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"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "47fa042d",
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "cannot import name 'Modules' from 'models' (unknown location)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 26\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msrc\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexp_optimization\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpopen\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Auto_popen\n\u001b[1;32m 28\u001b[0m abs_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./src/mrl_te_optimization/log/Backbone/RL_hard_share/3M/small_repective_filed_strides1113.ini\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 29\u001b[0m Configuration \u001b[38;5;241m=\u001b[39m Auto_popen(abs_path)\n",
"File \u001b[0;32m~/Documents/bilkent/utrgan/bioinformatics advances/code/UTRGAN/src/exp_optimization/popen.py:5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Modules\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mconfigparser\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlogging\u001b[39;00m\n",
"\u001b[0;31mImportError\u001b[0m: cannot import name 'Modules' from 'models' (unknown location)"
]
}
],
"source": [
"import requests\n",
"import json\n",
"import time\n",
"import numpy as np\n",
"import os\n",
"from re import A, L\n",
"import numpy as np\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import torch\n",
"import tensorflow as tf\n",
"import tensorflow.keras.backend as K\n",
"from tensorflow.keras import Model\n",
"from tensorflow.keras.models import load_model\n",
"import sys\n",
"import argparse\n",
"from src.exp_optimization.util import *\n",
"from src.exp_optimization.framepool import *\n",
"\n",
"tf.compat.v1.enable_eager_execution()\n",
"\n",
"from Bio import SeqIO\n",
"import pandas as pd\n",
"import numpy as np\n",
"import requests, sys\n",
"from src.exp_optimization.popen import Auto_popen\n",
"\n",
"abs_path = './src/mrl_te_optimization/log/Backbone/RL_hard_share/3M/small_repective_filed_strides1113.ini'\n",
"Configuration = Auto_popen(abs_path)\n",
"\n",
"np.random.seed(25)\n",
"\n",
"BATCH_SIZE = 100\n",
"N_GENES = 8\n",
"LR = 0.001\n",
"GPU = '0'\n",
"STEPS = 10\n",
"\n",
"if GPU == '-1':\n",
" device = 'cpu'\n",
"else:\n",
" os.environ['CUDA_VISIBLE_DEVICES'] = GPU\n",
" device = 'cuda'\n",
"\n",
"SEQ_BATCH = N_GENES\n",
"UTR_LEN = 128\n",
"DIM = 40\n",
"gpath = './models/checkpoint_3000.h5'\n",
"mrl_path = './models/utr_model_combined_residual_new.h5'\n",
"exp_path = './models/humanMedian_trainepoch.11-0.426.h5'\n",
"tpath = './src/exp_optimization/script/checkpoint/RL_hard_share_MTL/3R/schedule_MTL-model_best_cv1.pth'\n",
"LR = np.exp(-int(LR))\n",
"\n",
"gene_names = [\"MYOC\", \"TIGD4\", \"ATP6V1B2\", \"TAGLN\", \"COX7A2L\", \"IFNGR2\", \"TNFRSF21\", \"SETD6\"]\n",
"\n",
"target_genes = [\"ANTXR2\", \"NFIL3\", \"UNC13D\", \"DHRS2\", \"RPS13\", \"HBD\", \"METAP1D\", \"NCALD\"]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3a95c1ae",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 387\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mGenerated \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_utrs\u001b[38;5;250m \u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;250m \u001b[39mn_genes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m modified sequences (expected: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexpected_count\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m all_modified_sequences\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconvert_model\u001b[39m(model_:\u001b[43mModel\u001b[49m):\n\u001b[1;32m 389\u001b[0m input_ \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mInput(shape\u001b[38;5;241m=\u001b[39m( \u001b[38;5;241m10500\u001b[39m, \u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m input_\n",
"\u001b[0;31mNameError\u001b[0m: name 'Model' is not defined"
]
}
],
"source": [
"\n",
"\n",
"def reverse_complement(sequence):\n",
" \"\"\"Compute the reverse complement of a DNA sequence.\"\"\"\n",
" complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C', \n",
" 'a': 't', 't': 'a', 'c': 'g', 'g': 'c', 'N': 'N', 'n': 'N'}\n",
" return ''.join(complement.get(base, 'N') for base in reversed(sequence))\n",
"\n",
"class GeneInfoRetriever:\n",
" def __init__(self):\n",
" self.base_url = \"https://rest.ensembl.org\"\n",
" self.headers = {\"Content-Type\": \"application/json\"}\n",
" self.sleep_time = 0.5 # Respect Ensembl API rate limits\n",
"\n",
" def _make_request(self, endpoint):\n",
" \"\"\"Make a request to the Ensembl REST API.\"\"\"\n",
" url = self.base_url + endpoint\n",
" try:\n",
" response = requests.get(url, headers=self.headers)\n",
