Hello,

I am trying to integrate the data by correcting for batch effects per patient and I'm running into this error while executing the IntegrateData function, how do I fix this? Is it because the sepsis2HTO_HAB3 (7th dataset) has too few samples (66) to be properly integrated?

analyseFinalList = function(objlist, intname)
{
  dir.create(intname, recursive = TRUE)

  #
  # integrate based on RNA/GEX assay
  #
  objSamples = objlist
  print(objSamples)

  objSamples = lapply(objSamples, function(x) {
    DefaultAssay(x) <- 'RNA'
    #x <- RunPCA(x, verbose = FALSE, reduction.name="pca",  assay="RNA")
    #  DefaultAssay(x) <- 'SCT'
    #  print(x@reductions$pca)
    return(x)
  })

  print("GEX integration features")
  print(objSamples)

  features_gex <- SelectIntegrationFeatures(object.list = objSamples, nfeatures = 800)#, assay=rep("RNA", length(objSamples)))
  objlist.anchors <- FindIntegrationAnchors(object.list = objSamples,  reduction = "rpca", dims = 1:30, anchor.features = features_gex, reference = 1)
  obj.list.integrated <- IntegrateData(new.assay.name = "integrated_gex", anchorset = objlist.anchors, dims = 1:30, verbose=T) #normalization.method = "SCT",
  print("GEX integration done")
  return(obj.list.integrated)
}

integratedList_sample = analyseFinalList(finalList_samples, "wnn")

[1] "GEX integration features"

$sepsis1HTO_HAB7
An object of class Seurat 
36605 features across 2217 samples within 2 assays 
Active assay: RNA (36601 features, 800 variable features)
1 other assay present: HTO
1 dimensional reduction calculated: pca

$sepsis1HTO_HAB8
An object of class Seurat 
36605 features across 2610 samples within 2 assays 
Active assay: RNA (36601 features, 800 variable features)
1 other assay present: HTO
1 dimensional reduction calculated: pca

$sepsis1HTO_HAB5
An object of class Seurat 
36605 features across 1833 samples within 2 assays 
Active assay: RNA (36601 features, 800 variable features)
1 other assay present: HTO
1 dimensional reduction calculated: pca

$sepsis1HTO_HAB6
An object of class Seurat 
36605 features across 2116 samples within 2 assays 
Active assay: RNA (36601 features, 800 variable features)
1 other assay present: HTO
1 dimensional reduction calculated: pca

$sepsis2HTO_HAB1
An object of class Seurat 
36621 features across 513 samples within 2 assays 
Active assay: RNA (36601 features, 800 variable features)
1 other assay present: HTO
1 dimensional reduction calculated: pca

$sepsis2HTO_HAB2
An object of class Seurat 
36621 features across 418 samples within 2 assays 
Active assay: RNA (36601 features, 800 variable features)
1 other assay present: HTO
1 dimensional reduction calculated: pca

$sepsis2HTO_HAB3
An object of class Seurat 
36621 features across 66 samples within 2 assays 
Active assay: RNA (36601 features, 800 variable features)
1 other assay present: HTO
1 dimensional reduction calculated: pca

Scaling features for provided objects
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s  
Computing within dataset neighborhoods
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  
Finding anchors between all query and reference datasets
  |                                                  | 0 % ~calculating  Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
        Found 1038 anchors
  |+++++++++                                         | 17% ~09s          Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
        Found 678 anchors
  |+++++++++++++++++                                 | 33% ~07s          Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
        Found 1039 anchors
  |+++++++++++++++++++++++++                         | 50% ~05s          Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
        Found 242 anchors
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
        Found 230 anchors
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
        Found 115 anchors
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s  
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_
  |                                                  | 0 % ~calculating  
Integrating dataset 2 with reference dataset
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_
  |+++++++++                                         | 17% ~09s          
Integrating dataset 3 with reference dataset
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_
  |+++++++++++++++++                                 | 33% ~06s          
Integrating dataset 4 with reference dataset
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_
  |+++++++++++++++++++++++++                         | 50% ~05s          
Integrating dataset 5 with reference dataset
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
Integrating dataset 6 with reference dataset
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_gex_ to integratedgex_
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~01s          
Integrating dataset 7 with reference dataset
Finding integration vectors
Finding integration vector weights
Error in idx[i, ] <- res[[i]][[1]] : 
  number of items to replace is not a multiple of replacement length