-
Notifications
You must be signed in to change notification settings - Fork 33
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
RunAzimuth error: Error in idx[i, ] <- res[[i]][[1]] #144
Comments
Hi, what does your query object look like (how many cells/features)? |
There are 15,015 features and 1,144 cells. |
Hi all, same issue here. 20179 genes by 3965 cells. Thank you for all your work. |
Hi, |
Hello, I am having the same error as the author of the post. I have 70 cells and about 28000 genes. Is there a way to make Azimuth work with such a small dataset (in terms of number of cells)? Thank you. I have Seurat 4 and Azimuth 0.4.5 |
Hi, did you try lowering the |
Hi! Yes, I lowered both in order to have k.weight < number of cells and mapping.score.k < number of cells. The problem is that at some point it seems like mapping.score.k gets reset to 100. Warning: Adding a dimensional reduction (refUMAP) without the associated assay being present
detected inputs from HUMAN with id type Gene.name
reference rownames detected HUMAN with id type Gene.name
Using reference SCTModel to calculate pearson residuals
Determine variable features
Setting min_variance to: -Inf
Calculating residuals of type pearson for 2882 genes
|=================================================================================| 100%
|=================================================================================| 100%
Set default assay to refAssay
Normalizing query using reference SCT model
Projecting cell embeddings
Finding query neighbors
Finding neighborhoods
Finding anchors
Found 244 anchors
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from predictionscorecelltype.l2_ to predictionscorecelltypel2_
Predicting cell labels
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from predictionscorecelltype.l1_ to predictionscorecelltypel1_
| | 0 % ~calculating
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 integrated_dr_ to integrateddr_
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_dr_ to integrateddr_
Computing nearest neighbors
Running UMAP projection
19:00:25 Read 70 rows
19:00:25 Processing block 1 of 1
19:00:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
19:00:25 Initializing by weighted average of neighbor coordinates using 1 thread
19:00:25 Commencing optimization for 67 epochs, with 1400 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
19:00:25 Finished
Recomputing query neighborhoods.
**Setting mapping.score.k in FindTransferAnchors to the ksmooth
value here (100)**, can bypass this calculation in future runs.
Projecting reference PCA onto query
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Projecting back the query cells into original PCA space
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Computing scores:
Finding neighbors of original query cells
Finding neighbors of transformed query cells
Error in idx[i, ] <- res[[i]][[1]] : Thank you a lot! |
When using
RunAzimuth
:I get:
Following other issues,
I've tried reducing the
k.weight
value and the error persists.Would tweaking the other parameters like
n.trees
ormapping.score.k
help?The text was updated successfully, but these errors were encountered: