This page displays information for the following method:

Name:

scVI

Description:

Conditional variational autoencoder

GitHub:

YosefLab/scvi-tools

DOI:

10.1038/s41592-018-0229-2

Labels required?:

No

1 Overall

1.1 Plot

1.2 Table

2 Individual metrics

2.1 Overall

2.2 Batch correction

Batch correction

PCR batch

Batch ASW

Graph iLISI

Graph connectivity

kBET

2.3 Bio conservation

Bio conservation

NMI cluster/label

ARI cluster/label

Label ASW

Isolated label F1

Isolated label silhouette

Graph cLISI

HVG conservation

Cell cycle conservation

Trajectory conservation

3 Embeddings

Dataset (Output)

mouse_brain_atac_genes_large (Embedding)

Input features (Scaling)

Full (Unscaled)

mouse_brain_atac_peaks_small (Embedding)

Input features (Scaling)

Full (Unscaled)

mouse_brain_atac_genes_small (Embedding)

Input features (Scaling)

Full (Unscaled)

mouse_brain_atac_windows_small (Embedding)

Input features (Scaling)

Full (Unscaled)

4 Scalability

4.1 Time

4.2 Memory