Statistical Design and Analysis of RNA Sequencing Data

By Paul L. Auer, Rebecca W. Doerge. Genetics, 2010

395 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a834b996c8ba92ea55c6dd7f4a710de6d4b4dc7c

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Citations in the corpus

Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014, background): “Moreover, the relatively high cost of sequencing has led to study designs that often emphasize per sample sequencing depth over biological replication [45], despite advice to the contrary [46,47].”
RNA Sequencing and Analysis (2015, neutral): “Although the number of replicates required per condition is an open research question, a minimum of three replicates per sample has been suggested (Auer and Doerge 2010).”
Effects of library size variance, sparsity, and compositionality on the analysis of microbiome data (2015, background): “…of a generalized linear model (GLM) that 200 assumes a distribution (Cameron & Trivedi), and there is debate about which distribution to use 201 (Auer & Doerge 2010; Cheung 2002; Connolly et al. 2009; Holmes et al. 2012; McMurdie & 202 Holmes 2014; Paulson et al. 2013; Rapaport et al. 2013;…”
rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data (2014, background): “Previous studies have investigated the experimental design for RNA-Seq with respect to the use of replicates, sample size, and sequencing depth (27 – 31).”
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, methodology): “Generalized linear models (GLMs) have been suggested for count data from SAGE or RNA-Seq experiments, with the counts treated as over-dispersed binomial (31,32,37), Poisson (21,41), over-dispersed Poisson (32,34) or Poisson with random effects (33).”
RNA-seq differential expression studies: more sequence or more replication? (2014, background): “Critical issues include biological replication and sequencing depth (Auer and Doerge, 2010), and inefficient designs of RNA-seq studies can lead to suboptimal power and waste of resources, especially in large-scale treatment-control studies.”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “Guidelines toward achieving this end have been reviewed for bulk RNA-seq studies [1, 2] and many of the same guidelines also hold for scRNA-seq.”
A survey of best practices for RNA-seq data analysis (2016, methodology): “These effects can be minimized by appropriate experimental design [51] or, alternatively, removed by batch-correction methods such as COMBAT [52] or ARSyN [20, 53].”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, methodology): “For RNA-seq, we recommend barcoding and multiplexing samples (over eight per lane) to reduce batch effects 216 .”
De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity (2013, methodology): “For example, Trinity now supports tools (e.g., RSEM 14 , edgeR 15 and DESeq 16 ) that take its output transcripts and test for differential expression while accounting for both technical and biological sources of variation 17–19 and correcting for multiple hypothesis testing.”
Comprehending crystalline β-carotene accumulation by comparing engineered cell models and the natural carotenoid-rich system of citrus (2011, neutral): “The resultant plasmid has the”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, background): “The gene expression data from bulk RNA-seq (or micro-arrays) are also challenged from a missing value problem [15].”
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor (2013, methodology): “Here R and Bioconductor are adopted to tie together the workflow and provide data structures, and their unique strengths in workflow components are leveraged, including statistical algorithms, visualization”
From RNA-seq reads to differential expression results (2010, background): “Design of RNA-seq experiments in general, including the fundamental considerations of blocking, randomization and replication, has recently been discussed in depth [56].”