rain

rain detects arbitrary periodic patterns in biological time-series using robust nonparametric statistical methods to identify circadian, cell cycle, and other rhythms despite experimental noise, outliers, and missing values.

Key Features:

• Nonparametric Approach: Employs nonparametric methods that do not assume predefined waveform shapes, enabling detection of arbitrary rhythmic patterns.
• Robust Statistical Methods: Uses robust statistics tailored to complex experimental and biological noise that challenge Fourier-based approaches.
• Handling Data Imperfections: Manages outliers and missing values within time-series datasets.
• Optimized Sample Size Range: Optimized for time-series datasets comprising approximately 10-100 measurements.
• Detection of Prespecified Periods: Tests for rhythms at prespecified periods.
• Increased Detection Power: Expands detection of nonsymmetric and non-sinusoidal rhythmic profiles, increasing sensitivity compared to waveform-assuming methods.
• Controlled False Discovery Rate: Incorporates procedures to control false discovery rates for statistical significance.
• Validation and Reliability: Validated against independent datasets, including circadian transcriptome and proteome studies of mouse liver, with identification of additional rhythmic transcripts and proteins relative to traditional methods.

Scientific Applications:

• Genome-, Proteome-, and Metabolome-wide Studies: Applied to identify oscillating features in genome-wide, proteome-wide, and metabolome-wide datasets.
• Microarray and Mass Spectrometry Data: Suitable for analysis of microarray time-series, proteome mass spectrometry, and metabolomics data.
• Circadian and Cell Cycle Rhythms: Used to detect circadian and cell cycle rhythmicity in molecular abundance data.
• Nonsymmetric Waveform Detection: Detects nonsymmetric biological waveforms to support functional inference about rhythmic processes.

Methodology:

Applies robust nonparametric statistical tests that do not assume specific waveform shapes, handles outliers and missing values, tests for prespecified periods, and applies false discovery rate control.