Upload Precious3GPT_example.ipynb

Precious3GPT_example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "c7317218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "from copy import copy as cp\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c022e07b",
+   "metadata": {},
+   "source": [
+    "## Authorize with the endpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f1272e3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "API_URL = \"https://YOUR.ENDPOINT.aws.endpoints.huggingface.cloud\"\n",
+    "headers = {\n",
+    "    \"Accept\" : \"application/json\",\n",
+    "    \"Authorization\": \"Bearer hf_YOUR_TOKEN\",\n",
+    "    \"Content-Type\": \"application/json\"\n",
+    "}\n",
+    "\n",
+    "def query(payload):\n",
+    "    response = requests.post(API_URL, headers=headers, json=payload)\n",
+    "    return response.json()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "082c3300",
+   "metadata": {},
+   "source": [
+    "## Construct the query\n",
+    "Instructions define what type of experiment you are trying to simulate with P3GPT.<br>\n",
+    "Key instructions enabled at this endpoint include:\n",
+    "- <font size=\"4\">**`disease2diff2disease`**</font>: For tasks that are equivalent to case-control cross-sectional settings. E.g. the generation of DEGs for a medical condition;\n",
+    "- <font size=\"4\">**`compound2diff2compound `**</font>: For compound screening tasks. E.g. propose a compound that can selectively methylate certain gene promoters;\n",
+    "- <font size=\"4\">**`age_group2diff2age_group`**</font>: For task on aging-related omics dynamics. E.g. identify genes that are up-/down-regulated in older vs younger adults. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "fd84fc60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = {'instruction': ['age_group2diff2age_group','compound2diff2compound'], \n",
+    "          # This is a chemical screening experiment in a particular age group, \n",
+    "          # so you'll need to use 2 intructions\n",
+    "          'tissue': 'lung',\n",
+    "          'age': 70,\n",
+    "          'cell': '',\n",
+    "          'efo': 'EFO_0000768', #pulmonary fibrosis\n",
+    "          'datatype': 'expression', # we want to get DEGs\n",
+    "          'drug': 'curcumin',\n",
+    "          'dose': '',\n",
+    "          'time': '',\n",
+    "          'case': ['70.0-80.0', '80.0-90.0'], # define the age groups of interest\n",
+    "          'control': '', # left blank since no healthy controls participate in this experiment\n",
+    "          'dataset_type': '',\n",
+    "          'gender': 'm',\n",
+    "          'species': 'human',\n",
+    "          'up': [], # left blank to be filled in by P3GPT\n",
+    "          'down': []\n",
+    "        }\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "609bd3c0",
+   "metadata": {},
+   "source": [
+    "## Execution modes\n",
+    "- <font size=\"4\">**`meta2diff`**</font>: `compound2diff2compound` can be executed either way. This mode tells P3GPT to return differentially expressed genes and not compounds;\n",
+    "- <font size=\"4\">**`diff2compound`**</font>: The reverse of the `meta2diff` mode. Make sure to fill in 'up' and 'down' in the prompt first!\n",
+    "- <font size=\"4\">**`meta2diff2compound`**</font>: Runs `meta2diff` first and applies `diff2compound` to its output. This is mostly for utility reasons — you get to run P3GPT twice with one call.\n",
+    "\n",
+    "As an LLM, P3GPT is trained to fill in the blanks in its prompt pointed at by the instructions. Its native output has the same structure as the input prompt.<br>\n",
+    "Modes do not belong in the prompt and are used for parsing P3GPT's output so that only the expected part of the completed prompt is presented to the user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "c6280337",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_sample = {'inputs': prompt,\n",
+    "                 'mode': 'meta2diff', # this is a chemical screening experiment \n",
+    "                 'parameters': {'temperature': 0.4,\n",
+    "                                  'top_p': 0.8,\n",
+    "                                  'top_k': 3550,\n",
+    "                                  'n_next_tokens': 20}\n",
+    "                 }\n",
+    "output = query(config_sample) # send request to Hugging Face"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "47a3f882",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['output', 'mode', 'message', 'input'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(output.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "5408079c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Done!'"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# successful generation\n",
+    "output['message']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "f51d4314",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'[BOS]<age_group2diff2age_group><compound2diff2compound><tissue>lung </tissue><age_individ>70 </age_individ><cell></cell><efo>EFO_0000768 </efo><datatype>expression </datatype><drug>curcumin </drug><dose></dose><time></time><case>70.0-80.0 80.0-90.0 </case><control></control><dataset_type></dataset_type><gender>m </gender><species>human </species>'"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this is what actual P3GPT input looks like\n",
+    "# NB: there is no 'mode' in the prompt.  \n",
