Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica
ilustraciones, graficas
- Autores:
-
Bello Reyes, Nicolás
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81767
- Palabra clave:
- 570 - Biología::576 - Genética y evolución
ARN MENSAJERO
Databases, nucleic acid
Rna, messenger
BASES DE DATOS DE ACIDO NUCLEICO
RNA-Seq
Differential expression
Generalized Linear Models
Multiple testing
Expresión diferencial
Modelos lineales generalizados
Pruebas múltiples
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica |
| dc.title.translated.eng.fl_str_mv |
Analysis of the performance of DESeq2 for the detection of differentially expressed genes for genome sequencing data |
| title |
Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica |
| spellingShingle |
Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica 570 - Biología::576 - Genética y evolución ARN MENSAJERO Databases, nucleic acid Rna, messenger BASES DE DATOS DE ACIDO NUCLEICO RNA-Seq Differential expression Generalized Linear Models Multiple testing Expresión diferencial Modelos lineales generalizados Pruebas múltiples |
| title_short |
Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica |
| title_full |
Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica |
| title_fullStr |
Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica |
| title_full_unstemmed |
Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica |
| title_sort |
Análisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómica |
| dc.creator.fl_str_mv |
Bello Reyes, Nicolás |
| dc.contributor.advisor.none.fl_str_mv |
López Kleine, Liliana |
| dc.contributor.author.none.fl_str_mv |
Bello Reyes, Nicolás |
| dc.subject.ddc.spa.fl_str_mv |
570 - Biología::576 - Genética y evolución |
| topic |
570 - Biología::576 - Genética y evolución ARN MENSAJERO Databases, nucleic acid Rna, messenger BASES DE DATOS DE ACIDO NUCLEICO RNA-Seq Differential expression Generalized Linear Models Multiple testing Expresión diferencial Modelos lineales generalizados Pruebas múltiples |
| dc.subject.lemb.spa.fl_str_mv |
ARN MENSAJERO Databases, nucleic acid |
| dc.subject.lemb.eng.fl_str_mv |
Rna, messenger BASES DE DATOS DE ACIDO NUCLEICO |
| dc.subject.proposal.eng.fl_str_mv |
RNA-Seq Differential expression Generalized Linear Models Multiple testing |
| dc.subject.proposal.spa.fl_str_mv |
Expresión diferencial Modelos lineales generalizados Pruebas múltiples |
| description |
ilustraciones, graficas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-08-02T14:59:09Z |
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2022-08-02T14:59:09Z |
| dc.date.issued.none.fl_str_mv |
2022 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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Text |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/81767 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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RedCol LaReferencia |
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Al Mahi, Naim ; Begum, Munni: A two-step integrated approach to detect differentially expressed genes in RNA-Seq data. En: Journal of Bioinformatics and Computational Biology 14 (2016), Nr. 06, p. 1650034 Anders, Simon ; Huber, Wolfgang: Differential expression analysis for sequence count data. En: Nature Precedings (2010), p. 1-1 Auer, Paul L. ; Doerge, Rebecca W.: A two-stage Poisson model for testing RNA-seq data. En: Statistical applications in genetics and molecular biology 10 (2011), Nr. 1 Benjamini, Yoav ; Hochberg, Yosef: Controlling the false discovery rate: a practical and powerful approach to multiple testing. En: Journal of the Royal statistical society: series B (Methodological) 57 (1995), Nr. 1, p. 289-300 Boca, Simina M. ; Leek, Jeffrey T.: A direct approach to estimating false discovery rates conditional on covariates. En: bioRxiv (2018) Cheung, Vivian G. ; Nayak, Renuka R. ; Wang, Isabel X. ; Elwyn, Susannah ; Cousins, Sarah M. ; Morley, Michael ; Spielman, Richard S.: Polymorphic cis-and trans-regulation of human gene expression. En: PLoS Biol 8 (2010), Nr. 9, p. e1000480 Dillies, Marie-Agnès ; Rau, Andrea ; Aubert, Julie ; Hennequet-Antier, Christelle ; Jeanmougin, Marine ; Servant, Nicolas ; Keime, Céline ; Marot, Guillemette ; Castel, David ; Estelle, Jordi [u. a.]: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. En: