OutPyR: Bayesian inference for RNA-Seq outlier detection
<p dir="ltr">High-throughput RNA sequencing technologies (RNA-Seq) have recently started being used as a tool for helping diagnose rare genetic disorders, as they can indicate abnormal gene expression counts — a telltale sign of genetic pathology. Existing solutions either require a...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2020
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| الموضوعات: | |
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| _version_ | 1864513559428333568 |
|---|---|
| author | Edin Salkovic (16891479) |
| author2 | Mostafa M. Abbas (17058093) Samir Brahim Belhaouari (9427347) Khaoula Errafii (10914446) Halima Bensmail (10400) |
| author2_role | author author author author |
| author_facet | Edin Salkovic (16891479) Mostafa M. Abbas (17058093) Samir Brahim Belhaouari (9427347) Khaoula Errafii (10914446) Halima Bensmail (10400) |
| author_role | author |
| dc.creator.none.fl_str_mv | Edin Salkovic (16891479) Mostafa M. Abbas (17058093) Samir Brahim Belhaouari (9427347) Khaoula Errafii (10914446) Halima Bensmail (10400) |
| dc.date.none.fl_str_mv | 2020-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jocs.2020.101245 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/OutPyR_Bayesian_inference_for_RNA-Seq_outlier_detection/24210723 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Genetics Information and computing sciences Artificial intelligence Mathematical sciences Statistics RNA-Seq Outlier detection Bayesian modeling |
| dc.title.none.fl_str_mv | OutPyR: Bayesian inference for RNA-Seq outlier detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">High-throughput RNA sequencing technologies (RNA-Seq) have recently started being used as a tool for helping diagnose rare genetic disorders, as they can indicate abnormal gene expression counts — a telltale sign of genetic pathology. Existing solutions either require a large number of samples or do not provide proper statistical significance testing.</p><p dir="ltr">We present a Bayesian model (OutPyR) for identifying abnormal RNA-Seq gene expression counts in datasets, particularly those with a small number of samples. The model incorporates recently introduced data-augmentation techniques to efficiently and accurately infer parameters of the underlying negative binomial process, while also assessing the uncertainty of the inference, and giving the possibility to generate simulated data. The model's software implementation is object oriented and thus easily extensible, provides parameter-trace exploration, fault-tolerance and recovery during the parameter estimation process. We also develop a P-value based outlier score that naturally stems from our model. We apply the model to real and simulated datasets, for different organisms and tissues, and present comparisons with existing models.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Computational Science<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jocs.2020.101245" target="_blank">https://dx.doi.org/10.1016/j.jocs.2020.101245</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_65687978fe59a1fb0bc7b259726cf3cc |
| identifier_str_mv | 10.1016/j.jocs.2020.101245 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24210723 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | OutPyR: Bayesian inference for RNA-Seq outlier detectionEdin Salkovic (16891479)Mostafa M. Abbas (17058093)Samir Brahim Belhaouari (9427347)Khaoula Errafii (10914446)Halima Bensmail (10400)Biological sciencesBioinformatics and computational biologyGeneticsInformation and computing sciencesArtificial intelligenceMathematical sciencesStatisticsRNA-SeqOutlier detectionBayesian modeling<p dir="ltr">High-throughput RNA sequencing technologies (RNA-Seq) have recently started being used as a tool for helping diagnose rare genetic disorders, as they can indicate abnormal gene expression counts — a telltale sign of genetic pathology. Existing solutions either require a large number of samples or do not provide proper statistical significance testing.</p><p dir="ltr">We present a Bayesian model (OutPyR) for identifying abnormal RNA-Seq gene expression counts in datasets, particularly those with a small number of samples. The model incorporates recently introduced data-augmentation techniques to efficiently and accurately infer parameters of the underlying negative binomial process, while also assessing the uncertainty of the inference, and giving the possibility to generate simulated data. The model's software implementation is object oriented and thus easily extensible, provides parameter-trace exploration, fault-tolerance and recovery during the parameter estimation process. We also develop a P-value based outlier score that naturally stems from our model. We apply the model to real and simulated datasets, for different organisms and tissues, and present comparisons with existing models.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Computational Science<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jocs.2020.101245" target="_blank">https://dx.doi.org/10.1016/j.jocs.2020.101245</a></p>2020-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jocs.2020.101245https://figshare.com/articles/journal_contribution/OutPyR_Bayesian_inference_for_RNA-Seq_outlier_detection/24210723CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/242107232020-11-01T00:00:00Z |
| spellingShingle | OutPyR: Bayesian inference for RNA-Seq outlier detection Edin Salkovic (16891479) Biological sciences Bioinformatics and computational biology Genetics Information and computing sciences Artificial intelligence Mathematical sciences Statistics RNA-Seq Outlier detection Bayesian modeling |
| status_str | publishedVersion |
| title | OutPyR: Bayesian inference for RNA-Seq outlier detection |
| title_full | OutPyR: Bayesian inference for RNA-Seq outlier detection |
| title_fullStr | OutPyR: Bayesian inference for RNA-Seq outlier detection |
| title_full_unstemmed | OutPyR: Bayesian inference for RNA-Seq outlier detection |
| title_short | OutPyR: Bayesian inference for RNA-Seq outlier detection |
| title_sort | OutPyR: Bayesian inference for RNA-Seq outlier detection |
| topic | Biological sciences Bioinformatics and computational biology Genetics Information and computing sciences Artificial intelligence Mathematical sciences Statistics RNA-Seq Outlier detection Bayesian modeling |