File size: 1,379 Bytes
6ca45de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ---
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
|