Real age prediction from the transcriptome with RAPToR.

Area of Science:

• Genomics
• Bioinformatics
• Developmental Biology

Background:

• Transcriptomic data analysis is often confounded by uncontrolled biological variation, particularly differences in developmental stages among samples.
• Existing methods for estimating developmental progression from transcriptomes are sample-intensive and do not provide precise real-age estimations.
• Accurate age determination is crucial for interpreting gene expression differences and understanding biological processes.

Purpose of the Study:

• To introduce RAPToR (real-age prediction from transcriptome staging on reference), a novel computational method for precise age estimation from transcriptomic data.
• To demonstrate RAPToR's utility in correcting for age-related confounding factors in transcriptomic analyses.
• To provide a tool that facilitates large-scale transcriptomic studies by reducing the need for precise sample synchronization.

Main Methods:

• RAPToR utilizes existing time-series transcriptomic data as a reference to predict the real age of a new sample.
• The method is applicable to various data types, including whole animal, dissected tissue, and single-cell RNA sequencing data.
• RAPToR is designed to work across different species, including model organisms, humans, and non-model organisms.

Main Results:

• RAPToR accurately estimates the real age of a sample directly from its transcriptome.
• The method successfully removes age as a confounding variable in differential gene expression analysis, enabling the recovery of biologically relevant signals.
• Validation across diverse datasets and species confirms RAPToR's robustness and broad applicability.

Conclusions:

• RAPToR offers a powerful solution for precisely determining sample age from transcriptomic data, overcoming limitations of existing methods.
• This computational tool significantly enhances the reliability of transcriptomic studies by controlling for developmental stage variation.
• RAPToR is particularly valuable for large-scale projects and studies involving non-model organisms, streamlining experimental design and data interpretation.