+    "output['input']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "08c9f49a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Up-regulated genes:\n",
+      "MUC5B; AHSP; ALAS2; SLC4A1; CDHR5; NXF2B; CYP4F3; LGALS7B; FBN3; NTS; CYSTM1; ORM2; ASL; CD177; GLRX5; H4C3; NDUFA3; TUBA4B; EPB42; GCHFR\n",
+      "\n",
+      "Down-regulated genes:\n",
+      "KRT6A; KRT5; KRT15; KRT14; KRT6B; DSG3; CALML3; S100A7; SERPINB5; SPRR2A; SPRR3; LY6D; TMEM45A; KRT16; S100A9; GOLGA8A; SPINK6; CXCL10; CXCL9; CSTA\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# output gene symbols\n",
+    "genes_up, genes_dn = output['output']['up'][0], output['output']['down'][0]\n",
+    "print(\"Up-regulated genes:\")\n",
+    "print(*genes_up[:20], sep = \"; \",end='\\n\\n')\n",
+    "print(\"Down-regulated genes:\")\n",
+    "print(*genes_dn[:20], sep = \"; \",end='\\n\\n')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "f6910a3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now, let's do the opposite and get a compounds based on these DEG lists\n",
+    "# to do that, we only need a couple changes to the original prompt\n",
+    "prompt2 = cp(prompt)\n",
+    "prompt2.update({\n",
+    "                'drug':'',\n",
+    "                'up':genes_up,\n",
+    "                'down':genes_dn\n",
+    "              })\n",
+    "# remember to reverse meta2diff!\n",
+    "config_sample.update({'mode':'diff2compound',\n",
+    "                      'inputs':prompt2})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "e791e285",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = query(config_sample) # send request to Hugging Face"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "8ae15313",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['output', 'compounds', 'raw_output', 'mode', 'message', 'input'])"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "5f35f00c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "artemisinin; todralazine; dyphylline; esmolol; formestane; z160; netupitant; brd-k89304341; isoprenaline\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(*output['compounds'][0], sep='; ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "5d883cf8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# alternatively, use the meta2diff2compound to get straigth to compounds\n",
+    "prompt3 = cp(prompt)\n",
+    "prompt3.update({'instruction':['compound2diff2compound']})\n",
+    "config_sample.update({'mode':'meta2diff2compound',\n",
+    "                      'inputs':prompt3})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "c2adb995",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = query(config_sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "99da6eb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'instruction': ['compound2diff2compound'],\n",
+       " 'tissue': 'lung',\n",
+       " 'age': 70,\n",
+       " 'cell': '',\n",
+       " 'efo': 'EFO_0000768',\n",
+       " 'datatype': 'expression',\n",
+       " 'drug': '',\n",
+       " 'dose': '',\n",
+       " 'time': '',\n",
+       " 'case': ['70.0-80.0', '80.0-90.0'],\n",
+       " 'control': '',\n",
+       " 'dataset_type': '',\n",
+       " 'gender': 'm',\n",
+       " 'species': 'human',\n",
+       " 'up': [],\n",
+       " 'down': []}"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "ac9c4890",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output': [None],\n",
+       " 'mode': 'meta2diff2compound',\n",
+       " 'message': '62149 is not in list',\n",
+       " 'input': '[BOS]<compound2diff2compound><tissue>lung </tissue><age_individ>70 </age_individ><cell></cell><efo>EFO_0000768 </efo><datatype>expression </datatype><drug></drug><dose></dose><time></time><case>70.0-80.0 80.0-90.0 </case><control></control><dataset_type></dataset_type><gender>m </gender><species>human </species>'}"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "09ec4fe2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Up-regulated genes:\n",
+      "MUC5B; AHSP; ALAS2; SLC4A1; CDHR5; NXF2B; CYP4F3; LGALS7B; FBN3; NTS; CYSTM1; ORM2; ASL; CD177; GLRX5; H4C3; NDUFA3; TUBA4B; EPB42; GCHFR; KLF1; CFAP119; TRAPPC2L; DMTN; PDZK1IP1; SEM1; PCYT2; SERF2; CDC20; DAD1; MPC2; EMC3; BOLA1; CMTM5; PGD; EBP; GUK1; NDUFB7; UQCR11; LGALS9C; KEL; HBQ1; TUBB2A; RBX1; TMEM141; F8A1; COX7B; TMEM258; NDUFA7; MYL6; UQCRQ; MRPS24; HPGD; BOLA2B; KRTAP19-4; ATP5MF; RPL29; RPP25L; WDR83OS; FAU; UXT; ZNHIT1; SLC6A8\n",
+      "\n",
+      "Down-regulated genes:\n",
+      "KRT6A; KRT5; KRT15; KRT14; KRT6B; DSG3; CALML3; S100A7; SERPINB5; SPRR2A; SPRR3; LY6D; TMEM45A; KRT16; S100A9; GOLGA8A; SPINK6; CXCL10; CXCL9; CSTA; DSC3; APOL1; CXCL8; PKIA; MYBL1; CYP26B1; POSTN; THBS1; ARL14; UPK1B; CXCL13; CXCL6; C1R; COL14A1; TNFAIP2; TIMP1; VEGFC; C1QB; COL15A1; MGP; BICC1; S100A2; XIST; MARCKS; TLR2; TYMP; RPS4Y1; COL1A1; KLF6; KRT17; FBN1; STK32B; KDM5D; SPP1; APOD; THBS2; EIF1AY; CD163; CCL8; SYNM; CD44; HSPA9; CD14; SOCS3; HSPA6; MCL1; ALOX5AP; PBX3; DDX21; IRF8; HMGA1; MAFB; RGS1; SERPINE1; FKBP5; NOVA1; GFPT2; RRP12; AGTR1; C3AR1; GBP1; CCL18; TLR4; IGSF6; MSMB; SERPINA3; HLA-DQA1; HSPB8; SLC2A1; FOXD1; MS4A14; NAMPT; FYB1; TCAF1; NCF2; SERPINA1; F13A1; GBP3; FHL2; VSIG4; IFI16; MRC1\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(\"Up-regulated genes:\")\n",
+    "print(*output['output']['up'][0], sep='; ', end=\"\\n\\n\")\n",
+    "print(\"Down-regulated genes:\")\n",
+    "print(*output['output']['down'][0], sep='; ', end=\"\\n\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}