tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

Area of Science:

• Chronobiology
• Computational Biology
• Genomics

Background:

• Circadian clocks regulate essential biological processes.
• Disrupted circadian rhythms are linked to various diseases.
• Accurate determination of circadian time is crucial for circadian medicine and research.

Purpose of the Study:

• To present tauFisher, a novel computational pipeline for predicting circadian time.
• To validate tauFisher's accuracy across diverse transcriptomic samples and experimental conditions.

Main Methods:

• tauFisher analyzes correlations between rhythmic genes in a single transcriptomic sample.
• The pipeline was tested on both bulk and single-cell RNA sequencing data.
• Applied to mouse dermal skin single-cell RNAseq data to assess cell-type specific rhythms.

Main Results:

• tauFisher accurately predicts circadian time from transcriptomic samples.
• Demonstrated robustness across multiple tissue types and experimental settings.
• Revealed potential circadian phase heterogeneity in dermal immune cells compared to fibroblasts.

Conclusions:

• tauFisher is a versatile and accurate tool for circadian time prediction.
• The pipeline is applicable to various data types, including single-cell RNA sequencing.
• Facilitates advancements in circadian medicine and biological research.