FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3126629478> ?p ?o ?g. }

• W3126629478 abstract "Abstract *Seq techniques (e.g. RNA-Seq) generate compositional datasets, i.e. the number of fragments sequenced is not proportional to the sample’s total RNA content. Thus, datasets carry only relative information, even though absolute RNA copy numbers are of interest. Current normalization methods assume most features do not change, which can lead to misleading conclusions when there are many changes. Furthermore, there are few real datasets and no simulation protocols currently available that can directly benchmark methods when many changes occur. We present absSimSeq , an R package that simulates compositional data in the form of RNA-Seq reads. We compared absSimSeq with several existing tools used for RNA-Seq differential analysis: sleuth, DESeq2, edgeR, limma, sleuth and ALDEx2 (which explicitly takes a compositional approach). We compared the standard normalization of these tools to either “compositional normalization”, which uses log-ratios to anchor the data on a set of negative control features, or RUVSeq, another tool that directly uses negative control features. Our analysis shows that common normalizations result in reduced performance with current methods when there is a large change in the total RNA per cell. Performance improves when spike-ins are included and used with a compositional approach, even if the spike-ins have substantial variation. In contrast, RUVSeq, which normalizes count data rather than compositional data, has poor performance. Further, we show that previous criticisms of spike-ins did not take into consideration the compositional nature of the data. We demonstrate that absSimSeq can generate more representative datasets for testing performance, and that spike-ins should be more frequently used in a compositional manner to minimize misleading conclusions in differential analyses. Author Summary A critical question in biomedical research is “Is there any change in the RNA transcript abundance when cellular conditions change?” RNA Sequencing (RNA-Seq) is a powerful tool that can help answer this question, but two critical parts of obtaining accurate measurements are (A) understanding the kind of data that RNA-Seq produces, and (B) “normalizing” the data between samples to allow for a fair comparison. Most tools assume that RNA-Seq data is count data, but in reality it is “compositional” data, meaning only percentages/proportions are available, which cannot directly answer the critical question. This leads to distorted results when attempting to simulate or analyze data that has a large global change. To address this problem, we designed a new simulation protocol called absSimSeq that can more accurately represent RNA-Seq data when there are large changes. We also proposed a “compositional normalization” method that can utilize “negative control” features that are known to not change between conditions to anchor the data. When there are many features changing, this approach improves performance over commonly used normalization methods across multiple tools. This work highlights the importance of having negative controls features available and of treating RNA-Seq data as compositional." @default.
• W3126629478 created "2021-02-15" @default.
• W3126629478 creator A5008942444 @default.
• W3126629478 creator A5027271279 @default.
• W3126629478 creator A5044103741 @default.
• W3126629478 creator A5086863480 @default.
• W3126629478 date "2019-03-02" @default.
• W3126629478 modified "2023-10-04" @default.
• W3126629478 title "Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data" @default.
• W3126629478 cites W1589417242 @default.
• W3126629478 cites W1819333709 @default.
• W3126629478 cites W2010305827 @default.
• W3126629478 cites W2025943989 @default.
• W3126629478 cites W2034937344 @default.
• W3126629478 cites W2039521726 @default.
• W3126629478 cites W2043831708 @default.
• W3126629478 cites W2064397275 @default.
• W3126629478 cites W2065128082 @default.
• W3126629478 cites W2072602203 @default.
• W3126629478 cites W2074414424 @default.
• W3126629478 cites W2101536220 @default.
• W3126629478 cites W2107018762 @default.
• W3126629478 cites W2110017531 @default.
• W3126629478 cites W2112876600 @default.
• W3126629478 cites W2130410032 @default.
• W3126629478 cites W2137526110 @default.
• W3126629478 cites W2141458291 @default.
• W3126629478 cites W2145416275 @default.
• W3126629478 cites W2147109452 @default.
• W3126629478 cites W2151876471 @default.
• W3126629478 cites W2152239989 @default.
