title: 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 and allows the exploration of various scenarios differing in the proportion of non-differentially expressed genes. Hence, it provides an evaluation of the key ingredients of the differential analysis, free of the biases associated with the simulation of data using parametric models. Our results show the relevance of a proper modeling of the mean by using linear or generalized linear modeling. Once the mean is properly modeled, the impact of the other parameters on the performance of the test is much less important. Finally, we propose to use the simple visualization of the raw P-value histogram as a practical evaluation criterion of the performance of differential analysis methods on real data sets.
authors.forename: Guillem
authors.initials: G
authors.lastname: Rigaill
authors.forename: Sandrine
authors.initials: S
authors.lastname: Balzergue
authors.forename: Véronique
authors.initials: V
authors.lastname: Brunaud
authors.forename: Eddy
authors.initials: E
authors.lastname: Blondet
authors.forename: Andrea
authors.initials: A
authors.lastname: Rau
authors.forename: Odile
authors.initials: O
authors.lastname: Rogier
authors.forename: José
authors.initials: J
authors.lastname: Caius
authors.forename: Cathy
authors.initials: C
authors.lastname: Maugis-Rabusseau
authors.forename: Ludivine
authors.initials: L
authors.lastname: Soubigou-Taconnat
authors.forename: Sébastien
authors.initials: S
authors.lastname: Aubourg
authors.forename: Claire
authors.initials: C
authors.lastname: Lurin
authors.forename: Marie-Laure
authors.initials: ML
authors.lastname: Martin-Magniette
authors.forename: Etienne
authors.initials: E
authors.lastname: Delannoy
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D029681:Arabidopsis Proteins / Q000235:genetics*; D003198:Computer Simulation; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D059467:Transcriptome
chemical_list: D029681:Arabidopsis Proteins; D012313:RNA
keywords: RNA-seq; benchmark data set; differential analysis