CODEWITHSUNDEEP
rbioinformaticsseurat
montyman14
montyman14

Reputation: 65

FindVariableFeatures Function in Seurat Producing "Error in match(x, table, nomatch = 0L) : 'match' requires vector arguments"

I am running Seurat V3 in RStudio and attempting to run PCA on a newly subsetted object. As part of that process, I am using the commands:

 tnk.cells <- FindVariableFeatures(tnk.cells, assay = "RNA", selection.method = "vst", nfeatures = 2000)
 tnk.cells <- RunPCA(tnk.cells, verbose = TRUE, npcs = 30, features = FindVariableFeatures(tnk.cells))

The first process seems to work, but I am unsure if it actually did, and if so, whether I need to specify that "features" in the second command should refer to those features. Either way, every time I attempt to run the second command, it produces this error, along with three warning messages:

 Error in match(x, table, nomatch = 0L) : 
   'match' requires vector arguments
 In addition: Warning messages:
 1: In FindVariableFeatures.Assay(object = assay.data, selection.method = selection.method,  :
   selection.method set to 'vst' but count slot is empty; will use data slot instead
 2: In eval(predvars, data, env) : NaNs produced
 3: In hvf.info$variance.expected[not.const] <- 10^fit$fitted :
    number of items to replace is not a multiple of replacement length

Does anyone have any idea why these errors/warnings are being produced? I have tried coercing the output of FindVariableFeatures as a vector and a dataframe, to no avail. I also want to ask: do I need to rerun FindVariableFeatures after subsetting a new dataset from a larger one?

Upvotes: 1

Views: 4077

Answers (1)

StupidWolf
StupidWolf

Reputation: 46978

The variable features are already stored in the Seurat object. You can access them using VariableFeatures() , for example:

library(Seurat)
pbmc_small =SCTransform(pbmc_small)
pbmc_small = FindVariableFeatures(pbmc_small,nfeatures=20)
head(VariableFeatures(pbmc_small))
[1] "GNLY"   "PPBP"   "PF4"    "S100A8" "VDAC3"  "CD1C"

Then you can run it is like this, although by default, it will use the variable features stored in the object:

pbmc_small <- RunPCA(pbmc_small,features = VariableFeatures(pbmc_small))

Upvotes: 1

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