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#!/usr/bin/env python
import streamlit as st
from constants import MODELS
st.set_page_config(layout="wide")
st.markdown(
"""
# Download & Credits
## 1. Preprocessing pipelines
- Downloading datasets: [nf-core/fetchngs (revision 1.10.0)](https://github.com/nf-core/fetchngs)
- Aligning datasets: [brickmanlab/scrnaseq (revision: feature/smartseq)](https://github.com/brickmanlab/scrnaseq)
- **Ensembl Genomes (models <= v1.1)**
- Mouse: GRCm38 v102
- Human: GRCh38 v110
## 2. Codebase
- Data analysis: [brickmanlab/proks-salehin-et-al](https://github.com/brickmanlab/proks-salehin-et-al)
- Web portal on HF: [brickmanlab/hf-preimplantation-portal](https://huggingface.co/spaces/brickmanlab/hf-preimplantation-portal/tree/main)
- Web portal (deprecated): [brickmanlab/preimplantation-portal](https://github.com/brickmanlab/preimplantation-portal)
## 3. Raw and normalized counts
Raw counts are stored in `layers['counts']` and normalized counts are stored in `.X`.
- **models <= v1.1**
- [mouse](https://zenodo.org/records/13749348/files/01_mouse_reprocessed.h5ad)
- [human](https://zenodo.org/records/13749348/files/32_human_adata.h5ad)
## 4. scVI/scANVI models
Trained models with parameters were uploaded to [Hugging Face](https://huggingface.co/brickmanlab/preimplantation-models).
"""
)
text = ""
for specie in MODELS:
text += f"- **{specie}**: "
for version in MODELS[specie]:
url = (
f"https://huggingface.co/brickmanlab/{specie.lower()}-scanvi/tree/{version}"
)
text += f"[{version}]({url}), "
text = text[:-2] + "\n"
st.markdown(text)
st.markdown(
"""
## 5. Credit
> [!TIP]
> Proks, M., Salehin, N. & Brickman, J.M. Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing. Nat Methods 22, 207–216 (2025). https://doi.org/10.1038/s41592-024-02511-3
"""
)
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