replogle_scETM_metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8270f66-7a27-4263-aa97-25abeb2cf34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "529f64f3-c83e-481d-872c-df89b9dd063b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from utils import (\n",
    "    auprc,\n",
    "    factor_enrichment_gsea,\n",
    "    get_gprofiler,\n",
    "    read_aws_csv,\n",
    "    read_aws_h5ad,\n",
    "    split_data_by_cell,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db596f53-18d6-4c3b-82ac-601923be6eb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in scETM results\n",
    "adata = read_aws_h5ad(\n",
    "    \"s3://pert-spectra/scETM_checkpoints/scetm_replogle/scetm_replogle.h5ad\"\n",
    ")\n",
    "# read in pertspectra results to retrieve gene labels\n",
    "ref_adata = read_aws_h5ad(\n",
    "    \"s3://pert-spectra/PertSpectra_checkpoints/pertspectra_replogle/replogle.h5ad\"\n",
    ")\n",
    "adata.var_names = ref_adata.var_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b7bf773-5e0c-43b8-8df2-4ce3376be959",
   "metadata": {},
   "source": [
    "# Reconstruction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4b092b-2120-48c6-a492-84656e8b7169",
   "metadata": {},
   "source": [
    "## Spearman Coefficient\n",
    "- Spearman correlation between predicted and observed expression (on DE genes if available)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2885bd3-5433-47d6-88d5-47be83df5b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import spearmanr\n",
    "\n",
    "test_corr_singles = []\n",
    "test_corr_combos = []\n",
    "train_idx, val_idx, test_idx = split_data_by_cell(\n",
    "    adata.X, adata.obs[\"gene\"], test_size=0.2, val_size=0.2\n",
    ")\n",
    "adata_test = adata[test_idx]\n",
    "for pert in adata_test.obs[\"gene\"].unique():\n",
    "    hold_idx = [i for i, x in enumerate(adata_test.obs[\"gene\"]) if x == pert]\n",
    "    recon = adata_test.uns[\"recon\"][hold_idx]\n",
    "    # correlation\n",
    "    mean_reconstruction = recon.mean(axis=0)\n",
    "    mean_observed = np.squeeze(np.array(adata_test[hold_idx].X.mean(axis=0)))\n",
    "    if (\"+\" in pert) and (\"ctrl\" not in pert):\n",
    "        test_corr_combos.append(\n",
    "            [pert, spearmanr(mean_reconstruction, mean_observed)[0]]\n",
    "        )\n",
    "    else:\n",
    "        test_corr_singles.append(\n",
    "            [pert, spearmanr(mean_reconstruction, mean_observed)[0]]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f7404d5-c4b3-44be-a6c3-0e213a6704b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot train correlation distribution vs test correlation distribution\n",
    "import seaborn as sns\n",
    "\n",
    "test_corr = np.array(test_corr_singles + test_corr_combos)[:, 1].astype(float)\n",
    "sns.histplot(test_corr, label=\"test_corr\")\n",
    "plt.title(\"Correlation between predicted and observed expression per perturbation\")\n",
    "plt.xlabel(\"Spearman Correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6817a48-76e2-4d36-9725-68dab2568d2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(\n",
    "    \"figures/reconstruction_spearmans/scETM_replogle_spearman_correlations.csv\",\n",
    "    test_corr,\n",
    "    delimiter=\",\",\n",
    "    fmt=\"%.2f\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64fc0b35-f87e-4d8a-87f1-e6080a3ee83f",
   "metadata": {},
   "source": [
    "# Perturbation Embedding Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36ebe2b-2835-44ed-9f71-a52b522125b1",
   "metadata": {},
   "source": [
    "## Hierarchical Clustering + Enrichment\n",
    "- Perform enrichment tests (gprofiler) on hierarchical clustering of perturbation embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aaf8dc3-7e5b-4748-8685-fc8560bf7fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "\n",
    "# aggregate cell embeddings to perturbation embeddings\n",
    "cell_emb = adata.uns[\"cell_emb\"] @ adata.uns[\"topics\"]\n",
    "perts = []\n",
    "pert_emb = []\n",
    "for i in adata.obs[\"gene\"].unique():\n",
    "    if \"+\" not in i and i != \"nan\":\n",
    "        perts.append(i)\n",
    "        pert_emb.append(cell_emb[adata.obs[\"gene\"] == i].mean(axis=0))\n",
    "pert_emb = np.array(pert_emb)\n",
    "pert_emb_df = pd.DataFrame(pert_emb, index=perts)\n",
    "\n",
    "# Compute the pairwise distances\n",
    "df = pert_emb_df.drop(index=[\"non-targeting\"])\n",
    "\n",
    "distance = \"euclidean\"\n",
    "distances = pdist(df.values, metric=distance)\n",
    "\n",
    "# Convert the distances into a square distance matrix\n",
    "distance_matrix = pd.DataFrame(squareform(distances), index=df.index, columns=df.index)\n",
