EpiCMITHyper#
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.EpiCMITHyper)
class EpiCMITHyper(epiTOC1):
pass
[3]:
model = pya.models.EpiCMITHyper()
Define clock metadata#
[4]:
model.metadata["clock_name"] = 'epicmithyper'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2020
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Duran-Ferrer, Marti, et al. \"The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome.\" Nature Cancer 1.11 (2020): 1066-1081."
model.metadata["doi"] = "https://doi.org/10.1038/s43018-020-00131-2"
model.metadata["research_only"] = None
model.metadata["notes"] = "Hypermethylation-based component of the epiCMIT mitotic clock, approximating the cumulative proliferative (mitotic) history of normal and neoplastic B cells from methylation gains at Polycomb/H3K27me3-marked regions, with independent prognostic value in B-cell tumors."
model.metadata["tissue"] = 'B cells (normal B-cell subpopulations and neoplastic B-cell tumors spanning the B-cell lineage)'
model.metadata["predicts"] = 'cumulative mitotic (cell-division) / proliferative history (epiCMIT hypermethylation component)'
model.metadata["unit"] = 'score (arbitrary)'
model.metadata["model_type"] = 'Mitotic model'
model.metadata["platform"] = 'Illumina 450K/EPIC'
model.metadata["population"] = 'pan-age; normal and neoplastic B cells across the human B-cell developmental lineage (pediatric to adult tumor cohorts)'
model.metadata["journal"] = 'Nature Cancer'
model.metadata["last_author"] = 'José I. Martı́n-Subero'
model.metadata["n_features"] = 184
model.metadata["citations"] = 104
model.metadata["citations_date"] = '2026-07-05'
Download clock dependencies#
[5]:
supplementary_url = "https://static-content.springer.com/esm/art%3A10.1038%2Fs43018-020-00131-2/MediaObjects/43018_2020_131_MOESM3_ESM.xlsx"
supplementary_file_name = "epicmit.xlsx"
os.system(f"curl -sL -o {supplementary_file_name} {supplementary_url}")
[5]:
0
Load features#
[6]:
df = pd.read_excel('epicmit.xlsx', sheet_name='Table 23')
df = df[df['epiCMIT.class'].astype(str).str.contains('hyper', case=False)]
model.features = df['Name'].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 = "mean"
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': 'Duran-Ferrer, Marti, et al. "The proliferative history shapes '
'the DNA methylome of B-cell tumors and predicts clinical '
'outcome." Nature Cancer 1.11 (2020): 1066-1081.',
'clock_name': 'epicmithyper',
'data_type': 'methylation',
'doi': 'https://doi.org/10.1038/s43018-020-00131-2',
'notes': None,
'research_only': None,
'species': 'Homo sapiens',
'version': None,
'year': 2020}
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: 184]
preprocess_name: 'mean'
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg19715410', 'cg18279094', 'cg11823511', 'cg24453699', 'cg03989260', 'cg14565725', 'cg00466334', 'cg24035245', 'cg05378938', 'cg09671258', 'cg09826050', 'cg07279070', 'cg18093372', 'cg18173235', 'cg07813142', 'cg24772753', 'cg17737681', 'cg17078253', 'cg05467160', 'cg04415176', 'cg01405040', 'cg02694427', 'cg03964958', 'cg09578028', 'cg04739647', 'cg05167251', 'cg22674699', 'cg22541735', 'cg19384289', 'cg14473102']... [Total elements: 184]
base_model_features: None
%==================================== Model Details ====================================%
Model Structure:
base_model: LinearModel(
(linear): Linear(in_features=184, 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([[ 0.0423],
[ 0.0471],
[ 0.0455],
[ 0.0341],
[ 0.0093],
[ 0.0674],
[ 0.0421],
[ 0.0545],
[ 0.0112],
[-0.1275]], 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: epicmit.xlsx