• W3126629478 cites W2152544605 @default.
• W3126629478 cites W2156631105 @default.
• W3126629478 cites W2161577298 @default.
• W3126629478 cites W2168420558 @default.
• W3126629478 cites W2236822143 @default.
• W3126629478 cites W2323326409 @default.
• W3126629478 cites W2592811885 @default.
• W3126629478 cites W2611056508 @default.
• W3126629478 cites W2620721366 @default.
• W3126629478 cites W2625348030 @default.
• W3126629478 cites W2883542968 @default.
• W3126629478 cites W2949960840 @default.
• W3126629478 doi "https://doi.org/10.1101/564955" @default.
• W3126629478 hasPublicationYear "2019" @default.
• W3126629478 type Work @default.
• W3126629478 sameAs 3126629478 @default.
• W3126629478 citedByCount "13" @default.
• W3126629478 countsByYear W31266294782020 @default.
• W3126629478 countsByYear W31266294782021 @default.
• W3126629478 countsByYear W31266294782022 @default.
• W3126629478 countsByYear W31266294782023 @default.
• W3126629478 crossrefType "posted-content" @default.
• W3126629478 hasAuthorship W3126629478A5008942444 @default.
• W3126629478 hasAuthorship W3126629478A5027271279 @default.
• W3126629478 hasAuthorship W3126629478A5044103741 @default.
• W3126629478 hasAuthorship W3126629478A5086863480 @default.
• W3126629478 hasBestOaLocation W31266294781 @default.
• W3126629478 hasConcept C11413529 @default.
• W3126629478 hasConcept C119857082 @default.
• W3126629478 hasConcept C124101348 @default.
• W3126629478 hasConcept C13280743 @default.
• W3126629478 hasConcept C136886441 @default.
• W3126629478 hasConcept C144024400 @default.
• W3126629478 hasConcept C185798385 @default.
• W3126629478 hasConcept C19165224 @default.
• W3126629478 hasConcept C205649164 @default.
• W3126629478 hasConcept C2781147490 @default.
• W3126629478 hasConcept C41008148 @default.
• W3126629478 hasConceptScore W3126629478C11413529 @default.
• W3126629478 hasConceptScore W3126629478C119857082 @default.
• W3126629478 hasConceptScore W3126629478C124101348 @default.
• W3126629478 hasConceptScore W3126629478C13280743 @default.
• W3126629478 hasConceptScore W3126629478C136886441 @default.
• W3126629478 hasConceptScore W3126629478C144024400 @default.
• W3126629478 hasConceptScore W3126629478C185798385 @default.
• W3126629478 hasConceptScore W3126629478C19165224 @default.
• W3126629478 hasConceptScore W3126629478C205649164 @default.
• W3126629478 hasConceptScore W3126629478C2781147490 @default.
• W3126629478 hasConceptScore W3126629478C41008148 @default.
• W3126629478 hasLocation W31266294781 @default.
• W3126629478 hasLocation W31266294782 @default.
• W3126629478 hasOpenAccess W3126629478 @default.
• W3126629478 hasPrimaryLocation W31266294781 @default.
• W3126629478 hasRelatedWork W112744582 @default.
• W3126629478 hasRelatedWork W1485630101 @default.
• W3126629478 hasRelatedWork W1537221325 @default.
• W3126629478 hasRelatedWork W2030059621 @default.
• W3126629478 hasRelatedWork W2081245617 @default.
• W3126629478 hasRelatedWork W2126452360 @default.
• W3126629478 hasRelatedWork W2164513229 @default.
• W3126629478 hasRelatedWork W2498017833 @default.
• W3126629478 hasRelatedWork W2805452530 @default.
• W3126629478 hasRelatedWork W3033750096 @default.
• W3126629478 isParatext "false" @default.
• W3126629478 isRetracted "false" @default.
• W3126629478 magId "3126629478" @default.
• W3126629478 workType "article" @default.