2026-06-14T03:39:13Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/184122024-09-21T21:35:11Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927col_20.500.12105_16963col_20.500.12105_16984
Repisalud author Jabato, Fernando M author Córdoba-Caballero, José author Rojano, Elena author Romá-Mateo, Carlos author Sanz, Pascual author Pérez, Belén author Gallego, Diana author Seoane, Pedro author Ranea, Juan A G author Perkins, James R 2024-02-19T15:29:46Z 2024-02-19T15:29:46Z 2021-07-23 10.1038/s41598-021-94343-w 2045-2322 Scientific reports http://hdl.handle.net/10668/18254 34301987 http://hdl.handle.net/20.500.12105/18412 High-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite . It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples . eng Gene expression analysis method integration and co-expression module detection applied to rare glucide metabolism disorders using ExpHunterSuite. research article