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I am trying to run scDblFinder to find doublets in my scATAC-seq data but run into the following error early on:
Error in names(res) <- nms :
'names' attribute [4] must be the same length as the vector [2]
In addition: Warning message:
stop worker failed:
attempt to select less than one element in OneIndex
From preliminary google searches, this problem seems external to scDblFinder, but any insight you may have will be very helpful.
I am running the following code:
cancer_2_h_new
#An object of class Seurat
#251195 features across 23665 samples within 1 assay
#Active assay: ATAC (251195 features, 251195 variable features) #4 dimensional reductions calculated: lsi, umap, harmony, umap_harmony
Hi,
Thanks for reporting, but please always include the package version with issues!
With multi-threading we won't get very useful error msgs, so could you run it without the BPPARAM argument? Assuming you still encounter the error, use traceback() right after to get its trace.
Pierre-Luc
Usually this kind of cryptic error means that you ran out of memory on one of the worker processes. On my systems, I often observe this because the OS will kill individual workers when R exceeds the allocated memory; this means that some workers will finish and others won't, leading to weird errors like this.
Hello,
I am trying to run scDblFinder to find doublets in my scATAC-seq data but run into the following error early on:
Error in names(res) <- nms :
'names' attribute [4] must be the same length as the vector [2]
In addition: Warning message:
stop worker failed:
attempt to select less than one element in OneIndex
From preliminary google searches, this problem seems external to scDblFinder, but any insight you may have will be very helpful.
I am running the following code:
cancer_2_h_new
#An object of class Seurat
#251195 features across 23665 samples within 1 assay
#Active assay: ATAC (251195 features, 251195 variable features)
#4 dimensional reductions calculated: lsi, umap, harmony, umap_harmony
cancer_sce = as.SingleCellExperiment(cancer_2_h_new)
set.seed(123)
library(scDblFinder)
library(BiocParallel)
sce <- scDblFinder(cancer_sce, samples="Mouse", aggregateFeatures=TRUE, nfeatures=25,BPPARAM=MulticoreParam(3), processing = "normFeatures")
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