Finite-size effects in transcript sequencing count distribution: its power-law correction necessarily precedes...

Area of Science:

• Transcriptomics
• Bioinformatics
• Statistical Modeling

Background:

• Transcript abundance distributions often deviate from theoretical power-laws like Zipf's law.
• Real-world finite observations introduce size effects, causing deviations and curvature in log-log plots.
• These deviations can lead to heteroskedasticity, compromising statistical rigor in transcriptomic analyses.

Purpose of the Study:

• To investigate the impact of finite-size effects on transcript abundance data.
• To evaluate the effectiveness of a power-law correction in addressing these deviations.
• To quantify the improvements in data analysis and statistical rigor.

Main Methods:

• Analysis of two next-generation sequencing (NGS) datasets: a dilution miRNA study and a public spike-in miRNA dataset.
• Application of a straightforward power-law correction to the transcript count distribution.
• Evaluation of changes in signal-to-noise ratio, detection sensitivity, and concordance across normalization methods.

Main Results:

• Finite-size effects cause deviations from Zipf's law and reproducibility issues in sequencing data.
• Power-law correction significantly reduces data heteroskedasticity, increasing signal-to-noise by 50% and sensitivity by 30%.
• Correction improves concordance among normalization methods by 22% and enhances detection with higher sequencing depths.

Conclusions:

• Finite-size effects in transcriptomics can be mitigated by a simple power-law correction.
• This correction improves reproducibility and statistical rigor in transcriptomic studies.
• The method has direct implications for biological interpretation and the reliability of scientific findings.