2016
DOI: 10.1093/bib/bbw092
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Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis

Abstract: Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation strategy and data filtering. We propose an evaluation of the impact of these choices when few biological replicates are available through the use of synthetic data sets. This framework is based on real data sets a… Show more

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Cited by 48 publications
(54 citation statements)
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“…Here we summarize considerations for avian epigenome investigation from sequencing considerations to the data analysis involving huge datasets. The importance of a carefully designed and executed experimental design has been addressed in previous reports [90,91]. In particular, experimental design should take into account parental history (such as rearing conditions and nutrition) and hatchery practices.…”
Section: Design Of Epigenomic Studiesmentioning
confidence: 99%
“…Here we summarize considerations for avian epigenome investigation from sequencing considerations to the data analysis involving huge datasets. The importance of a carefully designed and executed experimental design has been addressed in previous reports [90,91]. In particular, experimental design should take into account parental history (such as rearing conditions and nutrition) and hatchery practices.…”
Section: Design Of Epigenomic Studiesmentioning
confidence: 99%
“…The distribution of the resulting p‐values followed the quality criterion described by Rigaill et al . (). The Benjamini−Hochberg correction was used to control false discovery rate (FDR; Benjamini and Hochberg, ).…”
Section: Methodsmentioning
confidence: 97%
“…In our model, the log2ratio was expressed as a linear combination of a species effect and the p-values corresponding to the contrasts described in Data S1 were calculated. The distribution of the resulting p-values followed the quality criterion described by Rigaill et al (2018). The BenjaminiÀHochberg correction was used to control false discovery rate (FDR; Benjamini and Hochberg, 1995).…”
Section: Statisticsmentioning
confidence: 99%
“…Choices for the differential analysis were made based on Rigaill et al, 2018. To increase the detection power by limiting the number of statistical tests (Bourgon et al, 2010) we performed an independent filtering by discarding genes which did not have at least 1 read after a count per million analysis (CPM) in at least one half of the samples.…”
Section: Rna-seq Read Processing and Gene Expression Analysismentioning
confidence: 99%