seuratUMAP fails, can't find reductions

[mcsimenc]

Hi, I am getting this result. Setting method to "scaterUMAP" works.

sceObj = runNormalization(
  inSCE = sceObj,
  useAssay = "counts",
  outAssayName = "logcounts",
  normalizationMethod = "LogNormalize",
  scale = F,
  verbose = T
)

sceObj = runSeuratFindHVG(
  inSCE = sceObj,
  useAssay = "logcounts", 
  method = "vst",
  hvgNumber = 2000, 
  createFeatureSubset = "hvf"
)
s
sceObj = runDimReduce(
  inSCE = sceObj,
  method = "seuratPCA",
  reducedDimName = "PCA",
  useAssay = "logcounts",
  scale = T,
  seed = 789,
  nComponents = 30
)

# runSeuratFindClusters
sceObj = runScranSNN(
  inSCE = sceObj,
  useReducedDim = "PCA",
  clusterName = "cluster",
  k = 14,
  nComp = 30,
  algorithm = "louvain",
  seed = 789
)

sceObj = runDimReduce(
  inSCE = sceObj,
  method = "seuratUMAP",
  useReducedDim = "PCA",
  useFeatureSubset = "hvf",
  reducedDimName = "UMAP",
  scale = F,
  seed = 789,
  nComponents = 2
)

Sun Nov  3 23:41:09 2024 ... Computing Seurat UMAP.
Warning: Layer ‘var.features’ is empty
Error in `x[[i, drop = TRUE]]`:
! ‘reductions’ not found in this Seurat object
 
Backtrace:
 1. singleCellTK::runDimReduce(...)
 2. singleCellTK::runSeuratUMAP(...)
 3. singleCellTK::convertSCEToSeurat(inSCE)
 5. SeuratObject:::`$.Seurat`(inSCE@metadata$seurat$obj, "reductions")
 7. SeuratObject:::`[[.Seurat`(x, i, drop = TRUE)

[mingl1997]

Hello,

I ran the following code loading in the PBMC3K dataset that I use as testing:

library(singleCellTK)
library(Seurat)
library(TENxPBMCData)

# Load pbmc3k data 
sceObj <- TENxPBMCData(dataset = "pbmc3k")

# Run QC
sceObj <- runCellQC(sceObj)

sceObj = runNormalization(
    inSCE = sceObj,
    useAssay = "counts",
    outAssayName = "logcounts",
    normalizationMethod = "LogNormalize",
    scale = F,
    verbose = T
)

sceObj = runSeuratFindHVG(
    inSCE = sceObj,
    useAssay = "logcounts", 
    method = "vst",
    hvgNumber = 2000, 
    createFeatureSubset = "hvf"
)

sceObj = runDimReduce(
    inSCE = sceObj,
    method = "seuratPCA",
    reducedDimName = "PCA",
    useAssay = "logcounts",
    scale = T,
    seed = 789,
    nComponents = 30
)

# runSeuratFindClusters
sceObj = runScranSNN(
    inSCE = sceObj,
    useReducedDim = "PCA",
    clusterName = "cluster",
    k = 14,
    nComp = 30,
    algorithm = "louvain",
    seed = 789
)

sceObj = runDimReduce(
    inSCE = sceObj,
    method = "seuratUMAP",
    useReducedDim = "PCA",
    useFeatureSubset = "hvf",
    reducedDimName = "UMAP",
    scale = F,
    seed = 789,
    nComponents = 2
)

I receive the following result:

