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Biostatistics with R: An Introduction to Statistics Through Biological Data by Babak Shahbaba

Biostatistics with R: An Introduction to Statistics Through Biological Data Babak Shahbaba ebook
Format: pdf
Publisher: Springer
ISBN: 146141301X, 9781461413028
Page: 369

GeneSpring was also utilized to identify .. We hypothesize, that using statistical methods to detect differential expression between samples is biased by transcript length and that this bias is inherent to the standard RNA-seq process. Pathway analysis was performed with PathVisio 2.0.7 [25] (www.pathvisio.org) using filtered microarray expression data and pathway collections from KEGG and WikiPathways (www.wikipathways.org). Quantitative Corpus Linguistics With R - a Practical Introduction Heiberger R. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. Biostatistical Design and Analysis Using R - A Practical Guide Maindonald J. Using R for Data Management, Statistical Analysis, and Graphics Data Mashups in R Logan M. Feature of current protocols for RNA-seq technology. A Primer of Ecology with R Horton N. Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S (2005) Linear models for microarray data In: Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health). Data Analysis and Graphics Using R - An Example-Based Approach, 3e.