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Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression.

TitleStatistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression.
Publication TypeJournal Article
Year of Publication2019
AuthorsSubramaniyam S, DeJesus MA, Zaveri A, Smith CM, Baker RE, Ehrt S, Schnappinger D, Sassetti CM, Ioerger TR
JournalBMC Bioinformatics
Volume20
Issue1
Pagination603
Date Published2019 Nov 21
ISSN1471-2105
KeywordsAnimals, Anti-Bacterial Agents, Binomial Distribution, Databases, Genetic, DNA Transposable Elements, Genes, Essential, High-Throughput Nucleotide Sequencing, Likelihood Functions, Linear Models, Mice, Inbred C57BL, Models, Statistical, Mycobacterium tuberculosis
Abstract

BACKGROUND: Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions.

RESULTS: In this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB distribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics.

CONCLUSIONS: Our results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model.

DOI10.1186/s12859-019-3156-z
Alternate JournalBMC Bioinformatics
PubMed ID31752678
PubMed Central IDPMC6873424
Grant ListP01 AI132130 / AI / NIAID NIH HHS / United States
AI107774 / / National Institute of Allergy and Infectious Diseases /

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