OMICfpp: a fuzzy approach for paired RNA-Seq counts
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OMICfpp: a fuzzy approach for paired RNA-Seq counts

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OMICfpp: a fuzzy approach for paired RNA-Seq counts

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dc.contributor.author Berral-González, Alberto
dc.contributor.author Riffo Campos, Angela Leticia
dc.contributor.author Ayala Gallego, Guillermo
dc.date.accessioned 2019-06-18T14:52:39Z
dc.date.available 2019-06-18T14:52:39Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10550/70499
dc.description.abstract BACKGROUND: RNA sequencing is a widely used technology for differential expression analysis. However, the RNA-Seq do not provide accurate absolute measurements and the results can be different for each pipeline used. The major problem in statistical analysis of RNA-Seq and in the omics data in general, is the small sample size with respect to the large number of variables. In addition, experimental design must be taken into account and few tools consider it. RESULTS: We propose OMICfpp, a method for the statistical analysis of RNA-Seq paired design data. First, we obtain a p-value for each case-control pair using a binomial test. These p-values are aggregated using an ordered weighted average (OWA) with a given orness previously chosen. The aggregated p-value from the original data is compared with the aggregated p-value obtained using the same method applied to random pairs. These new pairs are generated using between-pairs and complete randomization distributions. This randomization p-value is used as a raw p-value to test the differential expression of each gene. The OMICfpp method is evaluated using public data sets of 68 sample pairs from patients with colorectal cancer. We validate our results through bibliographic search of the reported genes and using simulated data set. Furthermore, we compared our results with those obtained by the methods edgeR and DESeq2 for paired samples. Finally, we propose new target genes to validate these as gene expression signatures in colorectal cancer. OMICfpp is available at http://www.uv.es/ayala/software/OMICfpp_0.2.tar.gz . CONCLUSIONS: Our study shows that OMICfpp is an accurate method for differential expression analysis in RNA-Seq data with paired design. In addition, we propose the use of randomized p-values pattern graphic as a powerful and robust method to select the target genes for experimental validation.
dc.language.iso eng
dc.relation.ispartof Bmc Genomics, 2019, vol. 20, num. 259, p. 1-20
dc.rights.uri info:eu-repo/semantics/openAccess
dc.source Berral-González, Alberto Riffo Campos, Angela Leticia Ayala Gallego, Guillermo 2019 OMICfpp: a fuzzy approach for paired RNA-Seq counts Bmc Genomics 20 259 1 20
dc.subject RNA
dc.subject Càncer
dc.title OMICfpp: a fuzzy approach for paired RNA-Seq counts
dc.type info:eu-repo/semantics/article
dc.date.updated 2019-06-18T14:52:40Z
dc.identifier.doi https://doi.org/10.1186/s12864-019-5496-5
dc.identifier.idgrec 133371

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