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After running PeacoQC, I got errors for training the the generated train files when using Cytonorm. I had no problem for training and normalizing the original fcs files. I am wondering if Cytonorm is suitable for normalizing the PeacoQC-corrected fcs files. It will be much appreciated if you have any clues.
The errors I got:
Splitting /Users/wswimmune/Documents/Work/Postdoc/Experiments/CRUSTY/CD56dimCD16poNK/PeacoQCresults/PeacoQC_results/fcs_files/Con018_1_QC_1.fcs
Error in newdata[, colnames(codes)] : subscript out of bounds
In addition: Warning message:
In CytoNorm.train(files = train_data$Path, labels = train_data$Batch, :
Reusing FlowSOM result previously saved at ./tmp/CytoNorm_FlowSOM.RDS
Hi,
Apologies for the late reply. It should give no problem to apply CytoNorm after PeacoQC.
In the error message, it states that the previous FlowSOM model is reused, can it be that there is a mismatch between the channels used in the FlowSOM model and the channels you want to normalize now?
If you don't want to reuse the previous FlowSOM model, you can either specify another outputDir, make use of the recompute parameter or move the FlowSOM object in the file manager.
Hi there,
After running PeacoQC, I got errors for training the the generated train files when using Cytonorm. I had no problem for training and normalizing the original fcs files. I am wondering if Cytonorm is suitable for normalizing the PeacoQC-corrected fcs files. It will be much appreciated if you have any clues.
The errors I got:
Splitting /Users/wswimmune/Documents/Work/Postdoc/Experiments/CRUSTY/CD56dimCD16poNK/PeacoQCresults/PeacoQC_results/fcs_files/Con018_1_QC_1.fcs
Error in newdata[, colnames(codes)] : subscript out of bounds
In addition: Warning message:
In CytoNorm.train(files = train_data$Path, labels = train_data$Batch, :
Reusing FlowSOM result previously saved at ./tmp/CytoNorm_FlowSOM.RDS
traceback()
3: MapDataToCodes(fsom$map$codes, fsom_new$data)
2: FlowSOM::NewData(fsom, ff)
1: CytoNorm.train(files = train_data$Path, labels = train_data$Batch,
channels = markerstotransform, transformList = NULL, truncate_max_range = FALSE,
FlowSOM.params = list(nCells = 6000, xdim = 5, ydim = 5,
nClus = 10, scale = FALSE), normMethod.train = QuantileNorm.train,
normParams = list(nQ = 101, goal = "mean"), seed = 1, verbose = TRUE)
Best wishes
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