Real Age Prediction from the Transcriptome with RAPToR

Abstract

Transcriptomic data is often affected by uncontrolled variation among samples that can obscure and confound the effects of interest. This is frequently due to unintended differences in developmental stages between samples. The transcriptome itself can be used to estimate developmental progression, but existing methods require many samples and do not estimate a real developmental time. Here we present RAPToR, a simple and precise computational method that estimates the real age of a sample from its transcriptome, exploiting existing time-series data as reference. RAPToR works with whole animal, dissected tissue and single-cell data, for the most common animal models, humans and even for nonmodel organisms lacking reference data. We show RAPToR estimated age improves differential expression analysis by recovering the signal of interest when confounded with age. RAPToR will be especially useful in large scale single organism profiling because it eliminates the need for accurate staging or synchronization before profiling.