" time.sleep(self.sleep_time)\n",
" if response.status_code == 200:\n",
" return response.json()\n",
" else:\n",
" print(f\"Error: {response.status_code} - {response.text}\")\n",
" return None\n",
" except Exception as e:\n",
" print(f\"Request error: {e}\")\n",
" return None\n",
"\n",
" def get_gene_id(self, gene_symbol, species=\"homo_sapiens\"):\n",
" \"\"\"Retrieve the Ensembl gene ID for a gene symbol.\"\"\"\n",
" endpoint = f\"/lookup/symbol/{species}/{gene_symbol}\"\n",
" response = self._make_request(endpoint)\n",
" return response.get(\"id\") if response else None\n",
"\n",
" def get_gene_coordinates(self, gene_id):\n",
" \"\"\"Retrieve genomic coordinates for a gene ID.\"\"\"\n",
" endpoint = f\"/lookup/id/{gene_id}?expand=1\"\n",
" response = self._make_request(endpoint)\n",
" if response:\n",
" return {\n",
" \"chromosome\": response.get(\"seq_region_name\"),\n",
" \"start\": response.get(\"start\"),\n",
" \"end\": response.get(\"end\"),\n",
" \"strand\": response.get(\"strand\")\n",
" }\n",
" return None\n",
"\n",
" def get_tss_and_utr(self, gene_id):\n",
" \"\"\"Retrieve TSS and 5' UTR coordinates for the canonical transcript.\"\"\"\n",
" endpoint = f\"/lookup/id/{gene_id}?expand=1&utr=1\"\n",
" response = self._make_request(endpoint)\n",
" if not response or \"Transcript\" not in response:\n",
" return None\n",
"\n",
" # Find canonical transcript\n",
" canonical_transcript = None\n",
" for transcript in response[\"Transcript\"]:\n",
" if transcript.get(\"is_canonical\", 0) == 1:\n",
" canonical_transcript = transcript\n",
" break\n",
" if not canonical_transcript:\n",
" for transcript in response[\"Transcript\"]:\n",
" if transcript.get(\"biotype\") == \"protein_coding\":\n",
" canonical_transcript = transcript\n",
" break\n",
" if not canonical_transcript:\n",
" canonical_transcript = response[\"Transcript\"][0] if response[\"Transcript\"] else None\n",
"\n",
" if not canonical_transcript:\n",
" return None\n",
"\n",
" # Determine TSS and 5' UTR\n",
" strand = canonical_transcript.get(\"strand\")\n",
" tss = canonical_transcript[\"start\"] if strand == 1 else canonical_transcript[\"end\"]\n",
" five_prime_utr = None\n",
"\n",
" if \"UTR\" in canonical_transcript:\n",
" for utr in canonical_transcript[\"UTR\"]:\n",
" if utr.get(\"object_type\") == \"five_prime_UTR\":\n",
" five_prime_utr = {\n",
" \"start\": utr.get(\"start\"),\n",
" \"end\": utr.get(\"end\")\n",
" }\n",
" break\n",
"\n",
" # Verify TSS matches 5' UTR start\n",
" if five_prime_utr:\n",
" expected_tss = five_prime_utr[\"start\"] if strand == 1 else five_prime_utr[\"end\"]\n",
" if expected_tss != tss:\n",
" print(f\"Warning: Adjusting TSS from {tss} to match 5' UTR {'start' if strand == 1 else 'end'} ({expected_tss})\")\n",
" tss = expected_tss\n",
"\n",
" return {\n",
" \"tss\": tss,\n",
" \"strand\": strand,\n",
" \"chromosome\": canonical_transcript.get(\"seq_region_name\"),\n",
" \"five_prime_utr\": five_prime_utr,\n",
" \"transcript_id\": canonical_transcript.get(\"id\")\n",
" }\n",
"\n",
" def get_promoter_sequence(self, gene_id, upstream=8000, downstream=4000):\n",
" \"\"\"Retrieve sequence around TSS (8kb upstream, 4kb downstream).\"\"\"\n",
" tss_info = self.get_tss_and_utr(gene_id)\n",
" if not tss_info:\n",
" return None, None\n",
"\n",
" chromosome = tss_info[\"chromosome\"]\n",
" strand = tss_info[\"strand\"]\n",
" tss_position = tss_info[\"tss\"]\n",
"\n",
" # Calculate region based on strand\n",
" if strand == 1:\n",
" seq_start = tss_position - upstream\n",
" seq_end = tss_position + downstream - 1\n",
" else:\n",
" seq_start = tss_position - downstream\n",
" seq_end = tss_position + upstream - 1\n",
"\n",
" seq_start = max(1, seq_start)\n",
"\n",
" # Store sequence coordinates\n",
" sequence_coords = {\n",
" \"chromosome\": chromosome,\n",
" \"start\": seq_start,\n",
" \"end\": seq_end,\n",
" \"strand\": 1 if strand == 1 else -1\n",
" }\n",
"\n",
" # Validate 5' UTR inclusion\n",
" if tss_info[\"five_prime_utr\"]:\n",
" utr_start = tss_info[\"five_prime_utr\"][\"start\"]\n",
" utr_end = tss_info[\"five_prime_utr\"][\"end\"]\n",
" if not (seq_start <= utr_start <= seq_end and seq_start <= utr_end <= seq_end):\n",
" print(f\"Warning: 5' UTR ({utr_start}-{utr_end}) not fully within sequence ({seq_start}-{seq_end})\")\n",
"\n",
" # Get sequence\n",
" strand_str = \"1\" if strand == 1 else \"-1\"\n",
" endpoint = f\"/sequence/region/human/{chromosome}:{seq_start}..{seq_end}:{strand_str}\"\n",
" response = self._make_request(endpoint)\n",
" return response.get(\"seq\") if response else None, sequence_coords\n",
"\n",
" def get_gene_info(self, gene_symbol, species=\"homo_sapiens\", output_json=\"gene_info.json\"):\n",
" \n",
