reading list for stats/etc

[stephenturner]

Nice list from Mike Love

Rendered: https://biodatascience.github.io/compbio/

Source: https://raw.githubusercontent.com/biodatascience/compbio/gh-pages/index.Rmd

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• What is the role of the computational biologist / statistician?
  • All biology is computational biology Florian Markowetz
  • Questions, Answers and Statistics Deborah Nolan
  • 50 Years of Data Science David Donoho
  • The Future of Data Analysis John Tukey (this article, discussed by Donoho, is from 1962)
  • Ten Simple Rules for Effective Statistical Practice Kass, Caffo, Davidian, Meng, Yu, and Reid
  • Statistical Modeling: The Two Cultures Leo Breiman
• Exploratory data analysis
  • dplyr - A Grammar of Data Manipulation
  • ggplot2
  • The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
• Bioconductor
  • Orchestrating high-throughput genomic analysis with Bioconductor Huber et al
• Distances and normalization
  • Differential expression analysis for sequence count data Simon Anders and Wolfgang Huber
  • Tackling the widespread and critical impact of batch effects in high-throughput data Leek et al
  • Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis Jeffrey Leek and John Storey
  • Normalization of RNA-seq data using factor analysis of control genes or samples Risso et al
  • Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses Stegle et al
• Multiple testing
  • Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Yoav Benjamini and Yosef Hochberg
  • A direct approach to false discovery rates John Storey
  • Statistical significance for genomewide studies John Storey and Robert Tibshirani
  • Large-scale simultaneous hypothesis testing Bradley Efron
  • Empirical Bayes Analysis of a Microarray Experiment Efron et al
  • Measuring reproducibility of high-throughput experiments Li et al
• Expectation maximization
  • What is the expectation maximization algorithm? Chuong B Do and Serafim Batzoglou
  • Gaussian mixture models and the EM algorithm Ramesh Sridharan
  • EM algorithm notes Andrew Ng
  • MEME: discovering and analyzing DNA and protein sequence motifs Bailey et al
• Hierarchical models
  • Linear models and empirical Bayes methods for assessing differential expression in microarray experiments Gordon Smyth
  • Analyzing ’omics data using hierarchical models Hongkai Ji and X Shirley Liu
  • Stein's Paradox in Statistics Bradley Efron and Carl Morris
  • Stein's estimation rule and its competitors - an empirical Bayes approach Bradley Efron and Carl Morris
• Signal processing
  • An Introduction to Hidden Markov Models Lawrence Rabiner and Biing-Hwang Juang
  • Hidden Markov models approach to the analysis of array CGH data Fridlyand et al
• Network analysis
  • Static And Dynamic DNA Loops Form AP-1 Bound Activation Hubs During Macrophage Development Phanstiel et al

[stephenturner]

Add motulsky's common misconceptions about data analysis and statistics

http://onlinelibrary.wiley.com/store/10.1002/prp2.93/asset/prp293.pdf;jsessionid=F9128252FFC4AD9777F5BFCEB2015BD6.f01t02?v=1&t=jb8btqrn&s=d717ac241cf7b4f866ea86c5709b499b3e6a9a61

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4203998/?report=classic