Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology
| cris.virtual.author-orcid | 0000-0001-6553-6023 | |
| cris.virtual.author-orcid | 0000-0002-1806-0891 | |
| cris.virtual.author-orcid | 0000-0001-9465-3851 | |
| cris.virtual.author-orcid | 0000-0002-5550-7007 | |
| cris.virtualsource.author-orcid | 024ca314-91a3-4ea2-87e2-ae22717d7389 | |
| cris.virtualsource.author-orcid | 6a2f8857-003b-41ec-9112-0ef6941bfd06 | |
| cris.virtualsource.author-orcid | a96d2343-ee65-450d-8ce2-355a51255d10 | |
| cris.virtualsource.author-orcid | b7671e1c-9850-4ccf-9eeb-882ff1d3c932 | |
| dc.abstract.en | We compared two of the most common methods for differential expression analysis in the RNA-seq field: edgeR and DESeq2. We evaluated these methods based on four real RNA-seq plant datasets. The results indicate that there is a large number of joint differentially expressed genes between the two methods. However, depending on the research goal and the preparation of an experiment, different approaches to statistical analysis and interpretation of the results can be suggested. We focus on answering the question: what workflow should be used in the statistical analysis of the datasets under consideration to minimize the number of falsely identified differentially expressed genes? | |
| dc.affiliation | Wydział Rolnictwa, Ogrodnictwa i Bioinżynierii | |
| dc.affiliation.institute | Katedra Metod Matematycznych i Statystycznych | |
| dc.contributor.author | Niedziela, Grażyna | |
| dc.contributor.author | Szabelska-Beręsewicz, Alicja | |
| dc.contributor.author | Zyprych-Walczak, Joanna Grażyna | |
| dc.contributor.author | Graczyk, Małgorzata | |
| dc.date.access | 2025-11-12 | |
| dc.date.accessioned | 2025-11-12T09:53:03Z | |
| dc.date.available | 2025-11-12T09:53:03Z | |
| dc.date.copyright | 2022-12-30 | |
| dc.date.issued | 2022 | |
| dc.description.abstract | <jats:title>Summary</jats:title> <jats:p>We compared two of the most common methods for differential expression analysis in the RNA-seq field: edgeR and DESeq2. We evaluated these methods based on four real RNA-seq plant datasets. The results indicate that there is a large number of joint differentially expressed genes between the two methods. However, depending on the research goal and the preparation of an experiment, different approaches to statistical analysis and interpretation of the results can be suggested. We focus on answering the question: what workflow should be used in the statistical analysis of the datasets under consideration to minimize the number of falsely identified differentially expressed genes?</jats:p> | |
| dc.description.accesstime | at_publication | |
| dc.description.bibliography | il., bibliogr. | |
| dc.description.finance | publication_nocost | |
| dc.description.financecost | 0,00 | |
| dc.description.number | 2 | |
| dc.description.points | 20 | |
| dc.description.version | final_published | |
| dc.description.volume | 59 | |
| dc.identifier.doi | 10.2478/bile-2022-0009 | |
| dc.identifier.eissn | 2199-577X | |
| dc.identifier.issn | 1896-3811 | |
| dc.identifier.uri | https://sciencerep.up.poznan.pl/handle/item/5836 | |
| dc.identifier.weblink | https://reference-global.com/article/10.2478/bile-2022-0009 | |
| dc.language | en | |
| dc.relation.ispartof | Biometrical Letters | |
| dc.relation.pages | 127-139 | |
| dc.rights | CC-BY-NC-ND | |
| dc.sciencecloud | nosend | |
| dc.share.type | OPEN_JOURNAL | |
| dc.subject.en | edgeR | |
| dc.subject.en | DESeq2 | |
| dc.subject.en | RNA-seq | |
| dc.subject.en | statistical methods for differential analysis | |
| dc.title | Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology | |
| dc.type | JournalArticle | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 2 | |
| oaire.citation.volume | 59 |