" if not os.path.exists(os.path.join('./.cache/',f\"{gene_symbol}_info.json\")):\n",
"\n",
" \"\"\"Retrieve and save promoter sequence, TSS, 5' UTR, and coordinates.\"\"\"\n",
" # Get gene ID\n",
" gene_id = self.get_gene_id(gene_symbol, species)\n",
" if not gene_id:\n",
" return {\"error\": f\"Gene {gene_symbol} not found\"}\n",
"\n",
" # Get TSS and 5' UTR\n",
" tss_info = self.get_tss_and_utr(gene_id)\n",
" if not tss_info:\n",
" return {\"error\": \"Could not retrieve TSS or transcript information\"}\n",
"\n",
" # Get promoter sequence and coordinates\n",
" promoter_sequence, sequence_coords = self.get_promoter_sequence(gene_id)\n",
" if not promoter_sequence:\n",
" return {\"error\": \"Could not retrieve promoter sequence\"}\n",
"\n",
" # Compile gene information\n",
" gene_info = {\n",
" \"gene_symbol\": gene_symbol,\n",
" \"gene_id\": gene_id,\n",
" \"promoter_sequence\": promoter_sequence,\n",
" \"sequence_length\": len(promoter_sequence),\n",
" \"sequence_coordinates\": sequence_coords,\n",
" \"tss\": {\n",
" \"chromosome\": tss_info[\"chromosome\"],\n",
" \"position\": tss_info[\"tss\"],\n",
" \"strand\": \"+\" if tss_info[\"strand\"] == 1 else \"-\"\n",
" },\n",
" \"five_prime_utr\": tss_info[\"five_prime_utr\"],\n",
" \"transcript_id\": tss_info[\"transcript_id\"]\n",
" }\n",
"\n",
" # Save to JSON\n",
" try:\n",
" os.makedirs(os.path.dirname('./.cache/'), exist_ok=True)\n",
" with open(os.path.join('./.cache/',f\"{gene_symbol}_info.json\"), \"w\") as f:\n",
" json.dump(gene_info, f, indent=2)\n",
" print(f\"Saved gene information to {output_json}\")\n",
" except Exception as e:\n",
" print(f\"Error saving JSON: {e}\")\n",
"\n",
" else:\n",
"\n",
" with open(os.path.join('./.cache/',f\"{gene_symbol}_info.json\"), \"r\") as f:\n",
" gene_info = json.load(f)\n",
"\n",
" return gene_info\n",
"\n",
" def reverse_complement(self, sequence):\n",
" \"\"\"Compute the reverse complement of a DNA sequence.\"\"\"\n",
" complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C', \n",
" 'a': 't', 't': 'a', 'c': 'g', 'g': 'c', 'N': 'N', 'n': 'N'}\n",
" return ''.join(complement.get(base, 'N') for base in reversed(sequence))\n",
"\n",
" def replace_utr_in_sequence(self, gene_info_file, generated_utrs, target_length=10500, output_prefix=\"modified_sequence\", write_json=False, verbose=False):\n",
" \"\"\"\n",
" Replace original 5' UTR with generated UTRs, ensuring 10,500nt output.\n",
" \n",
" Parameters:\n",
" gene_info_file (str): Path to JSON file with gene information\n",
" generated_utrs (list): List of generated 5' UTR sequences (64-128nt)\n",
" target_length (int): Desired output sequence length (default: 10500)\n",
" output_prefix (str): Prefix for output JSON files\n",
" \n",
" Returns:\n",
" list: List of modified sequences with metadata\n",
" \"\"\"\n",
" try:\n",
" # Read gene information\n",
" with open(gene_info_file, \"r\") as f:\n",
" gene_info = json.load(f)\n",
"\n",
" original_sequence = gene_info[\"promoter_sequence\"]\n",
" strand = gene_info[\"tss\"][\"strand\"]\n",
" tss_position = gene_info[\"tss\"][\"position\"]\n",
" sequence_coords = gene_info[\"sequence_coordinates\"]\n",
" seq_start = sequence_coords[\"start\"]\n",
" seq_end = sequence_coords[\"end\"]\n",
" five_prime_utr = gene_info[\"five_prime_utr\"]\n",
" gene_symbol = gene_info[\"gene_symbol\"]\n",
" transcript_id = gene_info[\"transcript_id\"]\n",
"\n",
" if not five_prime_utr:\n",
" print(f\"Error: No 5' UTR information available for {gene_symbol}\")\n",
" return []\n",
"\n",
" # Calculate original 5' UTR position in sequence\n",
" if strand == \"+\":\n",
" utr_start_genomic = five_prime_utr[\"start\"]\n",
" utr_end_genomic = five_prime_utr[\"end\"]\n",
" utr_start_seq = utr_start_genomic - seq_start\n",
" utr_end_seq = utr_end_genomic - seq_start\n",
" else:\n",
" utr_start_genomic = five_prime_utr[\"end\"] # TSS\n",
" utr_end_genomic = five_prime_utr[\"start\"]\n",
" utr_start_seq = seq_end - utr_start_genomic\n",
" utr_end_seq = seq_end - utr_end_genomic\n",
"\n",
" # Validate UTR positions\n",
" seq_length = len(original_sequence)\n",
" if not (0 <= utr_start_seq <= seq_length and 0 <= utr_end_seq <= seq_length):\n",
" print(f\"Error: 5' UTR coordinates (seq indices {utr_start_seq}-{utr_end_seq}) out of sequence bounds (0-{seq_length}) for {gene_symbol}\")\n",
" return []\n",
"\n",
" original_utr_length = abs(utr_end_genomic - utr_start_genomic) + 1\n",
" if verbose:\n",
" print(f\"Original 5' UTR length for {gene_symbol}: {original_utr_length} nt\")\n",
"\n",
" modified_sequences = []\n",
" for i, new_utr in enumerate(generated_utrs):\n",
" new_utr_length = len(new_utr)\n",
" if not 64 <= new_utr_length <= 128:\n",
" if verbose:\n",
" print(f\"Warning: Generated UTR {i+1} length ({new_utr_length}) outside 64-128nt range for {gene_symbol}\")\n",
