Pasta#

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:

[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.Pasta)

class Pasta(pyagingModel):
    def __init__(self):
        super().__init__()

    @staticmethod
    def _rank_average(values):
        """
        Assign average ranks (1-based) per vector, handling ties.
        """
        sorted_vals, sorted_idx = torch.sort(values)
        ranks = torch.empty_like(sorted_vals, dtype=values.dtype)

        n = values.numel()
        start = 0
        while start < n:
            end = start + 1
            while end < n and sorted_vals[end] == sorted_vals[start]:
                end += 1
            avg_rank = (start + end - 1) / 2.0 + 1.0
            ranks[sorted_idx[start:end]] = avg_rank
            start = end

        return ranks

    def preprocess(self, x):
        """
        Fill missing values with the global median then rank-normalize per sample.
        """
        median = torch.nanmedian(x)
        if torch.isnan(median):
            median = torch.tensor(0.0, device=x.device, dtype=x.dtype)
        x = torch.where(torch.isnan(x), median, x)

        ranked = torch.empty_like(x, dtype=x.dtype)
        for i in range(x.size(0)):
            ranked[i] = self._rank_average(x[i])

        return ranked

    def postprocess(self, x):
        """
        Apply linear scaling and shifting constants from the original Pasta definition.
        """
        scale = self.postprocess_dependencies[0]
        offset_factor = self.postprocess_dependencies[1]
        return x * scale + offset_factor * scale


[3]:

model = pya.models.Pasta()

Define clock metadata#

[4]:

model.metadata["clock_name"] = 'pasta'
model.metadata["data_type"] = 'transcriptomics'
model.metadata["species"] = 'Homo sapiens'
model.metadata["year"] = 2025
model.metadata["approved_by_author"] = '✅'
model.metadata["citation"] = "Salignon, Jerome, et al. \"Pasta, an age-shift transcriptomic clock, maps the chemical and genetic determinants of aging and rejuvenation.\" bioRxiv (2025): 2025-06."
model.metadata["doi"] = "https://doi.org/10.1101/2025.06.04.657785"
model.metadata["research_only"] = None
model.metadata["notes"] = "Cross-platform transcriptomic aging clock that estimates relative cellular age from rank-transformed gene expression using a ridge-regularized age-shift classifier trained on same-tissue sample pairs decades apart. It generalizes across bulk and single-cell RNA-seq, microarray, and L1000 data and multiple species, and was used to screen chemical and genetic perturbations that accelerate or reverse cellular aging."
model.metadata["tissue"] = 'Multi-tissue human (healthy donors; mostly GTEx, plus GEO and Expression Atlas datasets); also mouse datasets used for validation'
model.metadata["predicts"] = 'relative (biological) age-shift; also discriminates senescent vs stem-like/quiescent cell states'
model.metadata["unit"] = 'years'
model.metadata["model_type"] = 'Elastic net'
model.metadata["platform"] = 'RNA-seq'
model.metadata["population"] = 'Human (adult, healthy donors, pan-tissue) and mouse; generalizes across platforms and species'
model.metadata["journal"] = 'bioRxiv (Cold Spring Harbor Laboratory)'
model.metadata["last_author"] = 'Christian G. Riedel'
model.metadata["n_features"] = 8113
model.metadata["citations"] = 1
model.metadata["citations_date"] = '2026-07-05'

Download clock dependencies#

Download coefficient file#

[5]:

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

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  322k  100  322k    0     0  1174k      0 --:--:-- --:--:-- --:--:-- 1176k

[5]:

0

Load features#

From CSV file#

[6]:

coeffs = pd.read_csv('Pasta.csv')
coeffs['feature'] = coeffs['GeneID']
coeffs['coefficient'] = coeffs['CoefficientTraining']

model.features = coeffs['feature'].tolist()

Load weights into base model#

From CSV file#

[7]:

weights = torch.tensor(coeffs['coefficient'].tolist()).unsqueeze(0)
intercept = torch.tensor([0.0])

Linear model#

[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 = [float("nan")] * len(model.features)

Load preprocess and postprocess objects#

[10]:

model.preprocess_name = "median_fill_and_rank_normalization"
model.preprocess_dependencies = None

[11]:

model.postprocess_name = "scale_and_shift"
model.postprocess_dependencies = [-4.76348378687217, -0.0502893445253186]

Check all clock parameters#

[12]:

pya.utils.print_model_details(model)


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

training: True
metadata: {'approved_by_author': '✅',
 'citation': 'Salignon, Jerome, et al. "Pasta, an age-shift transcriptomic '
             'clock, maps the chemical and genetic determinants of aging and '
             'rejuvenation." bioRxiv (2025): 2025-06.',
 'clock_name': 'pasta',
 'data_type': 'transcriptomics',
 'doi': 'https://doi.org/10.1101/2025.06.04.657785',
 'notes': 'Rank-normalized transcriptomic clock.',
 'research_only': None,
 'species': 'Homo sapiens',
 'version': None,
 'year': 2025}
reference_values: [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]... [Total elements: 8113]
preprocess_name: 'median_fill_and_rank_normalization'
preprocess_dependencies: None
postprocess_name: 'scale_and_shift'
postprocess_dependencies: [-4.76348378687217, -0.0502893445253186]
features: ['ENSG00000196839', 'ENSG00000170558', 'ENSG00000133997', 'ENSG00000168060', 'ENSG00000101473', 'ENSG00000136754', 'ENSG00000113552', 'ENSG00000177485', 'ENSG00000136560', 'ENSG00000094631', 'ENSG00000108840', 'ENSG00000170248', 'ENSG00000153094', 'ENSG00000159921', 'ENSG00000165879', 'ENSG00000135451', 'ENSG00000142892', 'ENSG00000179776', 'ENSG00000167670', 'ENSG00000129484', 'ENSG00000041880', 'ENSG00000113361', 'ENSG00000141198', 'ENSG00000100284', 'ENSG00000013619', 'ENSG00000010017', 'ENSG00000105993', 'ENSG00000113810', 'ENSG00000182963', 'ENSG00000126261']... [Total elements: 8113]
base_model_features: None

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

base_model: LinearModel(
  (linear): Linear(in_features=8113, out_features=1, bias=True)
)

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

base_model.linear.weight: [-2.4399256290053017e-05, -1.774273368937429e-05, 1.554851587570738e-05, 1.1031659596483223e-05, 1.6993128156173043e-05, 3.9308954001171514e-05, -0.00012627331307157874, 2.8949250463483622e-06, -6.271281017689034e-05, 2.9893646569689736e-05, 3.6174697015667334e-05, 6.864466558909044e-05, -2.3814825908630155e-05, 3.11008479911834e-05, 1.0880126865231432e-05, 9.605172635929193e-06, 1.1990639904979616e-05, 9.29949510464212e-06, 6.331568147288635e-05, -3.362866482348181e-05, -0.00022874546993989497, -2.7509766368893906e-05, 6.674586074950639e-06, 1.986255301744677e-05, -3.5506527638062835e-05, 2.922421663242858e-05, -4.5067787141306326e-05, 5.991863872623071e-05, 3.728850060724653e-05, 4.235586311551742e-05]... [Tensor of shape torch.Size([1, 8113])]
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([[-12.0950],
        [-36.7366],
        [  2.5806],
        [ 29.9285],
        [-21.5258],
        [ -3.6489],
        [-39.3746],
        [ 36.6380],
        [ 27.4081],
        [-33.9218]], dtype=torch.float64, grad_fn=<AddBackward0>)

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: EPIC_salas_18_reference.csv
Deleted file: Pasta.csv