Briefings in bioinformatics 14 (2013), Nr. 6, p. 671-683 Gu, Jinghua ; Wang, Xiao ; Halakivi-Clarke, Leena ; Clarke, Robert ; Xuan, Jianhua: BADGE: A novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data. En: BMC bioinformatics Vol. 15 Springer, 2014, p. 1-11 Ignatiadis, Nikolaos ; Klaus, Bernd ; Zaugg, Judith B. ; Huber, Wolfgang: Datadriven hypothesis weighting increases detection power in genome-scale multiple testing. En: Nature methods 13 (2016), Nr. 7, p. 577-580 Korthauer, Keegan ; Kimes, Patrick K. ; Duvallet, Claire ; Reyes, Alejandro ; Subramanian, Ayshwarya ; Teng, Mingxiang ; Shukla, Chinmay ; Alm, Eric J. ; Hicks, Stephanie C.: A practical guide to methods controlling false discoveries in computational biology. En: Genome biology 20 (2019), Nr. 1, p. 1-21 Korthauer, Keegan D. ; Chu, Li-Fang ; Newton, Michael A. ; Li, Yuan ; Thomson, James ; Stewart, Ron ; Kendziorski, Christina: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. En: Genome biology 17 (2016), Nr. 1, p. 1-15 Lonsdale, John ; Thomas, Jeffrey ; Salvatore, Mike ; Phillips, Rebecca ; Lo, Edmund ; Shad, Saboor ; Hasz, Richard ; Walters, Gary ; Garcia, Fernando ; Young, Nancy [u. a.]: The genotype-tissue expression (GTEx) project. En: Nature genetics 45 (2013), Nr. 6, p. 580-585 Love, Michael ; Huber, W ; Anders, S: Assessment of DESeq2 performance through simulation. En: DESeq2 vignette (2014) Love, Michael I. ; Huber, Wolfgang ; Anders, Simon: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. En: Genome biology 15 (2014), Nr. 12, p. 550 Oshlack, Alicia ; Robinson, Mark D. ; Young, Matthew D.: From RNA-seq reads to differential expression results. En: Genome biology 11 (2010), Nr. 12, p. 220 Pickrell, Joseph K. ; Marioni, John C. ; Pai, Athma A. ; Degner, Jacob F. ; Engelhardt, Barbara E. ; Nkadori, Everlyne ; Veyrieras, Jean-Baptiste ; Stephens, Matthew ; Gilad, Yoav ; Pritchard, Jonathan K.: Understanding mechanisms underlying human gene expression variation with RNA sequencing. En: Nature 464 (2010), Nr. 7289, p. 768-772 Reyes, Alejandro. Count RNA-seq data used for benchmarking FDR control methods. Oktober 2018 Reyes, Alejandro ; Huber, Wolfgang: Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. En: Nucleic acids research 46 (2018), Nr. 2, p. 582-592 Ritchie, Matthew E. ; Phipson, Belinda ; Wu, DI ; Hu, Yifang ; Law, Charity W. ; Shi, Wei ; Smyth, Gordon K.: limma powers differential expression analyses for RNAsequencing and microarray studies. En: Nucleic acids research 43 (2015), Nr. 7, p. e47-e47 Robinson, Mark D. ; McCarthy, Davis J. ; Smyth, Gordon K.: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. En: Bioinformatics 26 (2010), Nr. 1, p. 139-140 Schuster, Stephan C.: Next-generation sequencing transforms today's biology. En: Nature methods 5 (2008), Nr. 1, p. 16-18 Scott, James G. ; Kelly, Ryan C. ; Smith, Matthew A. ; Zhou, Pengcheng ; Kass, Robert E.: False discovery rate regression: an application to neural synchrony detection in primary visual cortex. En: Journal of the American Statistical Association 110 (2015), Nr. 510, p. 459-471 Soneson, Charlotte: compcodeR - an R package for benchmarking differential expression methods for RNA-seq data. En: Bioinformatics 30 (2014), Nr. 17, p. 2517-2518 Soneson, Charlotte ; Delorenzi, Mauro: A comparison of methods for differential expression analysis of RNA-seq data. En: BMC bioinformatics 14 (2013), Nr. 1, p. 1-18 Sun, Shiquan ; Hood, Michelle ; Scott, Laura ; Peng, Qinke ; Mukherjee, Sayan ; Tung, Jenny ; Zhou, Xiang: Differential expression analysis for RNAseq using Poisson mixed models. En: Nucleic acids research 45 (2017), Nr. 11, p. e106-e106 Wang, Tianyu ; Li, Boyang ; Nelson, Craig E. ; Nabavi, Sheida: Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. En: BMC bioinformatics 20 (2019), Nr. 1, p. 1-16 Wang, Zhong ; Gerstein, Mark ; Snyder, Michael: RNA-Seq: a revolutionary tool for transcriptomics. En: Nature reviews genetics 10 (2009), Nr. 1, p. 57-63 |