" continue\n",
"\n",
" # Construct new sequence\n",
" if strand == \"+\":\n",
" new_sequence = (\n",
" original_sequence[:utr_start_seq] +\n",
" new_utr +\n",
" original_sequence[utr_end_seq + 1:]\n",
" )\n",
" new_utr_start_genomic = utr_start_genomic\n",
" new_utr_end_genomic = utr_start_genomic + new_utr_length - 1\n",
" if len(new_sequence) > target_length:\n",
" new_sequence = new_sequence[:target_length]\n",
" sequence_coords[\"end\"] = seq_start + target_length - 1\n",
" elif len(new_sequence) < target_length:\n",
" if verbose:\n",
" print(f\"Error: Sequence too short ({len(new_sequence)} nt) after UTR replacement for {gene_symbol}\")\n",
" continue\n",
" else:\n",
" new_utr_rc = reverse_complement(new_utr)\n",
" new_sequence = (\n",
" original_sequence[:min(utr_start_seq, utr_end_seq)] +\n",
" new_utr_rc +\n",
" original_sequence[max(utr_start_seq, utr_end_seq) + 1:]\n",
" )\n",
" new_utr_start_genomic = utr_start_genomic\n",
" new_utr_end_genomic = utr_start_genomic - new_utr_length + 1\n",
" if len(new_sequence) > target_length:\n",
" trim_amount = len(new_sequence) - target_length\n",
" new_sequence = new_sequence[trim_amount:]\n",
" sequence_coords[\"start\"] = seq_start + trim_amount\n",
" elif len(new_sequence) < target_length:\n",
" if verbose:\n",
" print(f\"Error: Sequence too short ({len(new_sequence)} nt) after UTR replacement for {gene_symbol}\")\n",
" continue\n",
"\n",
" # Store modified sequence and metadata\n",
" modified_info = {\n",
" \"gene_symbol\": gene_symbol,\n",
" \"transcript_id\": transcript_id,\n",
" \"modified_sequence\": new_sequence,\n",
" \"sequence_length\": len(new_sequence),\n",
" \"sequence_coordinates\": sequence_coords.copy(),\n",
" \"tss\": gene_info[\"tss\"],\n",
" \"five_prime_utr\": {\n",
" \"start\": new_utr_start_genomic,\n",
" \"end\": new_utr_end_genomic,\n",
" \"sequence\": new_utr if strand == \"+\" else new_utr_rc\n",
" },\n",
" \"original_utr_length\": original_utr_length,\n",
" \"new_utr_length\": new_utr_length,\n",
" \"utr_index\": i + 1\n",
" }\n",
"\n",
" # Save to JSON\n",
" if write_json:\n",
" output_file = f\"{output_prefix}_{gene_symbol}_utr_{i+1}.json\"\n",
" try:\n",
" os.makedirs(os.path.dirname(output_file), exist_ok=True)\n",
" with open(output_file, \"w\") as f:\n",
" json.dump(modified_info, f, indent=2)\n",
" print(f\"Saved modified sequence {i+1} for {gene_symbol} to {output_file}\")\n",
" except Exception as e:\n",
" print(f\"Error saving modified sequence {i+1} for {gene_symbol}: {e}\")\n",
"\n",
" modified_sequences.append(modified_info[\"modified_sequence\"])\n",
"\n",
" return modified_sequences\n",
"\n",
" except Exception as e:\n",
" print(f\"Error processing UTR replacement for {gene_info.get('gene_symbol', 'unknown')}: {e}\")\n",
" return []\n",
"\n",
"\n",
" def replace_utr_in_multiple_sequences(self, gene_symbols, generated_utrs, target_length=10500, cache_dir=\"./.cache\", output_prefix=\"modified_sequence\", verbose=False):\n",
" \"\"\"\n",
" Replace 5' UTRs for multiple genes with generated UTRs.\n",
" \n",
" Parameters:\n",
" gene_symbols (list): List of gene names\n",
" generated_utrs (list): List of generated 5' UTR sequences (64-128nt)\n",
" target_length (int): Desired output sequence length (default: 10500)\n",
" cache_dir (str): Directory containing cached gene info JSON files\n",
" output_prefix (str): Prefix for output JSON files\n",
" \n",
" Returns:\n",
" list: List of n_utrs * n_genes modified sequences with metadata\n",
" \"\"\"\n",
" all_modified_sequences = []\n",
" n_utrs = len(generated_utrs)\n",
" n_genes = len(gene_symbols)\n",
"\n",
" for gene_symbol in gene_symbols:\n",
" json_file = os.path.join(cache_dir, f\"{gene_symbol}_info.json\")\n",
" if not os.path.exists(json_file):\n",
" print(f\"Error: Gene info file {json_file} not found\")\n",
" continue\n",
" \n",
" if verbose:\n",
" print(f\"\\nProcessing gene: {gene_symbol}\")\n",
" modified_sequences = self.replace_utr_in_sequence(\n",
" gene_info_file=json_file,\n",
" generated_utrs=generated_utrs,\n",
" target_length=target_length,\n",
" output_prefix=os.path.join(cache_dir, output_prefix)\n",
" )\n",
"\n",
" if modified_sequences:\n",
" all_modified_sequences.extend(modified_sequences)\n",
" else:\n",
" if verbose:\n",
" print(f\"No modified sequences generated for {gene_symbol}\")\n",
"\n",
" expected_count = n_utrs * n_genes\n",
"\n",
" if verbose:\n",
" print(f\"\\nGenerated {n_utrs * n_genes} modified sequences (expected: {expected_count})\")\n",
"\n",
" return all_modified_sequences\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def convert_model(model_:Model):\n",
"\n",
" input_ = tf.keras.layers.Input(shape=( 10500, 4))\n",
" input = input_\n",
