Stability of methods for differential expression analysis of RNA-seq data
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[1] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[2] R. Real,et al. The Probabilistic Basis of Jaccard's Index of Similarity , 1996 .
[3] John Quackenbush,et al. Multiple-laboratory comparison of microarray platforms , 2005, Nature Methods.
[4] Melanie Hilario,et al. Knowledge and Information Systems , 2007 .
[5] Ludmila I. Kuncheva,et al. A stability index for feature selection , 2007, Artificial Intelligence and Applications.
[6] Sandrine Dudoit,et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.
[7] Hui Xiao,et al. Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes , 2009, Bioinform..
[8] C. Elsik. The pea aphid genome sequence brings theories of insect defense into question , 2010, Genome Biology.
[9] W. Huber,et al. which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .
[10] Joseph K. Pickrell,et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing , 2010, Nature.
[11] R. Spielman,et al. Polymorphic Cis- and Trans-Regulation of Human Gene Expression , 2010, PLoS biology.
[12] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[13] A. Conesa,et al. Differential expression in RNA-seq: a matter of depth. , 2011, Genome research.
[14] Peter J. Bickel,et al. Measuring reproducibility of high-throughput experiments , 2011, 1110.4705.
[15] Daniel Bottomly,et al. Evaluating Gene Expression in C57BL/6J and DBA/2J Mouse Striatum Using RNA-Seq and Microarrays , 2011, PloS one.
[16] R. Tibshirani,et al. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. , 2012, Biostatistics.
[17] Robert Tibshirani,et al. Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data , 2013, Statistical methods in medical research.
[18] Ning Leng,et al. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments , 2013, Bioinform..
[19] C. Mason,et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data , 2013, Genome Biology.
[20] Charity W. Law,et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.
[21] Wolfgang Huber,et al. Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size , 2013, Bioinform..
[22] Mark D. Robinson,et al. Robustly detecting differential expression in RNA sequencing data using observation weights , 2013, Nucleic acids research.
[23] W. Huber,et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.
[24] Li-Feng Zhang,et al. LFCseq: a nonparametric approach for differential expression analysis of RNA-seq data , 2014, BMC Genomics.
[25] Daniel J. Gaffney,et al. A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.
[26] Paolo Frasconi,et al. Machine Learning and Knowledge Discovery in Databases , 2016, Lecture Notes in Computer Science.
[27] Sophie Lamarre,et al. Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size , 2018, Front. Plant Sci..
[28] Charlotte Soneson,et al. Bias, robustness and scalability in single-cell differential expression analysis , 2018, Nature Methods.
[29] Ashley J Waardenberg,et al. consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction , 2019, PeerJ.