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
TLDR
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.Abstract:
New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. 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. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.read more
Citations
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Distinct structural classes of activating FOXA1 alterations in advanced prostate cancer
Abhijit Parolia,Marcin Cieslik,Shih-Chun Chu,Lanbo Xiao,Takahiro Ouchi,Yuping Zhang,Xiaoju Wang,Pankaj Vats,Xuhong Cao,Sethuramasundaram Pitchiaya,Fengyun Su,Rui Wang,Felix Y. Feng,Yi-Mi Wu,Robert J. Lonigro,Dan R. Robinson,Arul M. Chinnaiyan +16 more
TL;DR: This study reaffirms the central role of FOXA1 in mediating oncogenesis driven by the androgen receptor, and provides mechanistic insights into how the classes of FoxA1 alteration promote the initiation and/or metastatic progression of prostate cancer.
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Immune cytolytic activity stratifies molecular subsets of human pancreatic cancer
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Differential regulation of PD-L1 expression by immune and tumor cells in NSCLC and the response to treatment with atezolizumab (anti–PD-L1)
Marcin Kowanetz,Wei Zou,Scott N. Gettinger,Hartmut Koeppen,Mark M. Kockx,Peter Schmid,Edward E. Kadel,Ignacio I. Wistuba,Jamie E. Chaft,Naiyer A. Rizvi,David R. Spigel,Alexander I. Spira,Fred R. Hirsch,Victor Cohen,Dustin Smith,Zach Boyd,Natasha Miley,Susan Flynn,Vincent Leveque,David S. Shames,Marcus Ballinger,Simonetta Mocci,Geetha Shankar,Roel Funke,Garret Hampton,Alan Sandler,Lukas C. Amler,Ira Mellman,Daniel S. Chen,Priti S. Hegde +29 more
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Evaluation of variability in human kidney organoids.
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TL;DR: A transcriptional analysis of kidney organoids reveals batch effects as the key drivers of variation, mainly through differences in maturity, and provides a list of highly variable genes and a method for estimating differentiation stage for improved disease modeling.
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A field guide for the compositional analysis of any-omics data.
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References
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