TwelveCellDeconvoluteBloodEPIC

Index

1. Instantiate model class

2. Define clock metadata

3. Download clock dependencies

4. Load features

5. Load weights into base model

6. Load reference values

7. Load preprocess and postprocess objects

8. Check all clock parameters

9. Basic test

10. Save torch model

11. Clear directory

Let’s first import some packages:

import os
import inspect
import shutil
import json
import torch
import pandas as pd
import pyaging as pya

Instantiate model class

def print_entire_class(cls):
    source = inspect.getsource(cls)
    print(source)

print_entire_class(pya.models.TwelveCellDeconvoluteBloodEPIC)

class TwelveCellDeconvoluteBloodEPIC(DeconvolutionSingleCell):
    def __init__(self):
        super().__init__()

model = pya.models.TwelveCellDeconvoluteBloodEPIC()

Define clock metadata

model.metadata["clock_name"] = 'twelvecelldeconvolutebloodepiccd4mem'
model.metadata["data_type"] = 'methylation'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2024
model.metadata["approved_by_author"] = '⌛'
model.metadata["citation"] = "Ying, Kejun, et al. \"A unified framework for systematic curation and evaluation of aging biomarkers.\" Nature Aging (2025): 1-17."
model.metadata["doi"] = "https://doi.org/10.1038/s43587-025-00987-y"
model.metadata["research_only"] = None
model.metadata["notes"] = "Estimated proportion of CD4mem from 12-cell EPIC reference."

Download clock dependencies

Download reference file

coeff_url = "https://raw.githubusercontent.com/bio-learn/biolearn/master/biolearn/data/twelve_cell_deconv.csv"
os.system(f"curl -L {coeff_url} -o twelve_cell_deconv.csv")

0

Load features

From CSV file

import numpy as np
import pandas as pd

ref = pd.read_csv('twelve_cell_deconv.csv', index_col=0)
model.features = ref.index.astype(str).tolist()

ref_matrix = torch.tensor(ref.values, dtype=torch.float64)
pseudo_inv = torch.linalg.pinv(ref_matrix)

model.pseudo_inv = pseudo_inv
model.cell_index = 3
model.reference_values = torch.nanmean(ref_matrix, dim=1)

Load weights into base model

From CSV file

# No linear base model; deconvolution logic in model forward

Linear model

model.base_model = None

Load reference values

# reference_values already set above

Load preprocess and postprocess objects

model.preprocess_name = "fill_with_reference_means"
model.preprocess_dependencies = None

model.postprocess_name = None
model.postprocess_dependencies = None

Check all clock parameters

pya.utils.print_model_details(model)

%==================================== Model Details ====================================%
Model Attributes:

training: True
metadata: {'approved_by_author': '⌛',
 'citation': 'Ying, Kejun, et al. "A unified framework for systematic curation '
             'and evaluation of aging biomarkers." Nature Aging (2025): 1-17.',
 'clock_name': 'twelvecelldeconvolutebloodepiccd4mem',
 'data_type': 'methylation',
 'doi': 'https://doi.org/10.1038/s43587-025-00987-y',
 'notes': 'Estimated proportion of CD4mem from 12-cell EPIC reference.',
 'research_only': None,
 'species': 'Homo sapiens',
 'version': None,
 'year': 2024}
reference_values: [0.9106685833333333, 0.8335768333333333, 0.8720161666666666, 0.8286470833333333, 0.92628525, 0.9155413333333334, 0.8792788333333332, 0.9127326666666665, 0.9067565833333332, 0.84800775, 0.10073516666666667, 0.14440299999999998, 0.049399916666666675, 0.08766050000000002, 0.07896441666666666, 0.12878258333333334, 0.16374524999999998, 0.13815541666666667, 0.17540216666666666, 0.1698345, 0.9202164166666665, 0.8607305833333334, 0.9136148333333334, 0.9245049999999999, 0.8901303333333334, 0.9271245, 0.8883705833333333, 0.8855881666666668, 0.9244469166666666, 0.92171125]... [Tensor of shape torch.Size([240])]
preprocess_name: 'fill_with_reference_means'
preprocess_dependencies: None
postprocess_name: None
postprocess_dependencies: None
features: ['cg11778706', 'cg13745692', 'cg15163862', 'cg15667493', 'cg09590647', 'cg24160371', 'cg04200580', 'cg02177087', 'cg02690807', 'cg08486432', 'cg08674813', 'cg17565444', 'cg20208538', 'cg08747761', 'cg16576285', 'cg08269714', 'cg10931172', 'cg18421366', 'cg06805218', 'cg21268578', 'cg23635663', 'cg10712263', 'cg03984633', 'cg27576286', 'cg00789615', 'cg20591136', 'cg08012294', 'cg19811231', 'cg13335048', 'cg27109907']... [Total elements: 240]
base_model_features: None
base_model: None
pseudo_inv: [0.0062597217235298664, 0.01539477951995796, 0.003836934604777173, 0.005564101437478809, 0.002277140363479023, 0.006073633184626901, 0.008477183290511526, -0.0001975656527457585, 0.005601139656499912, 0.007094362071883866, -0.006240708820602573, -0.009260375574434002, -0.0010103501546960773, -2.0931822748843616e-05, -0.003443105142629081, -0.006973754074381415, -0.014081560765767536, -0.01165024465832652, -0.017254358157385866, -0.018101141218108914, 0.016011891819700665, 0.007716146910953028, 0.01145146135203884, 0.007735843885753704, 0.014959580407076114, 0.011932867918461359, 0.014137094238940516, 0.016570818459337337, 0.006383014308174419, 0.007057464604785132]... [Tensor of shape torch.Size([12, 240])]
cell_index: 3

%==================================== Model Details ====================================%
Model Structure:


%==================================== Model Details ====================================%
Model Parameters and Weights:


%==================================== Model Details ====================================%

Basic Test

torch.manual_seed(42)
input = torch.randn(10, len(model.features), dtype=float)
model.eval()
model.to(float)
pred = model(input)
pred

tensor([[0.0000],
        [0.0000],
        [0.1939],
        [0.0000],
        [0.4345],
        [0.0000],
        [0.0000],
        [0.0000],
        [0.0000],
        [0.0000]], dtype=torch.float64)

Save torch model

torch.save(model, f"../weights/{model.metadata['clock_name']}.pt")

Clear directory

# 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: twelve_cell_deconv.csv