" for i in range(len(model_.layers)-1):\n",
"\n",
" \n",
" if isinstance(model_.layers[i+1],tf.keras.layers.Concatenate):\n",
" paddings = tf.constant([[0,0],[0,6]])\n",
" output = tf.pad(input, paddings, 'CONSTANT')\n",
" input = output\n",
" else:\n",
" if not isinstance(model_.layers[i+1],tf.keras.layers.InputLayer):\n",
" output = model_.layers[i+1](input)\n",
" input = output\n",
"\n",
" if isinstance(model_.layers[i+1],tf.keras.layers.Conv1D):\n",
" pass\n",
"\n",
" model = tf.keras.Model(inputs=input_, outputs=output)\n",
" model.compile(loss=\"mse\", optimizer=\"adam\")\n",
" return model\n",
"\n",
"def one_hot(seq):\n",
" convert = True\n",
" if isinstance(seq, tf.Tensor):\n",
" seq = seq.numpy().astype(str)\n",
" convert = True\n",
"\n",
" num_seqs = len(seq)\n",
" seq_len = len(seq[0])\n",
" seqindex = {'A':0, 'C':1, 'G':2, 'T':3, 'a':0, 'c':1, 'g':2, 't':3}\n",
" seq_vec = np.zeros((num_seqs,seq_len,4), dtype='bool')\n",
" for i in range(num_seqs):\n",
" thisseq = seq[i]\n",
" for j in range(seq_len):\n",
" try:\n",
" seq_vec[i,j,seqindex[thisseq[j]]] = 1\n",
" except:\n",
" pass\n",
" \n",
" if convert:\n",
" seq_vec = tf.convert_to_tensor(seq_vec,dtype=tf.float32)\n",
"\n",
"\n",
" return seq_vec\n",
"\n",
"\n",
"def select_best(scores, seqs, gc_control=False, GC=-1, per_gene=False):\n",
" selected_scores = []\n",
" selected_seqs = []\n",
" if per_gene: \n",
"\n",
" scores = np.asarray(scores)\n",
" seqs = np.asarray(seqs)\n",
" \n",
" A, B, C = np.shape(scores)\n",
" selected_scores = []\n",
" selected_seqs = []\n",
" \n",
" for b in range(B):\n",
"\n",
" best_score = np.max(scores[0, b, :]) \n",
" best_seq = seqs[0, :] \n",
" \n",
" for a in range(1, A):\n",
" current_score = np.max(scores[a, b, :]) \n",
" \n",
" if current_score > best_score:\n",
" if gc_control:\n",
"\n",
" gc_content = get_gc_content(seqs[a, :])\n",
" if gc_content < GC:\n",
" best_score = current_score\n",
" best_seq = seqs[a, :]\n",
" best_a = a\n",
" else:\n",
" best_score = current_score\n",
" best_seq = seqs[a, :]\n",
" best_a = a\n",
" \n",
" selected_scores.append(best_score)\n",
" selected_seqs.append(best_seq)\n",
" \n",
"\n",
" selected_scores = np.array(selected_scores) \n",
" selected_seqs = np.array(selected_seqs) \n",
" else:\n",
" for i in range(len(scores[0])):\n",
" best = scores[1][i]\n",
" best_seq = seqs[1][i]\n",
" for j in range(len(scores)-1):\n",
" if scores[j+1][i] > best:\n",
" if gc_control:\n",
" if get_gc_content(seqs[j][i]) < GC:\n",
" best = scores[j+1][i]\n",
" best_seq = seqs[j+1][i]\n",
" else:\n",
" best = scores[j+1][i]\n",
" best_seq = seqs[j+1][i]\n",
"\n",
" selected_scores.append(best)\n",
" selected_seqs.append(best_seq)\n",
"\n",
" return selected_seqs, selected_scores"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9dcefb89",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
"WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
"Saved gene information to MYOC_info.json\n",
"Saved gene information to TIGD4_info.json\n",
"Saved gene information to ATP6V1B2_info.json\n",
"Saved gene information to TAGLN_info.json\n",
"Saved gene information to COX7A2L_info.json\n",
"Saved gene information to IFNGR2_info.json\n",
"Saved gene information to TNFRSF21_info.json\n",
"Saved gene information to SETD6_info.json\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 10/10 [00:40<00:00, 4.08s/it]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8,)\n",
"\n",
"Evaluation of Optimization on Original Genes (Log TPM):\n",
"\n",
"Expression Levels (Log TPM):\n",
" Average Initial Log TPM: -0.1834 (TPM: 0.6555)\n",
" Average Optimized Log TPM: -0.0183 (TPM: 0.9587)\n",
" Log TPM Difference: 0.1651\n",
" TPM Improvement: 0.3032 (+46.25% (increase))\n",
"Genes:\n",
"['MYOC', 'TIGD4', 'ATP6V1B2', 'TAGLN', 'COX7A2L', 'IFNGR2', 'TNFRSF21', 'SETD6']\n",
"Average Initial Expression: -0.1834263348579407\n",
"Best Expression: -0.018321754410862923\n",
"Error processing UTR replacement for DHRS2: ufunc 'add' did not contain a loop with signature matching types (dtype('<U8000'), dtype('<U128')) -> None\n",
"Error processing UTR replacement for METAP1D: ufunc 'add' did not contain a loop with signature matching types (dtype('<U8000'), dtype('<U128')) -> None\n",
"\n",
"Evaluation of Optimization on Target Genes (Log TPM):\n",
"Original Genes: ['MYOC', 'TIGD4', 'ATP6V1B2', 'TAGLN', 'COX7A2L', 'IFNGR2', 'TNFRSF21', 'SETD6']\n",
"Target Genes: ['ANTXR2', 'NFIL3', 'UNC13D', 'DHRS2', 'RPS13', 'HBD', 'METAP1D', 'NCALD']\n",
"\n",
"Expression Levels (Log TPM):\n",
" Average Initial Log TPM: -0.5353 (TPM: 0.2915)\n",
" Average Optimized Log TPM: -0.4488 (TPM: 0.3558)\n",
" Log TPM Difference: 0.0865\n",
" TPM Improvement: 0.0643 (+22.05% (increase))\n",
"\n",
"Per-Gene Expression Levels (Log TPM):\n",
