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Changelog

Source: NEWS.md

tAge 1.1.0

Corrects three bugs that produced wrong predictions in 1.0.0 / 1.0.1. Analyses run with those versions should be repeated with 1.1.0.

Bug fixes

  • Species rescaling is now applied only to chronological-age clocks. Previously every prediction was multiplied by the species maximum-lifespan factor, which turned mortality output (log10 hazard ratio) and normalized-age output into meaningless numbers. The clock type is taken from the model file name, matching the TACO reference application.

  • Reference centring is never skipped. control_subtraction() used to return the data unchanged when no reference group was given. All distributed clocks are relative (_scaleddiff / _yugenediff) models trained on reference-centred expression, so predictions made without centring were invalid. column_name and control_label now default to NULL, which centres on all samples (overall per-gene median) — the TACO default.

  • Genes absent from the input are padded with NA instead of 0 when aligning to the clock gene list. The trained model’s imputer then fills them with the training-set median for that gene, which is the correct neutral value.

  • predict_tAge() coerces the reticulate result to a data.frame, fixing a failure on reticulate/pandas versions that return a bare vector for a single-column result.

  • tage_boxplot() is exported.

New features

  • Clock registry: list_clocks() browses the pre-trained models (filter by type, outcome, species, tissue and scaling) and download_clocks() fetches them from Zenodo record 18763485, returning the table with a path column. The registry ships with the package as inst/extdata/clocks_metadata.csv.

Documentation

  • Two vignettes replace the former Jupyter notebooks: vignette("tage-bulk") for bulk RNA-seq and vignette("tage-singlecell") for the pseudobulk single-cell workflow.

  • pkgdown site at https://gladyshev-lab.github.io/tAge/.

  • README rewritten: units of each clock outcome, the role of reference groups in relative clocks, supported species, and licensing.

  • scale_eset() documents that scaling is per sample (column-wise, each sample scaled across genes). Per-gene standardisation happens separately inside the trained model, using training-set statistics.

Testing

  • testthat suite covering the clock registry, prediction and preprocessing.

tAge 1.0.1

  • README and LICENSE updated for the MGB Open Access License 1.0.
  • Model availability notice and placeholder paths corrected in the tutorials.

tAge 1.0.0