> library(singleCellTK)
> library(Seurat)
> 
> # Load pbmc3k data
> sceObj <- TENxPBMCData(dataset = "pbmc3k")
> 
> # Run QC
> sceObj <- runCellQC(sceObj)
>
> {...}
>
> sceObj = runNormalization(
+     inSCE = sceObj,
+     useAssay = "counts",
+     outAssayName = "logcounts",
+     normalizationMethod = "LogNormalize",
+     scale = F,
+     verbose = T
+ )
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Normalization performed using LogNormalize method.
> 
> sceObj = runSeuratFindHVG(
+     inSCE = sceObj,
+     useAssay = "logcounts", 
+     method = "vst",
+     hvgNumber = 2000, 
+     createFeatureSubset = "hvf"
+ )
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Wed Dec 11 12:04:14 2024 ... Feature subset variable 'hvf' created.
> 
> sceObj = runDimReduce(
+     inSCE = sceObj,
+     method = "seuratPCA",
+     reducedDimName = "PCA",
+     useAssay = "logcounts",
+     scale = T,
+     seed = 789,
+     nComponents = 30
+ )
Wed Dec 11 12:04:14 2024 ... Computing Seurat PCA.
Centering and scaling data matrix
  |================================================================================| 100%
PC_ 1 
Positive:  ENSG00000143947, ENSG00000213402, ENSG00000145425, ENSG00000227507, ENSG00000168028, ENSG00000167286, ENSG00000008517, ENSG00000198851, ENSG00000213741, ENSG00000111716 
	   ENSG00000121966, ENSG00000205246, ENSG00000168685, ENSG00000173762, ENSG00000182866, ENSG00000104964, ENSG00000133639, ENSG00000089157, ENSG00000181163, ENSG00000116824 
	   ENSG00000108654, ENSG00000156976, ENSG00000105373, ENSG00000110848, ENSG00000172183, ENSG00000147168, ENSG00000114942, ENSG00000196405, ENSG00000101608, ENSG00000147604 
Negative:  ENSG00000101439, ENSG00000011600, ENSG00000163220, ENSG00000204482, ENSG00000087086, ENSG00000090382, ENSG00000204472, ENSG00000085265, ENSG00000167996, ENSG00000158869 
	   ENSG00000025708, ENSG00000100097, ENSG00000197766, ENSG00000143546, ENSG00000100079, ENSG00000129226, ENSG00000163131, ENSG00000197249, ENSG00000130066, ENSG00000066336 
	   ENSG00000197746, ENSG00000126759, ENSG00000142089, ENSG00000163191, ENSG00000216490, ENSG00000119655, ENSG00000103187, ENSG00000030582, ENSG00000131981, ENSG00000182287 
PC_ 2 
Positive:  ENSG00000105374, ENSG00000145649, ENSG00000077984, ENSG00000180644, ENSG00000100453, ENSG00000172543, ENSG00000137441, ENSG00000115523, ENSG00000161570, ENSG00000197540 
	   ENSG00000159674, ENSG00000100450, ENSG00000126264, ENSG00000129277, ENSG00000198821, ENSG00000196154, ENSG00000008517, ENSG00000134539, ENSG00000122862, ENSG00000143185 
	   ENSG00000135046, ENSG00000169583, ENSG00000133321, ENSG00000203747, ENSG00000108518, ENSG00000173762, ENSG00000196139, ENSG00000160255, ENSG00000171476, ENSG00000180739 
Negative:  ENSG00000105369, ENSG00000156738, ENSG00000196735, ENSG00000179344, ENSG00000100721, ENSG00000007312, ENSG00000204287, ENSG00000247982, ENSG00000128218, ENSG00000019582 
	   ENSG00000196126, ENSG00000223865, ENSG00000237541, ENSG00000231389, ENSG00000198502, ENSG00000104921, ENSG00000204257, ENSG00000153064, ENSG00000104894, ENSG00000242574 
	   ENSG00000106537, ENSG00000132185, ENSG00000122986, ENSG00000241106, ENSG00000168734, ENSG00000176533, ENSG00000145088, ENSG00000137101, ENSG00000269404, ENSG00000112799 
PC_ 3 
Positive:  ENSG00000100453, ENSG00000137441, ENSG00000180644, ENSG00000105374, ENSG00000115523, ENSG00000077984, ENSG00000159674, ENSG00000145649, ENSG00000007312, ENSG00000105369 
	   ENSG00000129277, ENSG00000196735, ENSG00000156738, ENSG00000100450, ENSG00000019582, ENSG00000169583, ENSG00000223865, ENSG00000134539, ENSG00000100721, ENSG00000179344 
	   ENSG00000143185, ENSG00000196139, ENSG00000231389, ENSG00000075234, ENSG00000203747, ENSG00000150687, ENSG00000196126, ENSG00000247982, ENSG00000172543, ENSG00000180739 
Negative:  ENSG00000111716, ENSG00000167286, ENSG00000026025, ENSG00000168685, ENSG00000171223, ENSG00000241343, ENSG00000170345, ENSG00000142546, ENSG00000198851, ENSG00000213741 
