{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.simplefilter(action='ignore', category=FutureWarning)\n",
"\n",
"import multigrate as mtg\n",
"import scanpy as sc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MOFA"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 6334 × 13\n",
" obs: 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'batch'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latent = sc.read('../data/integrated/mofa/sagner-mofa.h5ad')\n",
"latent"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"latent.obsm['latent'] = latent.X"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clustering...\n",
"ASW label/batch...\n",
"Graph connectivity...\n",
"ASW label...\n",
"NMI cluster/label...\n",
"ARI cluster/label...\n",
"Isolated label silhouette...\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ASW_label/batch</th>\n",
" <td>0.916624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>graph_conn</th>\n",
" <td>0.882245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ASW_label</th>\n",
" <td>0.588075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NMI_cluster/label</th>\n",
" <td>0.740319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ARI_cluster/label</th>\n",
" <td>0.553285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>isolated_label_silhouette</th>\n",
" <td>0.540644</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score\n",
"ASW_label/batch 0.916624\n",
"graph_conn 0.882245\n",
"ASW_label 0.588075\n",
"NMI_cluster/label 0.740319\n",
"ARI_cluster/label 0.553285\n",
"isolated_label_silhouette 0.540644"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics = mtg.metrics.metrics(None, latent,\n",
" batch_key='batch',\n",
" label_key='cell_type',\n",
" isolated_label_f1=False,\n",
" pcr_batch=False,\n",
" embed='latent', \n",
" save='sagner-mofa.csv', \n",
" method='mofa')\n",
"metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Seurat"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AnnData object with n_obs × n_vars = 6334 × 20\n",
" obs: 'cell_type', 'batch', 'nCount_RNA', 'nFeature_RNA', 'nCount_SCT', 'nFeature_SCT', 'integrated.weight'\n",
" obsp: 'connectivities'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latent = sc.read('../data/integrated/seurat/sagner-seurat.h5ad')\n",
"latent"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"latent.obsm['latent'] = latent.X"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clustering...\n",
"ASW label/batch...\n",
"Graph connectivity...\n",
"ASW label...\n",
"NMI cluster/label...\n",
"ARI cluster/label...\n",
"Isolated label silhouette...\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ASW_label/batch</th>\n",
" <td>0.927233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>graph_conn</th>\n",
" <td>0.977470</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ASW_label</th>\n",
" <td>0.662901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NMI_cluster/label</th>\n",
" <td>0.881507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ARI_cluster/label</th>\n",
" <td>0.864936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>isolated_label_silhouette</th>\n",
" <td>0.560829</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score\n",
"ASW_label/batch 0.927233\n",
"graph_conn 0.977470\n",
"ASW_label 0.662901\n",
"NMI_cluster/label 0.881507\n",
"ARI_cluster/label 0.864936\n",
"isolated_label_silhouette 0.560829"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics = mtg.metrics.metrics(None, latent,\n",
" batch_key='batch',\n",
" label_key='cell_type',\n",
" isolated_label_f1=False,\n",
" pcr_batch=False,\n",
" embed='latent', \n",
" save='sagner-seurat.csv', \n",
" method='seurat')\n",
"metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
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"file_extension": ".py",
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