Rafael A. Irizarry

TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.

TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.

TL;DR: Three methods of performing normalization at the probe intensity level are presented: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure and the simplest and quickest complete data method is found to perform favorably.

TL;DR: Salmon is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.

TL;DR: It is found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models.