Transcription of limma Linear Models for Microarray and RNA-Seq Data …
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limma Linear Models for Microarray and RNA-Seq Data …
limma : Linear Models for Microarray and RNA-Seq DataUser s GuideGordon K. Smyth, Matthew Ritchie, Natalie Thorne,James Wettenhall, Wei Shi and Yifang HuBioinformatics Division, The Walter and Eliza Hall Instituteof Medical Research, Melbourne, AustraliaFirst edition 2 December 2002 Last revised 14 November 2021 This free open-source software implements academic researchby the authors and co-workers. If you use it, please supportthe project by citing the appropriate journal articles listed inSection Introduction52 Citinglimma.. Installation .. How to get help ..93 Quick A brief introduction to R .. SamplelimmaSession .. Data Objects .. 134 Reading Microarray Scope of this Chapter .. Recommended Files .. The Targets Frame .. Reading Two-Color Intensity Data .. Reading Single-Channel Agilent Intensity Data .. Reading Illumina BeadChip Data.
Liu, R, Holik, AZ, Su, S, Jansz, N, Chen, K, Leong, HS, Blewitt, ME, Asselin-Labat, M-L, Smyth, GK, Ritchie, ME (2015). Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Research 43, e97. Law et al (2014) describe the voom and limma-trend pipelines for RNA-seq, while Liu et al (2015)
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