This repository provides a reproducible workflow for identifying robust senescence markers from SenCat transcriptomic and proteomic data and for validating marker-based scoring in external IMR90 fibroblast datasets.

The workflow standardizes transcriptomic and proteomic measurements, applies consistent preprocessing, and uses a cross-cell-type machine learning strategy to identify markers that remain informative across biological contexts. A refined marker set is then used to derive stable marker weights and generate sample-level senescence scores in both reference and external validation datasets, with all primary outputs written to the analysis and plotting directories.

• SenCat transcriptomic data: primary RNA-level input used for marker discovery.
• SenCat proteomic data: primary protein-level input used for marker discovery.
• External validation data: IMR90 fibroblast datasets used to evaluate score transferability.
• Workflow configuration: centralized settings for inputs, analysis profiles, and output locations.

• Transcriptomics markers: analysis/transcriptomics.transcriptomics_loose5000f_tuned_common_features.results.csv.
• Proteomics markers: analysis/proteomics.proteomics_loose5000f_tuned_common_features.results.csv.

You can use our senescence markers to score your data for senescence.

The scoring is performed on h5ad file containing normalized transcriptomics or proteomics counts. Expected h5ad structure:

• adata.X: sample-by-feature expression matrix
• adata.var_names: feature identifiers matching the marker IDs in the marker CSV index

If your data are not normalized, you can use normalize_counts.py script:

python workflow/scripts/data/normalize_counts.py \
    --input-h5ad INPUT_H5AD \
    --design DESIGN_FACTORS \
    --output-h5ad NORMALIZED_H5AD \
    --log logs/my_data.normalize.log \
    --log-level INFO

• INPUT_H5AD specifies a path to your input h5ad file
• DESIGN_FACTORS specifies design factors for DESeq2, in the format x + z or ~x+z.
• NORMALIZED_H5AD specifies a path where your normalized data will be saved

python workflow/scripts/cls/marker_classifier.py \
    --markers PATH_TO_ML_MARKERS \
    --input-h5ad NORMALIZED_H5AD \
    --output-results-csv OUTPUT_CSV 

• PATH_TO_ML_MARKERS specifies path to ML markers. Use analysis/transcriptomics.transcriptomics_loose5000f_tuned_common_features.results.csv for transcriptomics and analysis/proteomics.proteomics_loose5000f_tuned_common_features.results.csv for proteomics
• NORMALIZED_H5AD specifies path to your normalized h5ad data
• OUTPUT_CSV specifies path to output csv file with per-sample score values (higher values indicate stronger similarity to the senescence-associated signature).

Notes:

• marker_classifier.py applies log1p internally.
• Marker matching is based on adata.var_names; non-overlapping markers are skipped automatically.