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v, 39 páginas |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Estadística |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2López Kleine, Liliana9c5b8dea895d5ed7db5c3cb9b48fb925Bello Reyes, Nicolásd60af66e2779576a22f56d0ce63a81112022-08-02T14:59:09Z2022-08-02T14:59:09Z2022https://repositorio.unal.edu.co/handle/unal/81767Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasLas metodologías de secuenciación de ARN han acelerado en gran medida el entendimiento de los procesos biológicos a nivel molecular en diferentes organismos. Aún así, estas metodologías son costosas, lo que lleva a conjuntos de datos de alta dimensionalidad con tamaños de muestra reducidos. Actualmente DESeq2 es una de las metodologías más usadas para el análisis de expresión diferencial, y a pesar de tener una gran fexibilidad en términos de sus hiper-parámetros, en la mayoría de casos se usa con parámetros predeterminados. En este trabajo se analizan dos elementos importantes de esta metodología: se evalúa el desempeño cuando los conteos siguen una distribución Poisson en vez de Binomial negativa y se muestra como la sensibilidad del método aumenta con esta distribución. Adicionalmente se contrasta la corrección por pruebas múltiples de Benjamini y Hochberg con la propuesta de Boca y Leek, y se propone un gráfico para la identificación de la relación funcional con la covariable. (Texto tomado de la fuente)ARN sequencing methods have dramatically accelerated our understanding of molecular biological processes within different organisms. However, these methodologies are costly, leading to datasets of high dimensionality and limited sampling size. At present DESeq2 is among the most used methodologies for this type of analysis, and despite its great flexibility regarding its hyper-parameters, it is mostly used with default values. In this work we analyze two important elements in this methodology: we assess the performance when counts follow a Poisson distribution instead of a negative binomial and we show how the sensibility increases with this distribution. Additionally we contrast the multiple-test correction proposed by Benjamini and Hochberg with that of Boca and Leek, and we also suggest a plot for the correct identification of the functional relationship with the informative covariate.MaestríaMagíster en Ciencias - Estadísticav, 39 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá570 - Biología::576 - Genética y evoluciónARN MENSAJERODatabases, nucleic acidRna, messengerBASES DE DATOS DE ACIDO NUCLEICORNA-SeqDifferential expressionGeneralized Linear ModelsMultiple testingExpresión diferencialModelos lineales generalizadosPruebas múltiplesAnálisis del desempeño de DESeq2 para detección de genes diferencialmente expresados para datos de secuenciación genómicaAnalysis of the performance of DESeq2 for the detection of differentially expressed genes for genome sequencing dataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaAl Mahi, Naim ; Begum, Munni: A two-step integrated approach to detect differentially expressed genes in RNA-Seq data. En: Journal of Bioinformatics and Computational Biology 14 (2016), Nr. 06, p. 1650034Anders, Simon ; Huber, Wolfgang: Differential expression analysis for sequence count data. En: Nature Precedings (2010), p. 1-1Auer, Paul L. ; Doerge, Rebecca W.: A two-stage Poisson model for testing RNA-seq data. En: Statistical applications in genetics and molecular biology 10 (2011), Nr. 1Benjamini, Yoav ; Hochberg, Yosef: Controlling the false discovery rate: a practical and powerful approach to multiple testing. En: Journal of the Royal statistical society: series B (Methodological) 57 (1995), Nr. 1, p. 289-300Boca, Simina M. ; Leek, Jeffrey T.: A direct approach to estimating false discovery rates conditional on covariates. En: bioRxiv (2018)Cheung, Vivian G. ; Nayak, Renuka R. ; Wang, Isabel X. ; Elwyn, Susannah ; Cousins, Sarah M. ; Morley, Michael ; Spielman, Richard S.: Polymorphic cis-and trans-regulation of human gene expression. En: PLoS Biol 8 (2010), Nr. 9, p. e1000480Dillies, Marie-Agnès ; Rau, Andrea ; Aubert, Julie ; Hennequet-Antier, Christelle ; Jeanmougin, Marine ; Servant, Nicolas ; Keime, Céline ; Marot, Guillemette ; Castel, David ; Estelle, Jordi [u. a.]: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. En: Briefings in bioinformatics 14 (2013), Nr. 6, p. 671-683Gu, Jinghua ; Wang, Xiao ; Halakivi-Clarke, Leena ; Clarke, Robert ; Xuan, Jianhua: BADGE: A novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data. En: BMC bioinformatics Vol. 15 Springer, 2014, p. 1-11Ignatiadis, Nikolaos ; Klaus, Bernd ; Zaugg, Judith B. ; Huber, Wolfgang: Datadriven hypothesis weighting increases