StemTOCvitro#
Index#
Let’s first import some packages:
[1]:
import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya
Instantiate model class#
[2]:
def print_entire_class(cls):
source = inspect.getsource(cls)
print(source)
print_entire_class(pya.models.StemTOCvitro)
class StemTOCvitro(stemTOC):
pass
[3]:
model = pya.models.StemTOCvitro()
Define clock metadata#
[4]:
model.metadata["clock_name"] = "stemtocvitro"
model.metadata["data_type"] = "DNA methylation" # Paper: The study constructs clocks from DNA methylation measurements.
model.metadata["species"] = "Homo sapiens" # Paper: The analyzed samples and clock are human.
model.metadata["year"] = 2024
model.metadata["approved_by_author"] = "⌛"
model.metadata["citation"] = "Zhu, Tianlei, et al. \"An improved epigenetic counter to track mitotic age in cells.\" Nature Communications 15 (2024): 4211."
model.metadata["doi"] = "https://doi.org/10.1038/s41467-024-48649-8"
model.metadata["notes"] = "In-vitro precursor of stemTOC based on the 95th percentile across 629 population-doubling-associated CpGs."
model.metadata["research_only"] = None
model.metadata["tissue"] = ["multi-tissue", "cultured human cells"] # Paper: Feature selection used fetal/neonatal reference tissues and cultured normal cell types; stemTOC also used blood cohorts.
model.metadata["predicts"] = ["mitotic age"] # Paper: The paper describes the returned score as relative mitotic age.
model.metadata["training_target"] = ["population doublings"] # Paper: Candidate CpGs were selected for methylation gain with population doublings and, where applicable, age in blood.
model.metadata["unit"] = ["beta value"] # Paper: The score is the 95% upper quantile of CpG DNAm beta values.
model.metadata["model_type"] = "95th-percentile methylation aggregation" # Paper: The score is computed as the 95% upper quantile across selected CpGs.
model.metadata["platform"] = ["Illumina 450K", "Illumina EPIC"] # Paper: Derivation datasets included 450K fetal samples and EPIC cultured-cell samples.
model.metadata["population"] = "prenatal and newborn" # Paper: The derivation cohorts are fetal/neonatal tissues, normal cell cultures, and for stemTOC adult blood cohorts.
model.metadata["journal"] = "Nature Communications"
model.metadata["last_author"] = "Andrew E. Teschendorff"
model.metadata["n_features"] = 629
model.metadata["citations"] = 24
model.metadata["citations_date"] = "2026-07-05"
Download clock dependencies#
[5]:
supplementary_url = "https://raw.githubusercontent.com/Duzhaozhen/OmniAge/c10fbe8cb92957520fbff1d55ae1def0691252e5/OmniAgePy/src/omniage/data/StemTOCvitro.csv"
supplementary_file_name = "coefficients.csv"
os.system(f"curl -sL -o {supplementary_file_name} {supplementary_url}")
[5]:
0
Load features#
[6]:
df = pd.read_csv('coefficients.csv')
if str(df.columns[0]).startswith('Unnamed'):
df = df.iloc[:, 1:]
model.features = df['probe'].tolist()
Load weights into base model#
[7]:
weights = torch.tensor([1.0]).unsqueeze(0)
intercept = torch.tensor([0.0])
[8]:
base_model = pya.models.LinearModel(input_dim=len(model.features))
base_model.linear.weight.data = weights.float()
base_model.linear.bias.data = intercept.float()
model.base_model = base_model
Load reference values#
[9]:
model.reference_values = [-1]*len(model.features)
Load preprocess and postprocess objects#
[10]:
model.preprocess_name = "0.95 quantile"
model.preprocess_dependencies = None
[11]:
model.postprocess_name = None
model.postprocess_dependencies = None
Check all clock parameters#
[12]:
pya.utils.print_model_details(model)
%==================================== Model Details ====================================%
Model Attributes:
training: True
metadata: {'approved_by_author': '⌛',
'citation': 'Zhu, Tianyu, et al. "A pan-tissue DNA methylation atlas enables '
'in silico decomposition of human tissue methylomes at cell-type '
'resolution." Nature Communications 15 (2024).',
'clock_name': 'stemtocvitro',
'data_type': 'methylation',
'doi': 'https://doi.org/10.1038/s41467-024-48649-8',
'notes': None,
'research_only': None,
'species': 'Homo sapiens',
'version': None,
'year': 2024}
reference_values: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]... [Total elements: 629]
preprocess_name: '0.95 quantile'
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg20327258', 'cg23062112', 'cg07365816', 'cg08659394', 'cg14575559', 'cg13294856', 'cg18560328', 'cg21229268', 'cg20078466', 'cg23091824', 'cg03865257', 'cg16443455', 'cg11250773', 'cg10687823', 'cg12892303', 'cg12781700', 'cg13583934', 'cg12065366', 'cg25203031', 'cg17404915', 'cg22299454', 'cg10698404', 'cg09097345', 'cg05404701', 'cg24010885', 'cg17712694', 'cg20707222', 'cg08901752', 'cg11297107', 'cg17876581']... [Total elements: 629]
base_model_features: None
%==================================== Model Details ====================================%
Model Structure:
base_model: LinearModel(
(linear): Linear(in_features=629, out_features=1, bias=True)
)
%==================================== Model Details ====================================%
Model Parameters and Weights:
base_model.linear.weight: tensor([[1.]])
base_model.linear.bias: tensor([0.])
%==================================== Model Details ====================================%
Basic test#
[13]:
torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float)
model.eval()
model.to(float)
pred = model(input)
pred
[13]:
tensor([[1.8129],
[1.7694],
[1.5825],
[1.5507],
[1.6496],
[1.5994],
[1.6658],
[1.6827],
[1.6054],
[1.7836]], dtype=torch.float64, grad_fn=<AddmmBackward0>)
Save torch model#
[14]:
torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")
Clear directory#
[15]:
# Function to remove a folder and all its contents
def remove_folder(path):
try:
shutil.rmtree(path)
print(f"Deleted folder: {path}")
except Exception as e:
print(f"Error deleting folder {path}: {e}")
# Get a list of all files and folders in the current directory
all_items = os.listdir('.')
# Loop through the items
for item in all_items:
# Check if it's a file and does not end with .ipynb
if os.path.isfile(item) and not item.endswith('.ipynb'):
os.remove(item)
print(f"Deleted file: {item}")
# Check if it's a folder
elif os.path.isdir(item):
remove_folder(item)
Deleted file: coefficients.csv