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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression.

Siddharth Subramaniyam1, Michael A DeJesus2, Anisha Zaveri3

  • 1Department of Computer Science & Engineering, Texas A&M Univeristy, College Station, TX, USA.

BMC Bioinformatics
|November 23, 2019
PubMed
Summary

This study introduces a new statistical method, Zero-Inflated Negative Binomial (ZINB) regression, to identify genes with significant insertion variability across multiple conditions in transposon mutant sequencing (TnSeq) experiments. ZINB analysis improves the detection of conditionally essential genes compared to existing methods.

Keywords:
EssentialityMycobacterium tuberculosisTnSeqTransposon insertion libraryZero-inflated negative binomial distribution

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Area of Science:

  • Genomics
  • Systems Biology
  • Statistical Genetics

Background:

  • Transposon mutant sequencing (TnSeq) is vital for identifying essential genes under various conditions.
  • Current methods struggle with analyzing large-scale TnSeq data across multiple conditions.
  • A need exists for robust statistical approaches to detect gene variability in multi-condition TnSeq experiments.

Purpose of the Study:

  • To develop and validate a novel statistical method for identifying genes with significant insertion count variability across multiple experimental conditions.
  • To compare the performance of the new method against existing analytical approaches for TnSeq data.

Main Methods:

  • Development of a Zero-Inflated Negative Binomial (ZINB) regression model for analyzing TnSeq data.
  • Application of likelihood ratio tests to compare ZINB with ANOVA and Negative Binomial models.
  • Identification of conditionally essential genes in *M. tuberculosis* H37Rv during mouse infection and antibiotic exposure.

Main Results:

  • ZINB regression provides a superior fit for TnSeq data compared to ANOVA or standard Negative Binomial models.
  • The ZINB method effectively identifies genes essential for *M. tuberculosis* H37Rv infection in mice.
  • ZINB analysis revealed conditionally essential genes in *M. tuberculosis* H37Rv under antibiotic stress.

Conclusions:

  • The ZINB model accurately identifies genes with significant insertion variability across multiple conditions.
  • ZINB outperforms pairwise resampling and ANOVA in detecting conditionally essential genes.
  • The ZINB model uniquely accounts for insertion count magnitudes and local saturation differences, improving gene essentiality analysis.