limma powers differential expression analyses for RNA-sequencing and microarray studies
暂无分享,去创建一个
Matthew E. Ritchie | Charity W. Law | W. Shi | G. Smyth | M. Ritchie | B. Phipson | Diabetes-Ling Wu | Yifang Hu | C. Law | Wei Shi
[1] B. Efron,et al. Stein's Estimation Rule and Its Competitors- An Empirical Bayes Approach , 1973 .
[2] C. Morris. Parametric Empirical Bayes Inference: Theory and Applications , 1983 .
[3] D. Altman,et al. STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.
[4] J M Bland,et al. Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .
[5] Acknowledgements , 1992, Experimental Gerontology.
[6] William S. Cleveland,et al. Visualizing Data , 1993 .
[7] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[8] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[9] M. Soller,et al. A whole genome scan for quantitative trait loci affecting milk protein percentage in Israeli-Holstein cattle, by means of selective milk DNA pooling in a daughter design, using an adjusted false discovery rate criterion. , 2001, Genetics.
[10] Terence P. Speed,et al. Normalization for cDNA microarry data , 2001, SPIE BiOS.
[11] Michael L. Bittner,et al. Microarrays: Optical Technologies and Informatics , 2001 .
[12] Thomas Seidl,et al. Changes in gene expression profiles in developing B cells of murine bone marrow. , 2002, Genome research.
[13] S. Dudoit,et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.
[14] Charles L. Kooperberg,et al. Improved Background Correction for Spotted DNA Microarrays , 2002, J. Comput. Biol..
[15] Friedrich Leisch,et al. Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis , 2002, COMPSTAT.
[16] S. Dudoit,et al. STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .
[17] Terry Speed,et al. Normalization of cDNA microarray data. , 2003, Methods.
[18] Terence P. Speed,et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..
[19] D. R. Goldstein,et al. Science and Statistics: A Festschrift for Terry Speed , 2003 .
[20] M. Hofker. Faculty Opinions recommendation of PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. , 2003 .
[21] Yee Hwa Yang,et al. Normalization for two-color cDNA microarray data , 2003 .
[22] M. Ritchie. Quantitative quality control and background correction for two-colour microarray data , 2004 .
[23] Gordon K. Smyth,et al. limmaGUI: A graphical user interface for linear modeling of microarray data , 2004, Bioinform..
[24] Gordon K Smyth,et al. Statistical Applications in Genetics and Molecular Biology Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2011 .
[25] Jean YH Yang,et al. Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.
[26] Gordon K. Smyth,et al. Use of within-array replicate spots for assessing differential expression in microarray experiments , 2005, Bioinform..
[27] Øyvind Langsrud,et al. Rotation tests , 2005, Stat. Comput..
[28] Gordon K. Smyth,et al. limma: Linear Models for Microarray Data , 2005 .
[29] Gordon K Smyth,et al. Identification and functional significance of genes regulated by structurally different histone deacetylase inhibitors. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[30] Yipeng Wang,et al. WebArray: an online platform for microarray data analysis , 2005, BMC Bioinformatics.
[31] B. Lindqvist,et al. Estimating the proportion of true null hypotheses, with application to DNA microarray data , 2005 .
[32] Beate Sick,et al. RACE: Remote Analysis Computation for gene Expression data , 2005, Nucleic Acids Res..
[33] Gordon K. Smyth,et al. Empirical array quality weights in the analysis of microarray data , 2006, BMC Bioinformatics.
[34] Pablo Tamayo,et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[35] Robert Gentleman,et al. Reproducible Research: A Bioinformatics Case Study , 2005, Statistical applications in genetics and molecular biology.
[36] Rafael A. Irizarry,et al. Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005 .
[37] Gordon K. Smyth,et al. affylmGUI: a graphical user interface for linear modeling of single channel microarray data , 2006, Bioinform..
[38] Nicolas Servant,et al. Goulphar: rapid access and expertise for standard two-color microarray normalization methods , 2006, BMC Bioinformatics.
[39] Zlatko Trajanoski,et al. CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis , 2006, Nucleic Acids Res..
[40] Alicia Oshlack,et al. Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes , 2007, Genome Biology.
[41] Dan Nettleton,et al. Estimating the number of true null hypotheses from a histogram of p values , 2006 .
[42] Mario Medvedovic,et al. Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments , 2006, BMC Bioinformatics.
[43] Hubert Rehrauer,et al. MAGMA: analysis of two-channel microarrays made easy , 2007, Nucleic Acids Res..
[44] Matthew E Ritchie,et al. Integrative analysis of RUNX1 downstream pathways and target genes , 2008, BMC Genomics.
[45] Mariana L. Neves,et al. Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software suite , 2007, Nucleic Acids Res..
[46] Gordon K. Smyth,et al. A comparison of background correction methods for two-colour microarrays , 2007, Bioinform..
