ERROR happened in irGSEA.integrate

[xgwb]

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?

[xgwb]

I have run such code : Idents(scRNA) <- scRNA$group

a <- Seurat::FindAllMarkers(object = scRNA, 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)
  

For a (much!) faster implementation of the Wilcoxon Rank Sum Test, (default method for FindMarkers) please install the presto package

install.packages('devtools') devtools::install_github('immunogenomics/presto')

After installation of presto, Seurat will automatically use the more efficient implementation (no further action necessary). This message will be shown once per session 差异通路非常的显著

[chuiqin]

方便分享你的数据吗？可以加我的微信fcq065837测试一下是哪里出了问题。

[xgwb]

我使用了官方pbmc数据，结果如下： result.dge <- irGSEA.integrate(object = pbmc3k.final,

•                            group.by = "seurat_annotations",
  

•                            metadata = NULL, col.name = NULL,
  

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

•                                       "ssgsea", "JASMINE", "viper"))
  

Calculate differential gene set : AUCell Calculate differential gene set : UCell Calculate differential gene set : singscore Calculate differential gene set : ssgsea Calculate differential gene set : JASMINE Calculate differential gene set : viper Error in UseMethod("distinct") : no applicable method for 'distinct' applied to an object of class "NULL" In addition: Warning messages: 1: In mean.fxn(object[features, cells.1, drop = FALSE]) : NaNs produced 2: In mean.fxn(object[features, cells.2, drop = FALSE]) : NaNs produced 3: In mean.fxn(object[features, cells.1, drop = FALSE]) : NaNs produced 4: In mean.fxn(object[features, cells.2, drop = FALSE]) : NaNs produced

[xgwb]

我怀疑是R包安装出错，但是我重装了所有官方要求的包以后仍然报错

[chuiqin]

这让人感觉不可思议，pbmc3k.final示例数据2天前测试过，是没有问题的。 你可以先检查一下是否有seurat_annotations这一列存在，以及随机挑选一个Assay验证一下是否存在，可以这样操作：

unique(pbmc3k.final$seurat_annotations
pbmc3k.final[["AUCell"]]@scale.data[1:2,1:2]

[xgwb]

我运行了这些代码，结果如下： unique(pbmc3k.final$seurat_annotations) [1] Memory CD4 T B CD14+ Mono NK CD8 T Naive CD4 T FCGR3A+ Mono DC Platelet
Levels: Naive CD4 T Memory CD4 T CD14+ Mono B CD8 T FCGR3A+ Mono NK DC Platelet

   pbmc3k.final[["AUCell"]]@scale.data[1:2,1:2]

                         AAACATACAACCAC AAACATTGAGCTAC

HALLMARK-ADIPOGENESIS 0.03405244 0.02949009 HALLMARK-ALLOGRAFT-REJECTION 0.10319892 0.13336782

[chuiqin]

确实让人感觉意外，你可以加我微信号fcq065837，我们仔细排查一下错误，后续错误原因也会公布在这里。

[xgwb]

好的，谢谢您的帮助

[chuiqin]

错误的发生是因为Seurat从5.02开始，增加了一个fc.slot参数。如果你要计算scale.data的差异，不管是FindAllMarkers还是FindMarkers函数，没有指定fc.slot，计算的时候都会出错。恰好irGSEA的打分矩阵都存放在scale.data里面。 我在新版本的irGSEA（3.2.4）里面已经修复了这个错误，只要重新安装即可解决这个问题。

[Taxols1996]

我也遇到了同样的问题，但可能与irGSEA版本无关，而是运行内存。我所用的服务器R版本是4.1.0，irGSEA 3.2.6，dplyr 1.1.6，但仍提示相同的报错。于是我在其他帖子给出的解决方法，设置了options，并转后台运行，成功解决了非版本问题： options(future.globals.maxSize = 100000 * 1024^5) job::job({result.age <- irGSEA.integrate(object = rds, group.by = "age", #根据年龄分组，也可根据cluster_name，或者其他的条件 method = c("UCell","singscore", "ssgsea"),metadata = NULL, col.name = NULL)})