semmrich
commented
3 years ago Update:
When I leave out the PCA/Neighbors/Clustering/UMAP step and use the query without dimreds,
S.mmuBM.integrated
An object of class Seurat
58801 features across 19480 samples within 3 assays
Active assay: integrated (3421 features, 3421 variable features)
2 other assays present: RNA, ADTit still fails to run through:
Transfer.anchors <- FindTransferAnchors(reference = S.mmuREF.integrated, query = S.mmuBM.integrated, dims = 1:50)
Performing PCA on the provided reference using 4656 features as input.
Projecting PCA
Error in Loadings(object = reference[[reduction]])[features, dims] :
subscript out of bounds
toc()
131.93 sec elapsed
Transfer.anchors <- FindTransferAnchors(reference = S.mmuREF.integrated, query = S.mmuBM.integrated, dims = 1:50,
+ features = intersect(S.mmuREF.integrated@assays[["integrated"]]@var.features,S.mmuBM.integrated@assays[["integrated"]]@var.features))
Performing PCA on the provided reference using 1764 features as input.
Projecting PCA
Error in Loadings(object = reference[[reduction]])[features, dims] :
subscript out of bounds
toc()
58.95 sec elapsedInterestingly, the Vignette example "panc8" runs smoothly while showing the same setup of the two Seurat objects:
pancreas.query
An object of class Seurat
34363 features across 638 samples within 1 assay
Active assay: RNA (34363 features, 2000 variable features)
pancreas.integrated
An object of class Seurat
36363 features across 5683 samples within 2 assays
Active assay: integrated (2000 features, 2000 variable features)
1 other assay present: RNA
2 dimensional reductions calculated: pca, umapThe query has no dimreds, while the ref has. I don't get it... Whats more:
>S.mmuREF.integrated <- ScaleData(object = S.mmuREF.integrated, assay = "integrated", , features = intersect(rownames(S.mmuREF.integrated),rownames(S.mmuBM.integrated)))
Centering and scaling data matrix
|=================================================================================================================================================| 100%
>S.mmuREF.integrated <- RunPCA(object = S.mmuREF.integrated, assay = "integrated", npcs = 50, verbose = TRUE, features = intersect(rownames(S.mmuREF.integrated),rownames(S.mmuBM.integrated)))
>S.mmuREF.integrated <- FindNeighbors(object = S.mmuREF.integrated, reduction = "pca", dims = 1:50)
Computing nearest neighbor graph
Computing SNN
>S.mmuREF.integrated <- FindClusters(object = S.mmuREF.integrated, resolution = 0.5)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 64650
Number of edges: 2539416
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9242
Number of communities: 23
Elapsed time: 22 seconds
> S.mmuREF.integrated <- RunUMAP(object = S.mmuREF.integrated, dims = 1:50)
16:47:21 UMAP embedding parameters a = 0.9922 b = 1.112
16:47:21 Read 64650 rows and found 50 numeric columns
16:47:21 Using Annoy for neighbor search, n_neighbors = 30
16:47:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:47:37 Writing NN index file to temp file C:\Users\FUCHSD~1\AppData\Local\Temp\RtmpY9vD9W\file2af42e0b14bb
16:47:37 Searching Annoy index using 1 thread, search_k = 3000
16:47:58 Annoy recall = 100%
16:47:59 Commencing smooth kNN distance calibration using 1 thread
16:48:05 Initializing from normalized Laplacian + noise
16:48:13 Commencing optimization for 200 epochs, with 2920214 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:49:32 Optimization finished
> S.mmuREF.integrated <- ScaleData(object = S.mmuREF.integrated, assay = "integrated", features = intersect(rownames(S.mmuREF.integrated),rownames(S.mmuBM.integrated)))
Centering and scaling data matrix
|=================================================================================================================================================| 100%
>dim(S.mmuREF.integrated@reductions[["pca"]]@feature.loadings)
[1] 1764 50
> Transfer.anchors <- FindTransferAnchors(reference = S.mmuREF.integrated, query = S.mmuBM.integrated, dims = 1:50, features = intersect(S.mmuREF.integrated@assays[["integrated"]]@var.features,S.mmuBM.integrated@assays[["integrated"]]@var.features))
Performing PCA on the provided reference using 1764 features as input.
Projecting PCA
Error in Loadings(object = reference[[reduction]])[features, dims] :
subscript out of boundsThis is very puzzling... All feature dims can be found in each ref and query, as in the vignette, still fails to retrieve anchors??? Any ideas that I could try are highly appreciated... Thx, Stephan
jaisonj708
Hi,
I am trying to apply the "Cell type classification using an integrated reference" from the Seurat Vignette https://satijalab.org/seurat/v3.2/integration.html
I integrated 4 query Seurat datasets following this Vignettes first part, retrieving an object
I then moved on to integrate 3 reference Seurat datasets into
When I try to project the reference it fails at the first step:
While the query integration still retains an ADT assay slot, even with that assay removed I encounter the same error. I do not understand what is wrong? The error log suggests that the feature loadings do not match. But if a new integrated PCA is performed on an intersect of the VariableFeatures between ref and query as I set with
features = intersect(), then why do the individual (PCA) feature loadings of each ref and query matter? I will try to run the FindTransferAnchors on both ref and query without any prior dimreds, and if that fails will run thefeatures = intersect()output as fixed VariableFeatures, hope that sets it. But from the Vignette it seems the "pancreas.query" did not had any VaribleFeature determined, while the ref "pancreas.integrated" had. How come those feature loadings "match" and it works?I will appreciate any helps in this matter.
Many Thanks in advance, Stephan