Quantifying metabolism activity at the single-cell resolution
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Error in if (class(A_norm) != "matrix") { : the condition has length > 1 此外: Warning message: In asMethod(object) : sparse->dense coercion: allocating vector of size 5.9 GiB #30
When I ran scMetabolism to quantify metabolic activity ，I met some problem：
library(Seurat)
library(scMetabolism)
scRNA <- readRDS('object.RDS')
countexp.scRNA <- sc.metabolism.Seurat(obj = scRNA, method = "AUCell", imputation = T, ncores = 2, metabolism.type = "KEGG")
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 31053 cells and 25457 genes
Error in if (class(A_norm) != "matrix") { : the condition has length > 1
此外: Warning message:
In asMethod(object) :
sparse->dense coercion: allocating vector of size 5.9 GiB
Is there anyone encounter the same error, and how to solve it? thank you?
When I ran scMetabolism to quantify metabolic activity ，I met some problem： library(Seurat) library(scMetabolism) scRNA <- readRDS('object.RDS') countexp.scRNA <- sc.metabolism.Seurat(obj = scRNA, method = "AUCell", imputation = T, ncores = 2, metabolism.type = "KEGG") 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 31053 cells and 25457 genes Error in if (class(A_norm) != "matrix") { : the condition has length > 1 此外: Warning message: In asMethod(object) : sparse->dense coercion: allocating vector of size 5.9 GiB Is there anyone encounter the same error, and how to solve it? thank you?