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"# Molecular Interaction: Temporal Dominant Regulatory Interactions\n",
"\n",
"To obtain mechanistic insights into key regulatory motifs from different perspectives, we developed three complementary strategies: cell-wise, trajectory-wise and plane-wise analyses. In this tutorial, we will introduce approaches for gene network motif analysis and guide you to perform cell-wise analyses of SPI1-GATA1 network motif."
]
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"source": [
"Import relevant packages"
]
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"id": "51265933-8c0a-4915-91ef-ad5fe19ab679",
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{
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"text": [
"Using already downloaded Arial font from: /tmp/dynamo_arial.ttf\n",
"Registered custom font as: Arial\n",
"\n",
"\n",
" ███ ████████ \n",
"█████ █████ █████ █████ ███ █████ \n",
" ██████ ██████ ██████ ████████ ████ \n",
" ___ ████ ███\n",
" | \\ _ _ _ _ __ _ _ __ ___ ███\n",
" | |) | || | ' \\/ _` | ' \\/ _ \\█████ ███ \n",
" |___/ \\_, |_||_\\__,_|_|_|_\\___/█████ ████ \n",
" |__/ ███ █████ \n",
"Tutorial: https://dynamo-release.readthedocs.io/ \n",
" █████ \n",
"\n"
]
},
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\n",
" \n",
" \n",
" | package | \n",
" umap-learn | \n",
" typing-extensions | \n",
" tqdm | \n",
" statsmodels | \n",
" setuptools | \n",
" session-info | \n",
" seaborn | \n",
" scipy | \n",
" requests | \n",
" pynndescent | \n",
" pre-commit | \n",
" pandas | \n",
" openpyxl | \n",
" numdifftools | \n",
" numba | \n",
" networkx | \n",
" mudata | \n",
" matplotlib | \n",
" loompy | \n",
" leidenalg | \n",
" igraph | \n",
" dynamo-release | \n",
" colorcet | \n",
" anndata | \n",
"
\n",
" \n",
" \n",
" \n",
" | version | \n",
" 0.5.7 | \n",
" 4.13.2 | \n",
" 4.67.1 | \n",
" 0.14.4 | \n",
" 79.0.0 | \n",
" 1.0.1 | \n",
" 0.13.2 | \n",
" 1.11.4 | \n",
" 2.32.3 | \n",
" 0.5.13 | \n",
" 4.2.0 | \n",
" 2.2.3 | \n",
" 3.1.5 | \n",
" 0.9.41 | \n",
" 0.60.0 | \n",
" 3.4.2 | \n",
" 0.3.1 | \n",
" 3.10.3 | \n",
" 3.0.8 | \n",
" 0.10.2 | \n",
" 0.11.8 | \n",
" 1.4.2rc1 | \n",
" 3.1.0 | \n",
" 0.11.4 | \n",
"
\n",
" \n",
"
\n",
"