📊 Randles 2021

Proteomic profiling of age-dependent changes in the kidney glomerular and tubulointerstitial compartments

📄 Original Publication

Authors: Randles MJ, Lausecker F, ...

Journal: Journal of the American Society of Nephrology (2021)

PubMed ID: 33446562

DOI: 10.1681/ASN.2020101442

📈 Dataset Statistics

458 Total Measurements

229 Unique Proteins

0 ECM Proteins

Homo sapiens Species

🧬 Tissue Information

Tissues:

Kidney_Glomerular
Kidney_Tubulointerstitial

Compartments:

Glomerular
Tubulointerstitial

⚙️ Data Processing

Pipeline Steps

Raw Data Extraction: Original proteomic data extracted from supplementary materials
Normalization: Converted to long format, filtered for ECM proteins (Match_Confidence > 0)
Wide Format Transformation: Pivoted to sample × protein abundance matrix
Merge to Unified Database: Added to merged_ecm_aging_zscore.csv
Z-score Calculation: Calculated per-compartment z-scores for cross-study comparison

Key Processing Parameters

Study ID: Randles_2021
Missing Values: Preserved (NaN = protein not detected)
Zero Values: Included (0.0 = detected but absent)
Normalization: Within-study z-scores per compartment

💾 Data Access

This dataset is included in the unified ECM-Atlas database. You can access it through:

Dashboard: Select "Randles_2021" from the Individual Dataset Analysis tab
CSV File: 08_merged_ecm_dataset/merged_ecm_aging_zscore.csv
Filter: df[df['Study_ID'] == 'Randles_2021']

Quick Start (Python)

import pandas as pd

# Load unified database
df = pd.read_csv('merged_ecm_aging_zscore.csv')

# Filter for this study
study_df = df[df['Study_ID'] == 'Randles_2021']

# View ECM proteins only
ecm_df = study_df[study_df['ECM_Class'].notna()]

print(f"Total proteins: {len(study_df['Protein_ID'].unique())}")
print(f"ECM proteins: {len(ecm_df['Protein_ID'].unique())}")

🔧 Metadata

{}

Generated: 2025-10-20 | ECM-Atlas Version: 2.0

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