)
Using reference SCTModel to calculate pearson residuals
Determine variable features
Setting min_variance to: -Inf
Calculating residuals of type pearson for 2755 genes
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Set default assay to refAssay
Find anchors between query and reference
anchors <- FindTransferAnchors(
reference = reference$map,
query = query,
k.filter = NA,
reference.neighbors = "refdr.annoy.neighbors",
reference.assay = "refAssay",
query.assay = "refAssay",
reference.reduction = "refDR",
normalization.method = "SCT",
features = intersect(rownames(x = reference$map), VariableFeatures(object = query)),
dims = 1:100,
n.trees = 20,
mapping.score.k = 100
)
Normalizing query using reference SCT model
Projecting cell embeddings
Finding query neighbors
Finding neighborhoods
Finding anchors
Found 905 anchors
Transfer cell type labels and impute protein expression
#
Transferred labels are in metadata columns named "predicted.*"
The maximum prediction score is in a metadata column named "predicted.*.score"
The prediction scores for each class are in an assay named "prediction.score.*"
})
names(x = refdata) <- "annotation.l2"
if (FALSE) {
refdata[["impADT"]] <- GetAssayData(
object = reference$map[['ADT']],
slot = 'data'
)
}
query <- TransferData(
reference = reference$map,
query = query,
dims = 1:100,
anchorset = anchors,
refdata = refdata,
n.trees = 20,
store.weights = TRUE
)
Finding integration vectors
Finding integration vector weights
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[----|----|----|----|----|----|----|----|----|----|
**|
Predicting cell labels
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from predictionscorea
nnotation.l2 to predictionscoreannotationl2
Calculate the embeddings of the query data on the reference SPCA
query <- IntegrateEmbeddings(
anchorset = anchors,
reference = reference$map,
query = query,
reductions = "pcaproject",
reuse.weights.matrix = TRUE
)
Integrating dataset 2 with reference dataset
Finding integration vectors
Integrating data
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrateddr t
o integrateddr_
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrateddr t
o integrateddr
Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to integrated
dr_
Calculate the query neighbors in the reference
with respect to the integrated embeddings
query[["query_ref.nn"]] <- FindNeighbors(
object = Embeddings(reference$map[["refDR"]]),
query = Embeddings(query[["integrated_dr"]]),
return.neighbor = TRUE,
l2.norm = TRUE
)
Computing nearest neighbors
The reference used in the app is downsampled compared to the reference on which
the UMAP model was computed. This step, using the helper function NNTransform,
corrects the Neighbors to account for the downsampling.
query <- Azimuth:::NNTransform(
object = query,
meta.data = reference$map[[]]
)
Project the query to the reference UMAP.
query[["proj.umap"]] <- RunUMAP(
object = query[["query_ref.nn"]],
reduction.model = reference$map[["refUMAP"]],
reduction.key = 'UMAP_'
)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using t
he 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
Running UMAP projection
02:12:52 Read 1020 rows
02:12:52 Processing block 1 of 1
02:12:52 Commencing smooth kNN distance calibration using 1 thread
02:12:52 Initializing by weighted average of neighbor coordinates using 1 thread
02:12:52 Commencing optimization for 67 epochs, with 20400 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**|
02:12:52 Finished
Warning: No assay specified, setting assay as RNA by default.
Warning message:
In RunUMAP.default(object = neighborlist, reduction.model = reduction.model, :
Number of neighbors between query and reference is not equal to the number of neighbros within reference
Calculate mapping score and add to metadata
query <- AddMetaData(
object = query,
metadata = MappingScore(anchors = anchors),
col.name = "mapping.score"
)
Projecting reference PCA onto query
Finding integration vector weights
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[----|----|----|----|----|----|----|----|----|----|
**|
Projecting back the query cells into original PCA space
Finding integration vector weights
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[----|----|----|----|----|----|----|----|----|----|
**|
Computing scores:
Finding neighbors of original query cells
Finding neighbors of transformed query cells
_Error in index$getNNsByVectorList(query[x, ], k, search.k, include.distance) :
fv.size() != vector_size
Calls: AddMetaData ... resolve.list -> signalConditionsASAP -> signalConditions
Execution halted_
qicheng-ma
commented
2 years ago
Dear Azimuth team,
I go the following error message from azimuth_analysis.R code which I downloaded from https://app.azimuth.hubmapconsortium.org/app/human-kidney
Even though with " features = intersect(rownames(x = reference$map), VariableFeatures(object = query)),"
"Error in index$getNNsByVectorList(query[x, ], k, search.k, include.distance) : fv.size() != vector_size Calls: AddMetaData ... resolve.list -> signalConditionsASAP -> signalConditions Execution halted"
But I can run it though https://app.azimuth.hubmapconsortium.org/app/human-kidney, that is how I got the azimuth_analysis.R code.
I appreciate your help!
Integrating dataset 2 with reference dataset Finding integration vectors Integrating data Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrateddr t o integrateddr_ Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrateddr t o integrateddr Warning: All keys should be one or more alphanumeric characters followed by an underscore '', setting key to integrated dr_