added mouse clocks

README.md

@@ -25,8 +25,9 @@ With a growing number of aging clocks and biomarkers of aging, comparing and ana
 
 ## 📝 To-Do List
 
+- [ ] Incorporate more murine DNA methylation clocks
 - [ ] Integrate scAge and scRNAseq clocks (and datasets)
-- [ ] Incorporate murine DNA methylation and proteomic clocks (and datasets)
+- [ ] Incorporate proteomic clocks (and datasets)
 
 ## ❓ Can't find an aging clock?
 

clocks/notebooks/bitage.ipynb

@@ -108,7 +108,7 @@
     "    'species': 'C elegans',\n",
     "    'data_type': 'transcriptomics',\n",
     "    'year': 2021,\n",
-    "    'implementation_approved_by_author(s)': '⌛',\n",
+    "    'implementation_approved_by_author(s)': '✅',\n",
     "    'preprocessing': weights_dict['preprocessing'], \n",
     "    'postprocessing': weights_dict['postprocessing'], \n",
     "    'citation': \"Meyer, David H., and Björn Schumacher. \\\"BiT age: A transcriptome‐based aging clock near the theoretical limit of accuracy.\\\" Aging cell 20.3 (2021): e13320.\",\n",

clocks/notebooks/join_metadata.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "id": "59eb29df-0597-4d45-b2e6-8825670effe2",
    "metadata": {},
    "outputs": [],
@@ -40,6 +40,14 @@
     "\n",
     "torch.save(combined_dictionary, '../metadata/all_clock_metadata.pt')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "047fee95-c914-4c3b-872b-c108696caa1b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

clocks/notebooks/meermultitissue.ipynb

@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fb157849-5454-4a60-8548-fff633fff764",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import pyaging as pya\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "46c6fc26-9a6b-4027-bd01-601b70eb401a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.system(\"curl -o coefficients.xlsx https://elifesciences.org/download/aHR0cHM6Ly9jZG4uZWxpZmVzY2llbmNlcy5vcmcvYXJ0aWNsZXMvNDA2NzUvZWxpZmUtNDA2NzUtc3VwcDMtdjIueGxzeA--/elife-40675-supp3-v2.xlsx?_hash=qzOMc4yUFACfDFG%2FlgxkFTHWt%2BSXSmP9zz1BM3oOTRM%3D\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bf89303a-6e7f-4585-a439-655fe0a79b05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You have to manually open Excel and convert to .csv (multi tissue, whole lifespan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b9f484b1-f501-41b7-9565-82e03bfe97dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('coefficients.csv')\n",
+    "\n",
+    "intercept = df['Position'].iloc[-1]\n",
+    "\n",
+    "df = df[0:-2]\n",
+    "\n",
+    "df['feature'] = df['Chromosome'].astype(str) + ':' + df['Position'].astype(int).astype(str)\n",
+    "df['coefficient'] = df['Weight']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a284fe99-dc47-4f0c-b2ff-274e136e7020",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = df['feature'].tolist()\n",
+    "\n",
+    "weights = torch.tensor(df['coefficient'].tolist()).unsqueeze(0)\n",
+    "intercept = torch.tensor([intercept])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7b4c3f6b-72af-4e99-84c4-65b8ef58c91d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearModel(\n",
+       "  (linear): Linear(in_features=435, out_features=1, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = pya.models.LinearModel(len(features))\n",
+    "\n",
+    "model.linear.weight.data = weights\n",
+    "model.linear.bias.data = intercept\n",
+    "\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e32706f0-ce07-455e-bb17-1993c1c0e152",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights_dict = {\n",
+    "    'preprocessing': \"times100\", \n",
+    "    'preprocessing_helper': None,\n",
+    "    'postprocessing': None,\n",
+    "    'postprocessing_helper': None,\n",
+    "    'features': features,\n",
+    "    'weight_dict': model.state_dict(),\n",
+    "}\n",
+    "\n",
+    "metadata_dict = {\n",
+    "    'species': 'Mus musculus',\n",
+    "    'data_type': 'methylation',\n",
+    "    'year': 2018,\n",
+    "    'implementation_approved_by_author(s)': '⌛',\n",
+    "    'preprocessing': weights_dict['preprocessing'], \n",
+    "    'postprocessing': weights_dict['postprocessing'], \n",
+    "    'citation': \"Meer, Margarita V., et al. \\\"A whole lifespan mouse multi-tissue DNA methylation clock.\\\" Elife 7 (2018): e40675.\",\n",
+    "    'doi': \"https://doi.org/10.7554/eLife.40675\",\n",
+    "    \"notes\": \"Predicts age in days\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "34136f3c-92b8-4641-a103-381d3a7dd857",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(weights_dict, '../weights/meermultitissue.pt')\n",
+    "torch.save(metadata_dict, '../metadata/meermultitissue.pt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "303e9b76-993f-4691-af9d-1151b3c7638f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.system(\"rm coefficients.xlsx\")\n",
+    "os.system(\"rm coefficients.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f111912-501e-4d2c-a592-1cc9829092dd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}