" ANTXR2: Initial Log TPM = -0.1230 (TPM: 0.7534), Optimized Log TPM = -0.5100 (TPM: 0.3091), TPM Improvement = -0.4443 (-58.98% (decrease))\n",
" NFIL3: Initial Log TPM = 0.7915 (TPM: 6.1874), Optimized Log TPM = 0.3522 (TPM: 2.2503), TPM Improvement = -3.9371 (-63.63% (decrease))\n",
" UNC13D: Initial Log TPM = -0.4673 (TPM: 0.3409), Optimized Log TPM = 0.3383 (TPM: 2.1793), TPM Improvement = 1.8384 (+539.22% (increase))\n",
" DHRS2: Initial Log TPM = -1.1142 (TPM: 0.0769), Optimized Log TPM = -0.5517 (TPM: 0.2807), TPM Improvement = 0.2038 (+265.13% (increase))\n",
" RPS13: Initial Log TPM = 0.1343 (TPM: 1.3622), Optimized Log TPM = 0.0449 (TPM: 1.1090), TPM Improvement = -0.2533 (-18.59% (decrease))\n",
" HBD: Initial Log TPM = -1.3306 (TPM: 0.0467), Optimized Log TPM = -0.8894 (TPM: 0.1290), TPM Improvement = 0.0823 (+176.14% (increase))\n",
" METAP1D: Initial Log TPM = -1.1724 (TPM: 0.0672), Optimized Log TPM = -1.2720 (TPM: 0.0535), TPM Improvement = -0.0138 (-20.50% (decrease))\n",
" NCALD: Initial Log TPM = -1.0010 (TPM: 0.0998), Optimized Log TPM = -1.1027 (TPM: 0.0789), TPM Improvement = -0.0208 (-20.88% (decrease))\n"
]
}
],
"source": [
"\n",
"model = tf.keras.models.load_model(exp_path)\n",
"\n",
"model = convert_model(model)\n",
"\n",
"wgan = tf.keras.models.load_model(gpath)\n",
"\n",
"\"\"\"\n",
"Data:\n",
"\"\"\"\n",
"\n",
"\n",
"noise = tf.Variable(tf.random.normal(shape=[BATCH_SIZE,40]))\n",
"\n",
"tf.random.set_seed(25)\n",
"\n",
"diffs = []\n",
"init_exps = []\n",
"\n",
"opt_exps = []\n",
"\n",
"orig_vals = []\n",
"\n",
"noise = tf.Variable(tf.random.normal(shape=[BATCH_SIZE,40]))\n",
"noise_small = tf.random.normal(shape=[BATCH_SIZE,40],stddev=1e-5)\n",
"\n",
"optimizer = tf.keras.optimizers.Adam(learning_rate=LR)\n",
"\n",
"'''\n",
"Optimization takes place here.\n",
"'''\n",
"\n",
"bind_scores_list = []\n",
"bind_scores_means = []\n",
"sequences_list = []\n",
"\n",
"means = []\n",
"maxes = []\n",
"\n",
"iters_ = []\n",
"\n",
"OPTIMIZE = True\n",
"\n",
"DNA_SEL = False\n",
"\n",
"retriever = GeneInfoRetriever()\n",
"refs = []\n",
"for i in range(len(gene_names)):\n",
" output_json = f\"{gene_names[i]}_info.json\"\n",
"\n",
" if not os.path.exists(os.path.join('./.cache/',output_json)):\n",
"\n",
" # Retrieve gene information\n",
" gene_info = retriever.get_gene_info(gene_names[i], output_json=output_json)\n",
"\n",
" if \"error\" in gene_info:\n",
" print(f\"Error: {gene_info['error']}\")\n",
" else:\n",
" refs.append(gene_info[\"promoter_sequence\"]) \n",
" else:\n",
" with open(os.path.join('./.cache/',output_json), \"r\") as f:\n",
" gene_info = json.load(f)\n",
" refs.append(gene_info[\"promoter_sequence\"])\n",
"\n",
"sequences_init = wgan(noise)\n",
"\n",
"gen_seqs_init = sequences_init.numpy().astype('float')\n",
"\n",
"seqs_gen_init = recover_seq(gen_seqs_init, rev_rna_vocab)\n",
"\n",
"seqs_init = retriever.replace_utr_in_multiple_sequences(gene_names, seqs_gen_init, target_length=10500, cache_dir=\"./.cache\", output_prefix=\"modified_sequence\")\n",
"\n",
"seqs_init = one_hot(seqs_init)\n",
"\n",
"pred_init = model(seqs_init) \n",
"\n",
"pred_init = tf.reshape(pred_init,(SEQ_BATCH,-1))\n",
"\n",
"average_initial_prediction = tf.reduce_mean(pred_init,axis=0).numpy().astype('float')\n",
"\n",
"# %%\n",
"\n",
"seqs_collection = []\n",
"scores_collection = []\n",
"scores_collection_genes = []\n",
"if OPTIMIZE:\n",
"\n",
" iter_ = 0\n",
" for opt_iter in tqdm(range(STEPS)):\n",
" \n",
" with tf.GradientTape() as gtape:\n",
" gtape.watch(noise)\n",
" \n",
" sequences = wgan(noise)\n",
"\n",
" seqs_gen = recover_seq(sequences, rev_rna_vocab)\n",
" seqs_collection.append(seqs_gen)\n",
"\n",
" g1_ = tf.zeros_like(sequences)\n",
"\n",
" scores_collection_temp = []\n",
"\n",
" for gene in gene_names:\n",
"\n",
" seqs_dna = retriever.replace_utr_in_sequence(f\"./.cache/{gene}_info.json\", seqs_gen, target_length=10500, output_prefix=\"modified_sequence\") \n",
" \n",
" seqs = one_hot(seqs_dna)\n",
" \n",
" with tf.GradientTape() as ptape:\n",
" ptape.watch(seqs)\n",
"\n",
" pred = model(seqs)\n",
" t = tf.reshape(pred,(-1))\n",
" scores_collection_temp.append(t.numpy().astype('float'))\n",
" nt = t.numpy().astype('float')\n",
"\n",
" g1 = ptape.gradient(pred,seqs)\n",
" g1 = tf.math.scalar_mul(-1.0, g1)\n",
" g1 = tf.slice(g1,[0,7000,0],[-1,128,-1])\n",
"\n",
" tmp_g = g1.numpy().astype('float')\n",
" tmp_seqs = seqs_gen\n",
"\n",
" # Initialize tmp_lst with correct size\n",
" batch_size = min(len(tmp_seqs), tmp_g.shape[0])\n",
" tmp_lst = np.zeros(shape=(batch_size, 128, 5))\n",
"\n",
" # Loop on the batch size and update the UTR only\n",
" for i in range(batch_size):\n",
" len_ = min(len(tmp_seqs[i]), tmp_g.shape[1]) # Prevent exceeding tmp_g's dimensions\n",
