Abstract
Rhythmic transcripts play pivotal roles in driving the daily oscillations of various biological processes. Genetic or environmental disruptions can lead to alterations in the rhythmicity of transcripts, ultimately impacting downstream circadian outputs, including metabolic processes and even behavior. To statistically compare the differences in transcript rhythms between 2 or more conditions, several algorithms have been developed to analyze circadian transcriptomic data, each with distinct features. In this study, we compared the performance of 7 algorithms that were specifically designed to detect differential rhythmicity (DODR, LimoRhyde, CircaCompare, compareRhythms, diffCircadian, dryR, and RepeatedCircadian). We found that even when applying the same statistical threshold, these algorithms yielded varying numbers of differentially rhythmic transcripts, most likely because each algorithm defines rhythmic and differentially rhythmic transcripts differently. Nevertheless, the output for the differential phase and amplitude were identical between dryR and compareRhyhms, and diffCircadian and CircaCompare, while the output from LimoRhyde2 was highly correlated with that from diffCircadian and CircaCompare. Because each algorithm has unique requirements for input data and reports different information as an output, it is crucial to ensure the compatibility of input data with the chosen algorithm and assess whether the algorithm's output fits the user's needs when selecting an algorithm for analysis.