File size: 1,628 Bytes
94e9257 | 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 53 54 55 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# scRep embedding example\n",
"\n",
"Run this notebook from the root of the downloaded scRep model repository."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"from types import SimpleNamespace\n",
"import numpy as np\n",
"from scRep_inference import encode_embeddings, load_examples, load_scRep_bundle\n",
"from scRep_pretrain.vocab import gene_vocab_to_map\n",
"\n",
"checkpoint = Path('checkpoints/scRep_20260625_30M')\n",
"input_h5ad = Path('/path/to/input.h5ad') # change this\n",
"args = SimpleNamespace(\n",
" model_dir=str(checkpoint), asset_dir='', device='cuda',\n",
" batch_size=128, n_bins=50, max_input_genes=2048, backbone='auto',\n",
")\n",
"model, gene_vocab, *_ = load_scRep_bundle(checkpoint, args)\n",
"gene_name_to_id = gene_vocab_to_map(gene_vocab)\n",
"examples, label_key = load_examples(input_h5ad, gene_name_to_id)\n",
"embeddings = encode_embeddings(model, examples, args)\n",
"np.save('embeddings.npy', embeddings)\n",
"print(embeddings.shape, label_key)"
]
}
],
"metadata": {
"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
"language_info": {"name": "python", "version": "3.10"}
},
"nbformat": 4,
"nbformat_minor": 5
}
|