" edited_g = tmp_g[i][:len_, :]\n",
" edited_g = np.pad(edited_g, ((0, 128-len_), (0, 1)), 'constant')\n",
" tmp_lst[i] = edited_g\n",
"\n",
" g1 = tf.convert_to_tensor(tmp_lst, dtype=tf.float32)\n",
"\n",
" g1_ = tf.math.add(g1, g1_)\n",
"\n",
" scores_collection.append(np.mean(scores_collection_temp,axis=0))\n",
" scores_collection_genes.append(scores_collection_temp)\n",
" g2 = gtape.gradient(sequences,noise,output_gradients=g1_)\n",
"\n",
"\n",
" a1 = g2 + noise_small\n",
" change = [(a1,noise)]\n",
"\n",
" optimizer.apply_gradients(change)\n",
"\n",
" iters_.append(iter_)\n",
" iter_ += 1\n",
"\n",
" sequences_opt = wgan(noise)\n",
"\n",
" gen_seqs_opt = sequences_opt.numpy().astype('float')\n",
"\n",
" seqs_gen_opt = recover_seq(gen_seqs_opt, rev_rna_vocab)\n",
"\n",
" seqs_opt = retriever.replace_utr_in_multiple_sequences(gene_names, seqs_gen_opt, target_length=10500, cache_dir=\"./.cache\", output_prefix=\"modified_sequence\")\n",
"\n",
" seqs_opt = one_hot(seqs_opt)\n",
"\n",
" pred_opt = model(seqs_opt)\n",
"\n",
" pred_opt = tf.reshape(pred_opt,(SEQ_BATCH,-1))\n",
"\n",
"\n",
" average_optimized_prediction = tf.reduce_mean(pred_opt,axis=0).numpy().astype('float')\n",
"\n",
"\n",
"best_seqs, best_scores = select_best(scores_collection_genes, seqs_collection, per_gene=True)\n",
"\n",
"\n",
"# %%\n",
"print(np.shape(best_scores))\n",
"\n",
"# %%\n",
"\n",
"with open('./outputs/mul_init_exps.txt', 'w') as f:\n",
" for item in average_initial_prediction:\n",
" f.write(f'{item}\\n')\n",
"\n",
"with open('./outputs/mul_best_exps.txt', 'w') as f:\n",
" for item in best_scores:\n",
" f.write(f'{item}\\n')\n",
"\n",
"with open('./outputs/mul_opt_exps.txt', 'w') as f:\n",
" for item in average_optimized_prediction:\n",
" f.write(f'{item}\\n')\n",
"\n",
"with open('./outputs/mul_best_seqs.txt', 'w') as f:\n",
" for item in best_seqs:\n",
" f.write(f'{item}\\n')\n",
"\n",
"with open('./outputs/mul_init_seqs.txt', 'w') as f:\n",
" for item in seqs_gen_init:\n",
" f.write(f'{item}\\n')\n",
"\n",
"# Compute average Log TPM per gene\n",
"init_log_tpm_target = tf.reduce_mean(pred_init, axis=1).numpy().astype('float')\n",
"opt_log_tpm_target = tf.reduce_mean(pred_opt, axis=1).numpy().astype('float')\n",
"opt_log_tpm_target = best_scores\n",
"\n",
"# Compute overall average Log TPM across target genes\n",
"avg_init_log_tpm = np.average(init_log_tpm_target)\n",
"avg_opt_log_tpm = np.average(opt_log_tpm_target)\n",
"\n",
"# Convert Log TPM to TPM for percentage improvement\n",
"# Assuming Log TPM is base-10 (common for TPM), TPM = 10^LogTPM\n",
"avg_init_tpm = np.power(10, avg_init_log_tpm)\n",
"avg_opt_tpm = np.power(10, avg_opt_log_tpm)\n",
"\n",
"# Compute improvement\n",
"log_tpm_diff = avg_opt_log_tpm - avg_init_log_tpm\n",
"tpm_improvement = avg_opt_tpm - avg_init_tpm\n",
"# Percentage improvement based on TPM: ((opt - init) / init) * 100\n",
"if avg_init_tpm != 0: # Avoid division by zero\n",
" tpm_percent_change = (tpm_improvement / avg_init_tpm) * 100\n",
"else:\n",
" tpm_percent_change = float('inf') if tpm_improvement > 0 else 0.0\n",
"\n",
"# Handle negative and positive percentages\n",
"percent_str = f\"{tpm_percent_change:.2f}%\"\n",
"if tpm_percent_change < 0:\n",
" percent_str = f\"{tpm_percent_change:.2f}% (decrease)\"\n",
"elif tpm_percent_change > 0:\n",
" percent_str = f\"+{tpm_percent_change:.2f}% (increase)\"\n",
"\n",
"# Print evaluation results\n",
"print(\"\\nEvaluation of Optimization on Original Genes (Log TPM):\")\n",
"print(\"\\nExpression Levels (Log TPM):\")\n",
"print(f\" Average Initial Log TPM: {avg_init_log_tpm:.4f} (TPM: {avg_init_tpm:.4f})\")\n",
"print(f\" Average Optimized Log TPM: {avg_opt_log_tpm:.4f} (TPM: {avg_opt_tpm:.4f})\")\n",
"print(f\" Log TPM Difference: {log_tpm_diff:.4f}\")\n",
"print(f\" TPM Improvement: {tpm_improvement:.4f} ({percent_str})\")\n",
"\n",
"\n",
"print(\"Genes:\")\n",
"print(gene_names)\n",
"print(f\"Average Initial Expression: {np.average(average_initial_prediction)}\")\n",
"print(f\"Best Expression: {np.average(best_scores)}\")\n",
"\n",
"\n",
"target_refs = []\n",
"for gene in target_genes:\n",
" output_json = f\"{gene}_info.json\"\n",
" cache_path = os.path.join('./.cache/', output_json)\n",
" \n",
" if not os.path.exists(cache_path):\n",
" # Retrieve gene information\n",
" gene_info = retriever.get_gene_info(gene, output_json=output_json)\n",
" if \"error\" in gene_info:\n",
" print(f\"Error retrieving info for {gene}: {gene_info['error']}\")\n",
" target_refs.append(None) # Handle errors gracefully\n",
" else:\n",
" target_refs.append(gene_info[\"promoter_sequence\"])\n",
" else:\n",
" with open(cache_path, \"r\") as f:\n",
" gene_info = json.load(f)\n",
