. It will be deprecated in the next AUCell version.
2: In .local(expr, gset.idx.list, ...) :
Using 'dgCMatrix' objects as input is still in an experimental stage.
3: In .filterFeatures(expr, method) :
1 genes with constant expression values throuhgout the samples.

谢谢！

用的seurat.V5对象,进行时出现报错，请问该如何解决呢？麻烦了

a strang bug of irGSEA.score when using AddModuleScore
I do not know why


Together, it doesn't work

Hi,
I want to know the mean of irGSEA.integrate result, the column "direction" which have two levels, how can you define up or down? Is it compare to median of the score

Hello everyone,

I tried to install irGSEA in Windows 10 system, but got the following error:

ERROR: dependencies 'AUCell', 'ComplexHeatmap', 'decoupleR', 'ggtree', 'Nebulosa', 'singscore' are not available for package 'irGSEA'
* removing 'C:/R/R-4.2.2/library/irGSEA'
Warning messages:
1: packages ‘singscore’, ‘Nebulosa’, ‘ggtree’, ‘decoupleR’, ‘ComplexHeatmap’, ‘AUCell’ are not available for this version of R

Versions of these packages for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages 
2: In file.copy(savedcopy, lib, recursive = TRUE) :
  problem copying C:\R\R-4.2.2\library\00LOCK\cli\libs\x64\cli.dll to C:\R\R-4.2.2\library\cli\libs\x64\cli.dll: Permission denied
3: In file.copy(savedcopy, lib, recursive = TRUE) :
  problem copying C:\R\R-4.2.2\library\00LOCK\fastmap\libs\x64\fastmap.dll to C:\R\R-4.2.2\library\fastmap\libs\x64\fastmap.dll: Permission denied
4: In file.copy(savedcopy, lib, recursive = TRUE) :
  problem copying C:\R\R-4.2.2\library\00LOCK\htmltools\libs\x64\htmltools.dll to C:\R\R-4.2.2\library\htmltools\libs\x64\htmltools.dll: Permission denied
5: In i.p(...) :
  installation of package ‘C:/Users/ys738/AppData/Local/Temp/RtmpSuQ8C1/file28ac5c0453f/irGSEA_2.1.3.tar.gz’ had non-zero exit status

Do you have any idea of how to fix this?

Best regards and many thanks!

irGSEA.heatmap.plot <- irGSEA.heatmap(object = result.dge,

•                                   method = "ssgsea",
  

•                                   top = 50,
  

•                                   show.geneset = NULL)
  

Error in chr_as_locations():
! Can't rename columns that don't exist.
x Column p_val_adj doesn't exist.
Run rlang::last_error() to see where the error occurred.

irGSEA.bubble.plot <- irGSEA.bubble(object = result.dge,

•                                 method = "ssgsea",
  

•                                 show.geneset = NULL,
  

•                                 top = 50)
  

Error in chr_as_locations():
! Can't rename columns that don't exist.
x Column p_val_adj doesn't exist.
Run rlang::last_error() to see where the error occurred.

ps, My 'result.dge' contains the p_val_adj

How to fix this error? THX

Hi,
Thanks for your cool tools. But I found some error in document of install:

# install packages from Bioconductor
bioconductor.packages <- c("AUCell", "BiocParallel", "ComplexHeatmap", 
                           "decoupleR", "fgsea", "ggtree", "GSEABase", 
                           "GSVA", "Nebulosa", "scde", "singscore",
                           "SummarizedExperiment", "UCell",
                           "viper","sparseMatrixStats")

for (i in bioconductor.packages) {
  if (!requireNamespace(i, quietly = TRUE)) {
    install.packages(i, ask = F, update = F)
  }
}

The function of install.packages could not install the bioconductor packages. There should be Biocmanager::install()

I have a require of calculating the differential gene set of 2 given cell groups by irGSEA，
how can i get it

Hi Chuiqin,
Thank you so much for providing us with a valuable tool and the accompanying tutorials. They have been incredibly helpful.
I am curious about the tool development roadmap, specifically regarding to possibility of conducting comparisons between different samples integrated within a single V5 Seurat object in the near future. I am eager to know if it might be available.
Look forward to your reply and updates on this matter. Thanks again!

