Predict transcriptomic age with one pre-trained model

Applies one pre-trained Elastic Net (EN) or Bayesian Ridge (BR) clock to a preprocessed ExpressionSet through the Python model (via reticulate).

Usage

predict_tAge_one(
  eset,
  model_path,
  species = NULL,
  mode,
  return_std = identical(mode, "BR"),
  age_units = c("auto", "months", "years"),
  normalized_age = c("fraction", "percent")
)

Arguments

eset

An ExpressionSet object containing processed expression data (one element of the list returned by tAge_preprocessing).

model_path

Character string specifying the path to the pre-trained model file.

species

Species of the samples: "mouse", "rat", "human" or "monkey" (see tage_species). Its only effect is the rescaling of chronological-age clocks to age units by the species maximum lifespan; mortality and normalized-age clocks ignore it. It is not the species group the model was trained on ("Mouse" / "Rodents" / "Multispecies" in list_clocks). Default NULL takes the species recorded by tAge_preprocessing.

mode

Character string specifying the model type. Must be either "EN" for Elastic Net or "BR" for Bayesian Ridge.

return_std

Logical. Whether to also return the per-sample predictive standard deviation, which only Bayesian Ridge models provide. Defaults to TRUE for mode = "BR". The standard deviations are what tage_compare_groups weights samples by, so keep them if you intend to run statistics on BR predictions.

age_units

Units of chronological-age predictions: "auto" (default; months for rodents, years for primates), "months" or "years".

normalized_age

Scale of normalized-age clocks: "fraction" of the expected maximum lifespan (default) or "percent" (x100, as in the paper).

Value

A data frame containing the predicted transcriptomic age results with sample information and predicted ages, plus a BR_tAge_std column when return_std is TRUE. The attribute "tage_units" names the unit of the prediction column.