Error in if (class(A_norm) != "matrix") { : the condition has length > 1 In addition: Warning message: In asMethod(object) : sparse->dense coercion: allocating vector of size 14.3 GiB

[PingXR]

When I set the imputation parameter to F, I can run the following code normally:
countexp.Seurat<-sc.metabolism.Seurat(obj = countexp.Seurat,
method = "AUCell", # VISION, AUCell, ssgsea, and gsva
imputation = F,
ncores = 2,
metabolism.type = "KEGG" # KEGG and REACTOME
)
But when I set imputation to T, an error will be reported.
Your choice is: KEGG
Start imputation...
Citation: George C. Linderman, Jun Zhao, Yuval Kluger. Zero-preserving imputation of scRNA-seq data using low-rank approximation. bioRxiv. doi: https://doi.org/10.1101/397588
Read matrix with 32534 cells and 59056 genes
Error in if (class(A_norm) != "matrix") { : the condition has length > 1
In addition: Warning message:
In asMethod(object) :
sparse->dense coercion: allocating vector of size 14.3 GiB.
Has anyone encountered a similar error and how to solve it? Thank you very much.

[wangyasen615]

I also meet this problem