Hi developer,
Thanks develop the great tools.
The error showing up when running the irGSEA, the error lying below:
Registered S3 method overwritten by 'spatstat.geom':
method from
print.boxx cli
Attaching SeuratObject
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Validating object structure
Updating object slots
Ensuring keys are in the proper strucutre
Ensuring feature names don't have underscores or pipes
Object representation is consistent with the most current Seurat version
Calculate AUCell scores
Finish calculate AUCell scores
Calculate UCell scores
Finish calculate UCell scores
Calculate singscore scores
Finish calculate singscore scores
Calculate ssgsea scores
Finish calculate ssgsea scores
Calculate differential gene set : AUCell
Calculate differential gene set : UCell
Calculate differential gene set : singscore
Calculate differential gene set : ssgsea
Mutate "has no methods that apply to the target object of "list"
Calls: irGSEA.integrate -> %>% ->

R.version
_
platform x86_64-pc-linux-gnu
arch x86_64
os linux-gnu
system x86_64, linux-gnu
status
major 4
minor 1.2
year 2021
month 11
day 01
svn rev 81115
language R
version.string R version 4.1.2 (2021-11-01)
nickname Bird Hippie

Had any idea for this issue?
Best,
hanhuihong

irGSEA.score(

• object = all_revised,
• assay = "RNA",
• slot = "data",
• min.cells = 3,
• min.feature = 0,
• seeds = 123,
• ncores = 10,
• custom = F,
• geneset = NULL,
• msigdb = T,
• species = "Homo sapiens",
• category = "C5",
• subcategory = NULL,
• geneid = "symbol",
• method = c("AUCell", "UCell", "singscore", "ssgsea"),
• aucell.MaxRank = 5000,
• ucell.MaxRank = 5000,
• kcdf = "Gaussian"
• )

Validating object structure
Updating object slots
Ensuring keys are in the proper strucutre
Ensuring feature names don't have underscores or pipes
Object representation is consistent with the most current Seurat version
No genes remaining in following genesets: GOBP_FLAVONE_METABOLIC_PROCESS, GOBP_FLAVONOID_GLUCURONIDATION, GOBP_FORMATION_OF_QUADRUPLE_SL_U4_U5_U6_SNRNP, GOBP_NEGATIVE_REGULATION_OF_CELL_CHEMOTAXIS_TO_FIBROBLAST_GROWTH_FACTOR, GOBP_NEGATIVE_REGULATION_OF_INTERLEUKIN_21_PRODUCTION, GOBP_NEGATIVE_REGULATION_OF_MYOBLAST_PROLIFERATION, GOBP_NEGATIVE_REGULATION_OF_VASCULAR_ENDOTHELIAL_GROWTH_FACTOR_PRODUCTION, GOBP_REGULATION_OF_PRESYNAPTIC_MEMBRANE_POTENTIAL, GOBP_RETINOIC_ACID_CATABOLIC_PROCESS, GOBP_XENOBIOTIC_GLUCURONIDATION, GOMF_ESTROGEN_2_HYDROXYLASE_ACTIVITY, GOMF_GLYCINE_N_ACYLTRANSFERASE_ACTIVITY, GOMF_TRACE_AMINE_RECEPTOR_ACTIVITY, GOMF_TYPE_I_INTERFERON_RECEPTOR_BINDING, HP_HYPERCHLORIDURIA, HP_ORTHOKERATOTIC_HYPERKERATOSIS
Calculate AUCell scores
Error in .local(exprMat, plotStats, nCores, mctype, keepZeroesAsNA, verbose, :
unused argument (splitByBlocks = TRUE)

when I run it with 1.13 , it shows a unsued argument which did not show in version= 1.12