detection power in genome-scale multiple testing. En: Nature methods 13 (2016), Nr. 7, p. 577-580Korthauer, Keegan ; Kimes, Patrick K. ; Duvallet, Claire ; Reyes, Alejandro ; Subramanian, Ayshwarya ; Teng, Mingxiang ; Shukla, Chinmay ; Alm, Eric J. ; Hicks, Stephanie C.: A practical guide to methods controlling false discoveries in computational biology. En: Genome biology 20 (2019), Nr. 1, p. 1-21Korthauer, Keegan D. ; Chu, Li-Fang ; Newton, Michael A. ; Li, Yuan ; Thomson, James ; Stewart, Ron ; Kendziorski, Christina: A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. En: Genome biology 17 (2016), Nr. 1, p. 1-15Lonsdale, John ; Thomas, Jeffrey ; Salvatore, Mike ; Phillips, Rebecca ; Lo, Edmund ; Shad, Saboor ; Hasz, Richard ; Walters, Gary ; Garcia, Fernando ; Young, Nancy [u. a.]: The genotype-tissue expression (GTEx) project. En: Nature genetics 45 (2013), Nr. 6, p. 580-585Love, Michael ; Huber, W ; Anders, S: Assessment of DESeq2 performance through simulation. En: DESeq2 vignette (2014)Love, Michael I. ; Huber, Wolfgang ; Anders, Simon: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. En: Genome biology 15 (2014), Nr. 12, p. 550Oshlack, Alicia ; Robinson, Mark D. ; Young, Matthew D.: From RNA-seq reads to differential expression results. En: Genome biology 11 (2010), Nr. 12, p. 220Pickrell, Joseph K. ; Marioni, John C. ; Pai, Athma A. ; Degner, Jacob F. ; Engelhardt, Barbara E. ; Nkadori, Everlyne ; Veyrieras, Jean-Baptiste ; Stephens, Matthew ; Gilad, Yoav ; Pritchard, Jonathan K.: Understanding mechanisms underlying human gene expression variation with RNA sequencing. En: Nature 464 (2010), Nr. 7289, p. 768-772Reyes, Alejandro. Count RNA-seq data used for benchmarking FDR control methods. Oktober 2018Reyes, Alejandro ; Huber, Wolfgang: Alternative start and termination sites of transcription drive most transcript isoform differences across human tissues. En: Nucleic acids research 46 (2018), Nr. 2, p. 582-592Ritchie, Matthew E. ; Phipson, Belinda ; Wu, DI ; Hu, Yifang ; Law, Charity W. ; Shi, Wei ; Smyth, Gordon K.: limma powers differential expression analyses for RNAsequencing and microarray studies. En: Nucleic acids research 43 (2015), Nr. 7, p. e47-e47Robinson, Mark D. ; McCarthy, Davis J. ; Smyth, Gordon K.: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. En: Bioinformatics 26 (2010), Nr. 1, p. 139-140Schuster, Stephan C.: Next-generation sequencing transforms today's biology. En: Nature methods 5 (2008), Nr. 1, p. 16-18Scott, James G. ; Kelly, Ryan C. ; Smith, Matthew A. ; Zhou, Pengcheng ; Kass, Robert E.: False discovery rate regression: an application to neural synchrony detection in primary visual cortex. En: Journal of the American Statistical Association 110 (2015), Nr. 510, p. 459-471Soneson, Charlotte: compcodeR - an R package for benchmarking differential expression methods for RNA-seq data. En: Bioinformatics 30 (2014), Nr. 17, p. 2517-2518Soneson, Charlotte ; Delorenzi, Mauro: A comparison of methods for differential expression analysis of RNA-seq data. En: BMC bioinformatics 14 (2013), Nr. 1, p. 1-18Sun, Shiquan ; Hood, Michelle ; Scott, Laura ; Peng, Qinke ; Mukherjee, Sayan ; Tung, Jenny ; Zhou, Xiang: Differential expression analysis for RNAseq using Poisson mixed models. En: Nucleic acids research 45 (2017), Nr. 11, p. e106-e106Wang, Tianyu ; Li, Boyang ; Nelson, Craig E. ; Nabavi, Sheida: Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. En: BMC bioinformatics 20 (2019), Nr. 1, p. 1-16Wang, Zhong ; Gerstein, Mark ; Snyder, Michael: RNA-Seq: a revolutionary tool for transcriptomics. En: Nature reviews genetics 10 (2009), Nr. 1, p. 57-63EstudiantesInvestigadoresMaestrosORIGINAL1031169106.2022.pdf1031169106.2022.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf6402858https://repositorio.unal.edu.co/bitstream/unal/81767/1/1031169106.2022.pdf24ef65d2724901e79b9e5bd6aee53f42MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81767/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1031169106.2022.pdf.jpg1031169106.2022.pdf.jpgGenerated Thumbnailimage/jpeg4195https://repositorio.unal.edu.co/bitstream/unal/81767/3/1031169106.2022.pdf.jpgec3b9dcf1b109b58c77b7ca58cacb119MD53unal/81767oai:repositorio.unal.edu.co:unal/817672024-08-07 23:10:53.589Repositorio Institucional Universidad Nacional de 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