	   ENSG00000089157, ENSG00000172809, ENSG00000102760, ENSG00000152518, ENSG00000172005, ENSG00000165272, ENSG00000126353, ENSG00000129824, ENSG00000082074, ENSG00000133872 
	   ENSG00000227507, ENSG00000008517, ENSG00000116251, ENSG00000148908, ENSG00000196683, ENSG00000171858, ENSG00000147604, ENSG00000116824, ENSG00000204387, ENSG00000181163 
PC_ 4 
Positive:  ENSG00000163736, ENSG00000163737, ENSG00000168497, ENSG00000127920, ENSG00000113140, ENSG00000154146, ENSG00000180573, ENSG00000169704, ENSG00000150681, ENSG00000101162 
	   ENSG00000120885, ENSG00000236304, ENSG00000010278, ENSG00000005961, ENSG00000171611, ENSG00000104267, ENSG00000088726, ENSG00000111644, ENSG00000108960, ENSG00000161911 
	   ENSG00000124491, ENSG00000184702, ENSG00000101856, ENSG00000176783, ENSG00000101335, ENSG00000145335, ENSG00000095303, ENSG00000102804, ENSG00000166091, ENSG00000204424 
Negative:  ENSG00000168028, ENSG00000051523, ENSG00000147604, ENSG00000231389, ENSG00000143947, ENSG00000223865, ENSG00000089157, ENSG00000143119, ENSG00000198502, ENSG00000196126 
	   ENSG00000145287, ENSG00000019582, ENSG00000104894, ENSG00000145425, ENSG00000128340, ENSG00000137441, ENSG00000106153, ENSG00000196735, ENSG00000105374, ENSG00000115523 
	   ENSG00000169442, ENSG00000179344, ENSG00000169100, ENSG00000180644, ENSG00000185201, ENSG00000135390, ENSG00000114942, ENSG00000007312, ENSG00000102879, ENSG00000077984 
PC_ 5 
Positive:  ENSG00000143546, ENSG00000162444, ENSG00000163221, ENSG00000163220, ENSG00000110203, ENSG00000170458, ENSG00000100079, ENSG00000110077, ENSG00000119535, ENSG00000166523 
	   ENSG00000090382, ENSG00000169429, ENSG00000183019, ENSG00000221869, ENSG00000130522, ENSG00000085265, ENSG00000006075, ENSG00000150337, ENSG00000141505, ENSG00000011600 
	   ENSG00000126883, ENSG00000090013, ENSG00000104918, ENSG00000124731, ENSG00000084207, ENSG00000226091, ENSG00000129277, ENSG00000169413, ENSG00000135929, ENSG00000213903 
Negative:  ENSG00000185201, ENSG00000026297, ENSG00000143933, ENSG00000184009, ENSG00000150991, ENSG00000070756, ENSG00000133872, ENSG00000143119, ENSG00000109971, ENSG00000169442 
	   ENSG00000155366, ENSG00000101608, ENSG00000123416, ENSG00000134333, ENSG00000171223, ENSG00000065978, ENSG00000102879, ENSG00000181163, ENSG00000168028, ENSG00000136156 
	   ENSG00000118680, ENSG00000187109, ENSG00000172757, ENSG00000203747, ENSG00000143549, ENSG00000122566, ENSG00000089327, ENSG00000142168, ENSG00000189043, ENSG00000125534
> 
> # runSeuratFindClusters
> sceObj = runScranSNN(
+     inSCE = sceObj,
+     useReducedDim = "PCA",
+     clusterName = "cluster",
+     k = 14,
+     nComp = 30,
+     algorithm = "louvain",
+     seed = 789
+ )
Wed Dec 11 12:04:17 2024 ... Running 'scran SNN clustering' with 'louvain' algorithm
Wed Dec 11 12:04:18 2024 ...   Identified 9 clusters
> 
> sceObj = runDimReduce(
+     inSCE = sceObj,
+     method = "seuratUMAP",
+     useReducedDim = "PCA",
+     useFeatureSubset = "hvf",
+     reducedDimName = "UMAP",
+     scale = F,
+     seed = 789,
+     nComponents = 2
+ )
Wed Dec 11 12:04:19 2024 ... Computing Seurat UMAP.
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
12:04:20 UMAP embedding parameters a = 0.9922 b = 1.112
12:04:20 Read 2700 rows and found 10 numeric columns
12:04:20 Using Annoy for neighbor search, n_neighbors = 30
12:04:20 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:04:20 Writing NN index file to temp file C:\Users\Cortana\AppData\Local\Temp\Rtmpu4piOF\file1f6c05ad71814
12:04:20 Searching Annoy index using 1 thread, search_k = 3000
12:04:21 Annoy recall = 100%
12:04:21 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
12:04:23 Initializing from normalized Laplacian + noise (using RSpectra)
12:04:23 Commencing optimization for 500 epochs, with 107660 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:04:29 Optimization finished

Could you supply the data that you used and which versions of sctk and Seurat?