src/process_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# -----------------------------\n",
    "# Load + deduplicate\n",
    "# -----------------------------\n",
    "df = pd.read_csv(\"../data/ptmint_final.csv\")\n",
    "# df = df.drop_duplicates(subset=[\"Gene\", \"Int_gene\", \"Site\"]).copy()\n",
    "\n",
    "# -----------------------------\n",
    "# FIGURE 1: Basic distributions\n",
    "# -----------------------------\n",
    "fig1, axes = plt.subplots(2, 2, figsize=(10, 8))\n",
    "axes = axes.ravel()\n",
    "\n",
    "# 1 Interface\n",
    "interface_counts = df[\"Interface\"].value_counts()\n",
    "axes[0].bar(interface_counts.index.astype(str), interface_counts.values)\n",
    "axes[0].set_title(\"Interface (True vs False)\")\n",
    "axes[0].set_xlabel(\"Interface\")\n",
    "axes[0].set_ylabel(\"Count\")\n",
    "\n",
    "# 2 Effect\n",
    "effect_counts = df[\"Effect\"].value_counts()\n",
    "axes[1].bar(effect_counts.index.astype(str), effect_counts.values)\n",
    "axes[1].set_title(\"Effect\")\n",
    "axes[1].set_xlabel(\"Effect\")\n",
    "axes[1].set_ylabel(\"Count\")\n",
    "axes[1].tick_params(axis=\"x\", rotation=30)\n",
    "\n",
    "# 3 AA (S/T/Y)\n",
    "aa_counts = df[\"AA\"].value_counts().reindex([\"S\",\"T\",\"Y\"]).dropna()\n",
    "axes[2].bar(aa_counts.index, aa_counts.values)\n",
    "axes[2].set_title(\"Phosphorylation Residue\")\n",
    "axes[2].set_xlabel(\"AA\")\n",
    "axes[2].set_ylabel(\"Count\")\n",
    "\n",
    "# 4 Organism\n",
    "org_counts = df[\"Organism\"].value_counts().head(10)\n",
    "axes[3].barh(org_counts.index[::-1], org_counts.values[::-1])\n",
    "axes[3].set_title(\"Organism (Top 10)\")\n",
    "axes[3].set_xlabel(\"Count\")\n",
    "axes[3].set_ylabel(\"Organism\")\n",
    "\n",
    "fig1.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# FIGURE 2: Relationship plots\n",
    "# -----------------------------\n",
    "fig2, axes = plt.subplots(1, 3, figsize=(12, 4))\n",
    "axes = axes.ravel()\n",
    "\n",
    "# 1 Effect vs Interface\n",
    "ct1 = pd.crosstab(df[\"Interface\"], df[\"Effect\"])\n",
    "ct1.plot(kind=\"bar\", ax=axes[0])\n",
    "axes[0].set_title(\"Effect vs Interface\")\n",
    "axes[0].set_xlabel(\"Interface\")\n",
    "axes[0].set_ylabel(\"Count\")\n",
    "\n",
    "# 2 Effect vs AA\n",
    "ct2 = pd.crosstab(df[\"AA\"], df[\"Effect\"]).reindex([\"S\",\"T\",\"Y\"])\n",
    "ct2.plot(kind=\"bar\", ax=axes[1])\n",
    "axes[1].set_title(\"Effect vs AA\")\n",
    "axes[1].set_xlabel(\"AA\")\n",
    "axes[1].set_ylabel(\"Count\")\n",
    "\n",
    "# 3 Interface vs AA\n",
    "ct3 = pd.crosstab(df[\"AA\"], df[\"Interface\"]).reindex([\"S\",\"T\",\"Y\"])\n",
    "ct3.plot(kind=\"bar\", ax=axes[2])\n",
    "axes[2].set_title(\"Interface vs AA\")\n",
    "axes[2].set_xlabel(\"AA\")\n",
    "axes[2].set_ylabel(\"Count\")\n",
    "\n",
    "fig2.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# -----------------------------\n",
    "# Method (separate plot)\n",
    "# -----------------------------\n",
    "top_methods = df[\"Method\"].value_counts().head(6)\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.barh(top_methods.index[::-1], top_methods.values[::-1])\n",
    "plt.title(\"Top 6 Methods\")\n",
    "plt.xlabel(\"Count\")\n",
    "plt.ylabel(\"Method\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote FASTA: ../intermediate/seqwindows.fasta n_rows: 5156\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../data/ptmint_final.csv\")\n",
    "\n",
    "fasta_path = \"../intermediate/seqwindows.fasta\"\n",
    "\n",
    "with open(fasta_path, \"w\") as f:\n",
    "    for idx, seq in zip(df.index, df[\"SequenceWindow\"]):\n",
