Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression
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 inser...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-11-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-019-3156-z |
| _version_ | 1818553647588442112 |
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| author | Siddharth Subramaniyam Michael A. DeJesus Anisha Zaveri Clare M. Smith Richard E. Baker Sabine Ehrt Dirk Schnappinger Christopher M. Sassetti Thomas R. Ioerger |
| author_facet | Siddharth Subramaniyam Michael A. DeJesus Anisha Zaveri Clare M. Smith Richard E. Baker Sabine Ehrt Dirk Schnappinger Christopher M. Sassetti Thomas R. Ioerger |
| author_sort | Siddharth Subramaniyam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T09:28:33Z |
| format | Article |
| id | doaj.art-c80d20f8c434430692d7e045fc78bfc1 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-12T09:28:33Z |
| publishDate | 2019-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c80d20f8c434430692d7e045fc78bfc12022-12-22T00:28:57ZengBMCBMC Bioinformatics1471-21052019-11-0120111510.1186/s12859-019-3156-zStatistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regressionSiddharth Subramaniyam0Michael A. DeJesus1Anisha Zaveri2Clare M. Smith3Richard E. Baker4Sabine Ehrt5Dirk Schnappinger6Christopher M. Sassetti7Thomas R. Ioerger8Department of Computer Science & Engineering, Texas A&M UniveristyRockefeller UniversityDepartment of Microbiology & Immunology, Weill Cornell Medical CollegeDepartment of Microbiology & Physiological Systems, University of Massachusetts Medical SchoolDepartment of Microbiology & Physiological Systems, University of Massachusetts Medical SchoolDepartment of Microbiology & Immunology, Weill Cornell Medical CollegeDepartment of Microbiology & Immunology, Weill Cornell Medical CollegeDepartment of Microbiology & Physiological Systems, University of Massachusetts Medical SchoolDepartment of Computer Science & Engineering, Texas A&M UniveristyAbstract 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.http://link.springer.com/article/10.1186/s12859-019-3156-zTnSeqTransposon insertion libraryEssentialityZero-inflated negative binomial distributionMycobacterium tuberculosis |
| spellingShingle | Siddharth Subramaniyam Michael A. DeJesus Anisha Zaveri Clare M. Smith Richard E. Baker Sabine Ehrt Dirk Schnappinger Christopher M. Sassetti Thomas R. Ioerger Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression BMC Bioinformatics TnSeq Transposon insertion library Essentiality Zero-inflated negative binomial distribution Mycobacterium tuberculosis |
| title | Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression |
| title_full | Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression |
| title_fullStr | Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression |
| title_full_unstemmed | Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression |
| title_short | Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression |
| title_sort | statistical analysis of variability in tnseq data across conditions using zero inflated negative binomial regression |
| topic | TnSeq Transposon insertion library Essentiality Zero-inflated negative binomial distribution Mycobacterium tuberculosis |
| url | http://link.springer.com/article/10.1186/s12859-019-3156-z |
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