[47] Peter Bühlmann,et al. Analyzing gene expression data in terms of gene sets: methodological issues , 2007, Bioinform..
[48] Ning Zhang,et al. Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics , 2008, BMC Bioinformatics.
[49] C. Mayer,et al. NuGO contributions to GenePattern , 2008, Genes & Nutrition.
[50] Matthew D. Young,et al. Gene ontology analysis for RNA-seq: accounting for selection bias , 2010, Genome Biology.
[51] M. Robinson,et al. A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.
[52] G. Smyth,et al. Microarray background correction: maximum likelihood estimation for the normal–exponential convolution , 2008, Biostatistics.
[53] Gordon K. Smyth,et al. Testing significance relative to a fold-change threshold is a TREAT , 2009, Bioinform..
[54] R. Uibo,et al. Aire-Deficient C57BL/6 Mice Mimicking the Common Human 13-Base Pair Deletion Mutation Present with Only a Mild Autoimmune Phenotype1 , 2009, The Journal of Immunology.
[55] S. Fox,et al. Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers , 2009, Nature Medicine.
[56] Wei Shi,et al. Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips , 2010, Nucleic acids research.
[57] J. Visvader,et al. Control of mammary stem cell function by steroid hormone signalling , 2010, Nature.
[58] Wei Shi,et al. Estimating the proportion of microarray probes expressed in an RNA sample , 2010, Nucleic acids research.
[59] Belinda Phipson,et al. Opposing roles of polycomb repressive complexes in hematopoietic stem and progenitor cells. , 2010, Blood.
[60] Joseph K. Pickrell,et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing , 2010, Nature.
[61] Elgene Lim,et al. Open Access Research Article Transcriptome Analyses of Mouse and Human Mammary Cell Subpopulations Reveal Multiple Conserved Genes and Pathways , 2022 .
[62] Heiko A. Mannsperger,et al. RPPanalyzer: Analysis of reverse-phase protein array data , 2010, Bioinform..
[63] Mark D. Robinson,et al. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..
[64] Di Wu,et al. ROAST: rotation gene set tests for complex microarray experiments , 2010, Bioinform..
[65] Maria Teresa Dell'Anno,et al. Direct generation of functional dopaminergic neurons from mouse and human fibroblasts , 2011, Nature.
[66] Colin N. Dewey,et al. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.
[67] Helga Thorvaldsdóttir,et al. Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..
[68] G. Smyth,et al. Camera: a competitive gene set test accounting for inter-gene correlation , 2012, Nucleic acids research.
[69] K. Hansen,et al. Removing technical variability in RNA-seq data using conditional quantile normalization , 2012, Biostatistics.
[70] Tim Beißbarth,et al. Detection of Simultaneous Group Effects in MicroRNA Expression and Related Target Gene Sets , 2012, PloS one.
[71] Gordon K. Smyth,et al. Separate-channel analysis of two-channel microarrays: recovering inter-spot information , 2013, BMC Bioinformatics.
[72] W. Shi,et al. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote , 2013, Nucleic acids research.
[73] Belinda Phipson,et al. Empirical Bayes in the presence of exceptional cases, with application to microarray data , 2013 .
[74] Jarny Choi. Guide: a desktop application for analysing gene expression data , 2013, BMC Genomics.
[75] Gordon K Smyth,et al. The use of miRNA microarrays for the analysis of cancer samples with global miRNA decrease , 2013, RNA.
[76] C. Mason,et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data , 2013, Genome Biology.
[77] J S Liu,et al. Gene-expression data integration to squamous cell lung cancer subtypes reveals drug sensitivity , 2013, British Journal of Cancer.
[78] Charity W. Law,et al. voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.
[79] Wei Shi,et al. featureCounts: an efficient general-purpose read summarization program , 2013 .
[80] Belinda Phipson. Empirical bayes modelling of expression profiles and their associations , 2013 .
[81] Roberto Romero,et al. A Comparison of Gene Set Analysis Methods in Terms of Sensitivity, Prioritization and Specificity , 2013, PloS one.
[82] David P. Kreil,et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium , 2014, Nature Biotechnology.
[83] Nuno A. Fonseca,et al. Expression Atlas update—a database of gene and transcript expression from microarray- and sequencing-based functional genomics experiments , 2013, Nucleic Acids Res..
[84] Christopher Ricks,et al. To J.S. , 2014 .
[85] Pax5 loss imposes a reversible differentiation block in B-progenitor acute lymphoblastic leukemia. , 2014, Genes & development.
[86] A. Oshlack,et al. DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging , 2014, bioRxiv.
[87] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[88] Aaron T. L. Lun,et al. De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly , 2014, Nucleic acids research.
[89] Wei Shi,et al. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..
[90] Yihui Xie,et al. Dynamic Documents with R and knitr , 2015 .
[91] Paul Theodor Pyl,et al. HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.