    "        seq = str(seq).replace(\"*\", \"\").strip().upper()\n",
    "        f.write(f\">{idx}\\n{seq}\\n\")\n",
    "\n",
    "print(\"Wrote FASTA:\", fasta_path, \"n_rows:\", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mmseqs easy-cluster \\\n",
    "  ../intermediate/seqwindows.fasta \\\n",
    "  ../intermediate/seqwin_clusters \\\n",
    "  ../intermediate/tmp_seqwin \\\n",
    "  --min-seq-id 0.4 -c 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster_ID\n",
      "1022    55\n",
      "1489    49\n",
      "2110    37\n",
      "2671    33\n",
      "759     30\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../data/ptmint_final.csv\")\n",
    "cluster_tsv = \"../intermediate/seqwin_clusters_cluster.tsv\"\n",
    "cl = pd.read_csv(cluster_tsv, sep=\"\\t\", header=None, names=[\"cluster_rep\", \"member\"])\n",
    "\n",
    "# your FASTA headers were df.index, so member should be an integer string\n",
    "cl[\"row_id\"] = cl[\"member\"].astype(int)\n",
    "\n",
    "# map each row -> cluster representative id\n",
    "row_to_cluster = dict(zip(cl[\"row_id\"], cl[\"cluster_rep\"]))\n",
    "\n",
    "# IMPORTANT: This assumes df row order matches the original FASTA indexing.\n",
    "# If you saved df to CSV and reloaded, df.index is 0..n-1 again (usually OK).\n",
    "df[\"cluster_ID\"] = df.index.map(row_to_cluster)\n",
    "\n",
    "print(df[\"cluster_ID\"].value_counts().head())\n",
    "\n",
    "df.to_csv(\"../data/ptmint_final_clustered.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# get unique clusters\n",
    "clusters = df[\"cluster_ID\"].drop_duplicates().values\n",
    "\n",
    "# shuffle\n",
    "np.random.seed(42)\n",
    "np.random.shuffle(clusters)\n",
    "\n",
    "# compute split boundaries\n",
    "n = len(clusters)\n",
    "train_cut = int(0.8 * n)\n",
    "val_cut = int(0.9 * n)\n",
    "\n",
    "train_clusters = clusters[:train_cut]\n",
    "val_clusters = clusters[train_cut:val_cut]\n",
    "test_clusters = clusters[val_cut:]\n",
    "\n",
    "# create split column\n",
    "df[\"split\"] = \"train\"\n",
    "\n",
    "df.loc[df[\"cluster_ID\"].isin(val_clusters), \"split\"] = \"validation\"\n",
    "df.loc[df[\"cluster_ID\"].isin(test_clusters), \"split\"] = \"test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/ptmint_all_with_splits.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only phosphorylation sites on the interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[\n",
    "    ((df['site_in_interface'] == True) | (df['Interface'] == True)) & \n",
    "    (df['PTM'] == 'Phos')\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2260198/681567452.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"split\"] = \"train\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# get unique clusters\n",
    "clusters = df[\"cluster_ID\"].drop_duplicates().values\n",
    "\n",
    "# shuffle\n",
    "np.random.seed(42)\n",
    "np.random.shuffle(clusters)\n",
    "\n",
    "# compute split boundaries\n",
    "n = len(clusters)\n",
    "train_cut = int(0.8 * n)\n",
    "val_cut = int(0.9 * n)\n",
    "\n",
    "train_clusters = clusters[:train_cut]\n",
    "val_clusters = clusters[train_cut:val_cut]\n",
    "test_clusters = clusters[val_cut:]\n",
    "\n",
    "# create split column\n",
    "df[\"split\"] = \"train\"\n",
    "\n",
    "df.loc[df[\"cluster_ID\"].isin(val_clusters), \"split\"] = \"validation\"\n",
    "df.loc[df[\"cluster_ID\"].isin(test_clusters), \"split\"] = \"test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/ptmint_interface_phospho_with_splits.csv\", index=False)"
   ]
  }
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