" target_refs.append(gene_info[\"promoter_sequence\"])\n",
"\n",
"\n",
"valid_indices = [i for i, ref in enumerate(target_refs) if ref is not None]\n",
"target_genes = [target_genes[i] for i in valid_indices]\n",
"target_refs = [target_refs[i] for i in valid_indices]\n",
"\n",
"if not target_genes:\n",
" print(\"No valid target genes retrieved. Exiting evaluation.\")\n",
"else:\n",
"\n",
" seqs_gen_init = seqs_gen_init \n",
" seqs_gen_opt = best_seqs \n",
"\n",
"\n",
" seqs_init_target = retriever.replace_utr_in_multiple_sequences(\n",
" target_genes, seqs_gen_init, target_length=10500, cache_dir=\"./.cache\", output_prefix=\"target_modified_sequence\"\n",
" )\n",
" seqs_opt_target = retriever.replace_utr_in_multiple_sequences(\n",
" target_genes, seqs_gen_opt, target_length=10500, cache_dir=\"./.cache\", output_prefix=\"target_modified_sequence\"\n",
" )\n",
"\n",
"\n",
" seqs_init_target = one_hot(seqs_init_target)\n",
" seqs_opt_target = one_hot(seqs_opt_target)\n",
"\n",
"\n",
" pred_init_target = model(seqs_init_target)\n",
" pred_opt_target = model(seqs_opt_target)\n",
"\n",
" pred_init_target = tf.reshape(pred_init_target, (len(target_genes), -1))\n",
" pred_opt_target = tf.reshape(pred_opt_target, (len(target_genes), -1))\n",
"\n",
" # Compute average Log TPM per gene\n",
" init_log_tpm_target = tf.reduce_mean(pred_init_target, axis=1).numpy().astype('float')\n",
" opt_log_tpm_target = tf.reduce_mean(pred_opt_target, axis=1).numpy().astype('float')\n",
"\n",
" # Compute overall average Log TPM across target genes\n",
" avg_init_log_tpm = np.average(init_log_tpm_target)\n",
" avg_opt_log_tpm = np.average(opt_log_tpm_target)\n",
"\n",
" # Convert Log TPM to TPM for percentage improvement\n",
" avg_init_tpm = np.power(10, avg_init_log_tpm)\n",
" avg_opt_tpm = np.power(10, avg_opt_log_tpm)\n",
"\n",
" # Compute improvement\n",
" log_tpm_diff = avg_opt_log_tpm - avg_init_log_tpm\n",
" tpm_improvement = avg_opt_tpm - avg_init_tpm\n",
" # Percentage improvement based on TPM: ((opt - init) / init) * 100\n",
" if avg_init_tpm != 0: # Avoid division by zero\n",
" tpm_percent_change = (tpm_improvement / avg_init_tpm) * 100\n",
" else:\n",
" tpm_percent_change = float('inf') if tpm_improvement > 0 else 0.0\n",
"\n",
" # Handle negative and positive percentages\n",
" percent_str = f\"{tpm_percent_change:.2f}%\"\n",
" if tpm_percent_change < 0:\n",
" percent_str = f\"{tpm_percent_change:.2f}% (decrease)\"\n",
" elif tpm_percent_change > 0:\n",
" percent_str = f\"+{tpm_percent_change:.2f}% (increase)\"\n",
"\n",
" # Print evaluation results\n",
" print(\"\\nEvaluation of Optimization on Target Genes (Log TPM):\")\n",
" print(f\"Original Genes: {gene_names}\")\n",
" print(f\"Target Genes: {target_genes}\")\n",
" print(\"\\nExpression Levels (Log TPM):\")\n",
" print(f\" Average Initial Log TPM: {avg_init_log_tpm:.4f} (TPM: {avg_init_tpm:.4f})\")\n",
" print(f\" Average Optimized Log TPM: {avg_opt_log_tpm:.4f} (TPM: {avg_opt_tpm:.4f})\")\n",
" print(f\" Log TPM Difference: {log_tpm_diff:.4f}\")\n",
" print(f\" TPM Improvement: {tpm_improvement:.4f} ({percent_str})\")\n",
"\n",
" # Save evaluation results to a file\n",
" with open('./outputs/target_genes_evaluation.txt', 'w') as f:\n",
" f.write(\"Evaluation of Optimization on Target Genes (Log TPM)\\n\")\n",
" f.write(f\"Original Genes: {gene_names}\\n\")\n",
" f.write(f\"Target Genes: {target_genes}\\n\\n\")\n",
" f.write(\"Expression Levels (Log TPM):\\n\")\n",
" f.write(f\" Average Initial Log TPM: {avg_init_log_tpm:.4f} (TPM: {avg_init_tpm:.4f})\\n\")\n",
" f.write(f\" Average Optimized Log TPM: {avg_opt_log_tpm:.4f} (TPM: {avg_opt_tpm:.4f})\\n\")\n",
" f.write(f\" Log TPM Difference: {log_tpm_diff:.4f}\\n\")\n",
" f.write(f\" TPM Improvement: {tpm_improvement:.4f} ({percent_str})\\n\")\n",
"\n",
" # Optional: Per-gene breakdown\n",
" print(\"\\nPer-Gene Expression Levels (Log TPM):\")\n",
" for gene, init_log, opt_log in zip(target_genes, init_log_tpm_target, opt_log_tpm_target):\n",
" init_tpm = np.power(10, init_log)\n",
" opt_tpm = np.power(10, opt_log)\n",
" tpm_diff = opt_tpm - init_tpm\n",
" if init_tpm != 0:\n",
" gene_percent = (tpm_diff / init_tpm) * 100\n",
" else:\n",
" gene_percent = float('inf') if tpm_diff > 0 else 0.0\n",
" gene_percent_str = f\"{gene_percent:.2f}%\"\n",
" if gene_percent < 0:\n",
" gene_percent_str = f\"{gene_percent:.2f}% (decrease)\"\n",
" elif gene_percent > 0:\n",
" gene_percent_str = f\"+{gene_percent:.2f}% (increase)\"\n",
" print(f\" {gene}: Initial Log TPM = {init_log:.4f} (TPM: {init_tpm:.4f}), \"\n",
" f\"Optimized Log TPM = {opt_log:.4f} (TPM: {opt_tpm:.4f}), \"\n",
" f\"TPM Improvement = {tpm_diff:.4f} ({gene_percent_str})\")\n",
"\n",
"\n",
"\n"
]
}
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