    "clustermap = sns.clustermap(distance_matrix, cmap=\"viridis_r\")\n",
    "clustermap.fig.suptitle(f\"Pairwise {distance} distance of perturbation latent vectors\")\n",
    "# clustermap.ax_row_dendrogram.set_visible(False)\n",
    "clustermap.ax_col_dendrogram.set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bc7de299-d751-480c-b275-23ffab452b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "\n",
    "den = scipy.cluster.hierarchy.dendrogram(\n",
    "    clustermap.dendrogram_col.linkage, labels=distance_matrix.index, color_threshold=1.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1902446f-8dcf-47fb-bd03-b3e31b40124c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract clusters and perform gprofiler\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "def get_cluster_classes(den, label=\"ivl\"):\n",
    "    cluster_idxs = defaultdict(list)\n",
    "    for c, pi in zip(den[\"color_list\"], den[\"icoord\"]):\n",
    "        for leg in pi[1:3]:\n",
    "            i = (leg - 5.0) / 10.0\n",
    "            if abs(i - int(i)) < 1e-5:\n",
    "                cluster_idxs[c].append(int(i))\n",
    "    cluster_classes = {}\n",
    "    for c, l in cluster_idxs.items():  # noqa\n",
    "        i_l = [den[label][i] for i in l]\n",
    "        cluster_classes[c] = i_l\n",
    "    return cluster_classes\n",
    "\n",
    "\n",
    "clusters = get_cluster_classes(den)\n",
    "# extract functions for clusters\n",
    "cluster_process = {}\n",
    "for c in clusters:\n",
    "    cluster_df = pd.DataFrame(clusters[c], columns=[\"gene_symbol\"])\n",
    "    res = get_gprofiler(cluster_df)\n",
    "    cluster_process[c] = res[res[\"p_value\"] <= 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cdb473cb-93a3-4752-9612-5a58fc81cd30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save grpofiler results\n",
    "with open(\n",
    "    \"figures/pert_embedding_cluster_gprofiler/scETM_replogle_pert_emb_gprofiler.pickle\",\n",
    "    \"wb\",\n",
    ") as handle:\n",
    "    pickle.dump(cluster_process, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6d699c-ee76-487e-80ff-807f1c112995",
   "metadata": {},
   "source": [
    "## PR Curve+AUC\n",
    "- AUCPR using prior graph as binary label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "62640fc7-412b-4a40-88da-bbd35fb23749",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               AUC\n",
      "StringDB  0.252574\n",
      "CORUM     0.240762\n",
      "                F1  Threshold\n",
      "StringDB  0.278519   0.818003\n",
      "CORUM     0.264378   0.839899\n"
     ]
    }
   ],
   "source": [
    "aucpr_df, f1_df, pr_dict = auprc(distance_matrix)\n",
    "print(aucpr_df)\n",
    "print(f1_df)\n",
    "aucpr_df.to_csv(\"figures/pert_embedding_recall/scETM_replogle_aucpr.csv\")\n",
    "f1_df.to_csv(\"figures/pert_embedding_recall/scETM_replogle_f1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fdff66a-8f20-4268-8520-d33a0fa4969a",
   "metadata": {},
   "source": [
    "# Interpretability of our learned latent space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ec54f80-8a0f-41f0-8c55-edd2b1fbfb3c",
   "metadata": {},
   "source": [
    "## Factor Enrichment\n",
    "- GSEA on log transformed latent-by-gene factors\n",
    "- Then associate each perturbation to its top latent factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35eb8169-15b1-4b5b-b926-d5b7238f372a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "factor_to_go = factor_enrichment_gsea(adata, np.abs(adata.uns[\"gene_emb\"]), fdr=5e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "814189ce-53f3-4716-834a-a4f0ea6efcf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter and add description to processes\n",
    "go_df = read_aws_csv(\"s3://pert-spectra/references/GO_to_Description.txt\")\n",
    "go_df.set_index(\"Term\", inplace=True)\n",
    "go_dict = go_df.to_dict()[\"Description\"]\n",
    "\n",
    "filtered_factor_to_go = {}\n",
    "for i in factor_to_go:\n",
    "    proc = factor_to_go[i]\n",
    "    proc[\"descr\"] = [go_dict[x] for x in proc[\"GO_ID\"]]\n",
    "    filtered_factor_to_go[i] = proc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a06b9724-d2a6-41e9-a38e-aac8d9f10818",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save latent enrichment results\n",
    "with open(\n",
    "    \"figures/factor_enrichments/scETM_replogle_factor_enrichment.pickle\", \"wb\"\n",
    ") as handle:\n",
    "    pickle.dump(filtered_factor_to_go, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47efd34e-e012-42b3-aa20-b5f7bc597832",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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