Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications

Abstract

Circadian rhythmicity in transcriptomic profiles has been shown in many physiological processes, and the disruption of circadian patterns has been found to associate with several diseases. In this paper, we developed a series of likelihood-based methods to detect (i) circadian rhythmicity (denoted as LR_rhythmicity) and (ii) differential circadian patterns comparing two experimental conditions (denoted as LR_diff). In terms of circadian rhythmicity detection, we demonstrated that our proposed LR_rhythmicity could better control the type I error rate compared to existing methods under a wide variety of simulation settings. In terms of differential circadian patterns, we developed methods in detecting differential amplitude, differential phase, differential basal level and differential fit, which also successfully controlled the type I error rate. In addition, we demonstrated that the proposed LR_diff could achieve higher statistical power in detecting differential fit, compared to existing methods. The superior performance of LR_rhythmicity and LR_diff was demonstrated in four real data applications, including a brain aging data (gene expression microarray data of human postmortem brain), a time-restricted feeding data (RNA sequencing data of human skeletal muscles) and a scRNAseq data (single cell RNA sequencing data of mouse suprachiasmatic nucleus). An R package for our methods is publicly available on GitHub https://github.com/diffCircadian/diffCircadian.

Systematic modeling-driven experiments identify distinct molecular clockworks underlying hierarchically organized pacemaker neurons

“…We used the log-likelihood ratio (LR) test to detect the rhythmicity of the short time-series data ( Fig. 1 C and E and SI Appendix , Table S1 ) because the method yields a low false-positive error ( 76 ). We used the significance criteria ( P < 0.01) suggested by ref.…”

“…We used the significance criteria ( P < 0.01) suggested by ref. 76 . This calculation was done using the R package diffCircadian provided by Ding et al.…”

Venn diagram analysis overestimates the extent of circadian rhythm reprogramming

“…However, nonlinear regression is not robust to violations of assumptions, does not account for particular properties of transcriptomic data and the analysis does not handle multiple testing needed in high-throughput datasets. diffCircadian [28] present a likelihood-ratio test-based DiffR analysis for generic pre-normalized data, which our implementations (except DODR) already use for transcriptomic datasets. CosinorPy [29] is the only package that allows for simple DiffR analysis in Python.…”

Defining the age-dependent and tissue-specific circadian transcriptome in male mice

“…All RNAseq samples were sequenced to a depth of at least 40 million reads aligned to the mouse genome. To identify the circadian transcriptome, we deployed the cosinor model implemented in the diffCircadian software (Ding et al, 2021). Specifically, we defined circadian genes as those with 24h cosine oscillations in transcript abundance based upon a raw p-value < 0.01.…”

“…Users may specify (1) single or batch entry of genes of interest, (2) 1-3 ages of interest, and (3) 1-6 tissue types of interest. Data can be conveniently exported as *.csv files with statistical outputs including peak time, amplitude, basal expression (i.e., MESOR), phase, R 2 , and p-values (Ding et al, 2021).…”