|
|
--- |
|
|
language: en |
|
|
tags: |
|
|
- single-cell |
|
|
- rna-seq |
|
|
- leukemia |
|
|
- scvi |
|
|
- bioinformatics |
|
|
license: mit |
|
|
--- |
|
|
|
|
|
# LeukoMap scVI Model |
|
|
|
|
|
This is a trained scVI (single-cell Variational Inference) model for pediatric leukemia single-cell RNA-seq analysis. |
|
|
|
|
|
## Model Details |
|
|
|
|
|
- **Model Type**: scVI (single-cell Variational Inference) |
|
|
- **Training Data**: Caron et al. (2020) pediatric leukemia dataset |
|
|
- **Architecture**: Variational Autoencoder for single-cell data |
|
|
- **Latent Dimensions**: Unknown |
|
|
- **Training Epochs**: Unknown |
|
|
|
|
|
## Usage |
|
|
|
|
|
```python |
|
|
from scvi.model import SCVI |
|
|
import scanpy as sc |
|
|
|
|
|
# Load your AnnData object |
|
|
adata = sc.read_h5ad("your_data.h5ad") |
|
|
|
|
|
# Load the model |
|
|
model = SCVI.load("your-username/leukomap-scvi", adata) |
|
|
|
|
|
# Get latent representation |
|
|
latent = model.get_latent_representation() |
|
|
``` |
|
|
|
|
|
## Dataset |
|
|
|
|
|
This model was trained on the Caron et al. (2020) pediatric leukemia dataset: |
|
|
- **GEO Accession**: GSE132509 |
|
|
- **Paper**: https://doi.org/10.1038/s41598-020-64929-x |
|
|
- **Original Analysis**: https://github.com/CBC-UCONN/Single-Cell-Transcriptomics |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use this model, please cite: |
|
|
- Caron et al. (2020) Single-cell analysis of childhood leukemia reveals a link between developmental states and ribosomal protein expression as a source of intra-individual heterogeneity |
|
|
- Lopez et al. (2018) Deep generative modeling for single-cell transcriptomics |
|
|
|
