RNA-sequencing’s conversion of molecules to reads is inconsistent. Experiment-to-experiment variations (systemic bias) create batch effects, while gene-to-gene variations (sequence-dependent bias) invalidate inter-gene comparisons, precluding a universal scale. This confines analysis to relative fold-changes, a metric unreliable across batches. We introduce TranScale: 100 biomimetic standards with SI-traceable concentrations certified by Isotope Dilution Mass Spectrometry. Co-processed within samples, they empirically characterize systemic and sequence-dependent biases, generating a library-specific calibration curve (R² > 0.97) to convert reads into absolute quantities. This approach reveals that consistent fold-changes can mask severe absolute errors, exposing systemic biases missed by conventional QC. Across laboratories, this calibration reduced median inter-lab CV from >85% to 7.9, outperforming the widely-used tool ComBat. By anchoring RNA-seq to the SI, our work establishes the metrological foundation for data interoperability and universal benchmarks, enabling absolute comparisons of SI-traceable quantities between any two genes. RNA-sequencing’s conversion of molecules to reads confines analysis to relative fold-changes, a metric unreliable across batches. Here, the authors introduce TranScale, a spike-in method with 100 biomimetic standards that converts reads into absolute quantities to resolve systemic biases.
Zhang et al. (Thu,) studied this question.