Transcriptomic age from bulk RNA-seq with tAge

Overview

tAge applies the transcriptomic clocks from Tyshkovskiy et al. (2026), Nature to bulk RNA-seq data, predicting chronological age (in age units) and expected mortality (as a log10 hazard ratio).

The workflow: build an ExpressionSet, preprocess it, and predict.

1. Build an ExpressionSet

We use the mouse example data bundled with the package (genes × samples raw counts, plus sample metadata).

exprs_data <- load_example_expression_data()
metadata   <- load_example_metadata()

eset <- make_ExpressionSet(exprs_data, metadata, verbose = FALSE)
eset
#> ExpressionSet (storageMode: lockedEnvironment)
#> assayData: 57010 features, 24 samples 
#>   element names: exprs 
#> protocolData: none
#> phenoData
#>   sampleNames: Klo93K Klo94K ... RNA_122M (24 total)
#>   varLabels: Mouse.ID Genotype Sex Tissue
#>   varMetadata: labelDescription
#> featureData: none
#> experimentData: use 'experimentData(object)'
#> Annotation:

2. Preprocess

tAge_preprocessing() filters low-expressed genes, maps them to the mouse Entrez gene space, RLE-normalises, log-transforms, scales (per sample), applies the YuGene transform, aligns to the clock gene list, and centres the data.

tAge_eset <- tAge_preprocessing(
  eset,
  species = "mouse",
  gene_mapping_type = "Ensembl",
  verbose = FALSE
)
#> calcNormFactors has been renamed to normLibSizes
names(tAge_eset)
#> [1] "RLE_normalized"  "log_transformed" "scaled"          "scaled_diff"    
#> [5] "yugene"          "yugene_diff"

Prediction uses the scaled_diff and yugene_diff representations.

3. Browse and download clocks

The published clocks live on Zenodo; list them from R (reads the bundled registry):

head(list_clocks(type = "EN", outcome = "Mortality"))
#>                                               filename type   outcome
#> 1        EN_Mortality_Mouse_Multitissue_scaleddiff.pkl   EN Mortality
#> 2        EN_Mortality_Mouse_Multitissue_yugenediff.pkl   EN Mortality
#> 3 EN_Mortality_Multispecies_Multitissue_scaleddiff.pkl   EN Mortality
#> 4 EN_Mortality_Multispecies_Multitissue_yugenediff.pkl   EN Mortality
#> 5            EN_Mortality_Rodents_Liver_scaleddiff.pkl   EN Mortality
#> 6            EN_Mortality_Rodents_Liver_yugenediff.pkl   EN Mortality
#>        species       tissue scaling lifespan_scaled
#> 1        Mouse Multi-Tissue  Scaled           FALSE
#> 2        Mouse Multi-Tissue  YuGene           FALSE
#> 3 Multispecies Multi-Tissue  Scaled           FALSE
#> 4 Multispecies Multi-Tissue  YuGene           FALSE
#> 5      Rodents        Liver  Scaled           FALSE
#> 6      Rodents        Liver  YuGene           FALSE

download_clocks() fetches a chosen set and returns a path column:

clocks <- list_clocks(type = "EN", outcome = "Mortality",
                      species = "Multispecies", tissue = "Multi-Tissue")
clocks <- download_clocks(clocks, dest_dir = "clocks")

model_paths <- list(
  scaled_diff = clocks$path[clocks$scaling == "Scaled"],
  yugene_diff = clocks$path[clocks$scaling == "YuGene"]
)

4. Predict

Sys.setenv(RETICULATE_PYTHON = Sys.getenv("RETICULATE_PYTHON"))  # use your env

results <- predict_tAge(
  tAge_eset,
  model_paths = list(scaled_diff = .model_path),
  species = "mouse",
  mode = "EN"
)
head(results[, c("Genotype", "Tissue", "scaled_diff_EN_tAge")])
#>         Genotype Tissue scaled_diff_EN_tAge
#> Klo93K        WT Kidney          -0.5939931
#> Klo94K        WT Kidney          -0.6682321
#> Klo95K        WT Kidney          -0.5306280
#> Klo96K        WT Kidney          -0.1638941
#> Klo99K        WT Kidney          -0.5889994
#> Klo100K       WT Kidney          -0.6160492

These mortality values are log10(hazard ratio) — small numbers centred near zero, not ages. See below.

Interpreting the output

Outcome	Units	Notes
Chronological	age (years / months)	normalised age rescaled by species max lifespan
Mortality	`log10(hazard ratio)`	not an age; higher = higher expected mortality
Normalized age	fraction of max lifespan	native scale

tAge looks up each clock in the registry and rescales only chronological clocks to age units.

Reference groups (centring)

All distributed clocks are relative: they operate on expression centred against a reference group.

Default (no reference group): centre on all samples (overall per-gene median) — a general cohort readout.
Matched controls: pass control_group_column and control_group_label to tAge_preprocessing() to centre on matched controls (recommended for treatment-vs-control designs), e.g. control_group_column = "Genotype", control_group_label = "WT".

Session info

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] tAge_1.1.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] cli_3.6.6           knitr_1.51          rlang_1.3.0        
#>  [4] xfun_0.60           otel_0.2.0          png_0.1-9          
#>  [7] generics_0.1.4      textshaping_1.0.5   jsonlite_2.0.0     
#> [10] statmod_1.5.2       htmltools_0.5.9     ragg_1.5.2         
#> [13] sass_0.4.10         locfit_1.5-9.12     Biobase_2.72.0     
#> [16] rmarkdown_2.31      grid_4.6.1          evaluate_1.0.5     
#> [19] jquerylib_0.1.4     fastmap_1.2.0       yaml_2.3.12        
#> [22] lifecycle_1.0.5     compiler_4.6.1      fs_2.1.0           
#> [25] limma_3.68.4        Rcpp_1.1.2          edgeR_4.10.1       
#> [28] lattice_0.22-9      systemfonts_1.3.2   digest_0.6.39      
#> [31] R6_2.6.1            reticulate_1.46.0   bslib_0.11.0       
#> [34] Matrix_1.7-5        tools_4.6.1         BiocGenerics_0.58.1
#> [37] pkgdown_2.2.1       cachem_1.1.0        desc_1.4.3

Gladyshev Lab