DKCC.RdDKCC
DKCC(seurat, threshold = 0.7, max.iter = 1)
| seurat | seurat object |
|---|---|
| threshold | minimum value for an identity to be assigned within the model call, default is 0.7 |
| max.iter | Can ask scPred to run this number of integrations, set to 0 be default |
seurat object with additional metadata columns
organoid <- DKCC(organoid)#> ● Matching reference with new dataset... #> ─ 9977 features present in reference loadings #> ─ 7520 features shared between reference and new dataset #> ─ 75.37% of features in the reference are present in new dataset #> ● Aligning new data to reference... #>#>#> ● Classifying cells... #> DONE! #> ‒ Data has already being aligned to a reference. #> ⁺ Skip data alignment using `recompute.alignment = FALSE`. #> ● Matching reference with new dataset... #> ─ 9768 features present in reference loadings #> ─ 7427 features shared between reference and new dataset #> ─ 76.03% of features in the reference are present in new dataset #> ● Aligning new data to reference... #>#>#> ● Classifying cells... #> DONE! #> ‒ Data has already being aligned to a reference. #> ⁺ Skip data alignment using `recompute.alignment = FALSE`. #> ● Matching reference with new dataset... #> ─ 8140 features present in reference loadings #> ─ 6605 features shared between reference and new dataset #> ─ 81.14% of features in the reference are present in new dataset #> ● Aligning new data to reference... #>#>#> ● Classifying cells... #> DONE! #> ‒ Data has already being aligned to a reference. #> ⁺ Skip data alignment using `recompute.alignment = FALSE`. #> ● Matching reference with new dataset... #> ─ 8693 features present in reference loadings #> ─ 7037 features shared between reference and new dataset #> ─ 80.95% of features in the reference are present in new dataset #> ● Aligning new data to reference... #>#>#> ● Classifying cells... #> DONE! #> ‒ Data has already being aligned to a reference. #> ⁺ Skip data alignment using `recompute.alignment = FALSE`. #> ● Matching reference with new dataset... #> ─ 8794 features present in reference loadings #> ─ 6956 features shared between reference and new dataset #> ─ 79.1% of features in the reference are present in new dataset #> ● Aligning new data to reference... #>#>#> ● Classifying cells... #> DONE! #> ‒ Data has already being aligned to a reference. #> ⁺ Skip data alignment using `recompute.alignment = FALSE`. #> ● Matching reference with new dataset... #> ─ 8736 features present in reference loadings #> ─ 6820 features shared between reference and new dataset #> ─ 78.07% of features in the reference are present in new dataset #> ● Aligning new data to reference... #>#>#> ● Classifying cells... #> DONE! #>#> Warning: pseudoinverse used at -1.4983#> Warning: neighborhood radius 0.30103#> Warning: reciprocal condition number 9.4429e-15#>#> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #> #>#> 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#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck #> #> Number of nodes: 63 #> Number of edges: 1823 #> #> Running Louvain algorithm... #> Maximum modularity in 10 random starts: 0.5100 #> Number of communities: 2 #> Elapsed time: 0 seconds#> Warning: Only less than three identities present, the expression values will be not scaled