Open AccessBook
Bioinformatics and Computational Biology Solutions Using R and Bioconductor
TLDR
In this article, the authors present a detailed case study of R algorithms with publicly available data, and a major section of the book is devoted to fully worked case studies, with a companion website where readers can reproduce every number, figure and table on their own computers.Abstract:
Full four-color book.
Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R.
All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies.
Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.read more
Citations
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Journal ArticleDOI
limma powers differential expression analyses for RNA-sequencing and microarray studies
Matthew E. Ritchie,Belinda Phipson,Di Wu,Yifang Hu,Charity W. Law,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Journal ArticleDOI
WGCNA: an R package for weighted correlation network analysis.
Peter Langfelder,Steve Horvath +1 more
TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
Journal ArticleDOI
Predicting effective microRNA target sites in mammalian mRNAs
TL;DR: It is shown that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical.
Journal ArticleDOI
Gene ontology analysis for RNA-seq: accounting for selection bias
TL;DR: Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.
Journal ArticleDOI
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Charity W. Law,Charity W. Law,Yunshun Chen,Yunshun Chen,Wei Shi,Wei Shi,Gordon K. Smyth,Gordon K. Smyth +7 more
TL;DR: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments, and the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
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