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While running sc.metabolism.Seurat function, I encountered an error in "Computing differential signature tests..." step.
It mentioned that Error in [<-.data.frame(*tmp*, , 2, value = 0) :
替换数据里有1行,但数据有0.
The input data was a standard Seurat object and parameters were set by default.
The specific running processes were listed below:
Your choice is: KEGG
Start quantify the metabolism activity...
Loading data from C:/Tools/R/R_Library/scMetabolism/data/KEGG_metabolism_nc.gmt ...
Using 10087/10105 genes detected in 0.10% of cells for signature analysis.
See the `sig_gene_threshold` input to change this behavior.
Beginning Analysis
Computing a latent space for expression data...
Determining projection genes...
Applying Threshold filter...removing genes detected in less than 2043 cells
Genes Retained: 5178
Applying Fano filter...removing genes with Fano < 2.0 MAD in each of 30 bins
Genes Retained: 811
Clustering cells...completed
Projecting data into 2 dimensions...
Running method 1/1: tSNE30 ...
Evaluating signature scores on cells...
as(<matrix>, "dgeMatrix") is deprecated since Matrix 1.5-0; do as(as(as(., "dMatrix"), "generalMatrix"), "unpackedMatrix") instead
|=========================================================================================| 100%, Elapsed 00:01Evaluating signature-gene importance...
|=========================================================================================| 100%, Elapsed 00:01Creating 5 background signature groups with the following parameters:
sigSize sigBalance
1 7 1
2 14 1
3 22 1
4 36 1
5 78 1
signatures per group: 3000
Computing KNN Cell Graph in the Latent Space...
Evaluating local consistency of signatures in latent space...
|=========================================================================================| 100%, Elapsed 00:00Clustering signatures...
Computing differential signature tests...
| | 0%, ETA NAError in `[<-.data.frame`(`*tmp*`, , 2, value = 0) :
替换数据里有1行,但数据有0
In addition: Warning messages:
1: In asMethod(object) :
sparse->dense coercion: allocating vector of size 3.1 GiB
2: In readSignaturesInput(signatures) : NAs introduced by coercion
3: In asMethod(object) :
sparse->dense coercion: allocating vector of size 3.1 GiB
4: In pbmclapply(sigBatches, function(sigBatch) { :
mc.cores > 1 is not supported on Windows due to limitation of mc*apply() functions.
mc.core is set to 1.
5: In pbmclapply(setNames(sigs, sigs), sigGene) :
mc.cores > 1 is not supported on Windows due to limitation of mc*apply() functions.
mc.core is set to 1.
6: In asMethod(object) :
sparse->dense coercion: allocating vector of size 3.1 GiB
7: In pbmclapply(fgSigBatches, function(ii) { :
mc.cores > 1 is not supported on Windows due to limitation of mc*apply() functions.
mc.core is set to 1.
8: In pbmclapply(randomSigBatches, function(randomSigSubset) { :
mc.cores > 1 is not supported on Windows due to limitation of mc*apply() functions.
mc.core is set to 1.
9: In pbmclapply(factorMeta, function(metaName) { :
mc.cores > 1 is not supported on Windows due to limitation of mc*apply() functions.
mc.core is set to 1.
The text was updated successfully, but these errors were encountered:
I had the same error. I solved it by updating VISION to 3.0.1 with the suggestions in #17 using devtools::install_github("YosefLab/VISION"). However, it seems against what is recommended in README.md.
While running sc.metabolism.Seurat function, I encountered an error in "Computing differential signature tests..." step.
It mentioned that Error in
[<-.data.frame
(*tmp*
, , 2, value = 0) :替换数据里有1行,但数据有0.
The input data was a standard Seurat object and parameters were set by default.
The specific running processes were listed below:
The text was updated successfully, but these errors were encountered: