Chris Pratt, Elizabeth Banks, Will Arnett and Morgan Freeman lend their voices to this CGI-animated comedy based on the line of toys made by Lego. The wicked Lord Business (Will Ferrell) is determined to destroy the Lego universe and rebuild it using glue - which goes against the very nature of Lego. Mistaken as the 'Special', the only surviving Master Builder, the rather ordinary Emmet (Pratt) is selected to lead a group of figures on a mission to put a stop to Lord Business's evil plan. Emmet is helped by wise wizard Vitruvius (Freeman), tough girl Wyldstyle (Banks) and DC superhero Batman (Arnett), but can he find something extraordinary within himself in order to save the world? The film also features the voices of Channing Tatum, Liam Neeson, Nick Offerman, Alison Brie and Jonah Hill.

Chris Pratt, Elizabeth Banks, Will Arnett and Morgan Freeman lend their voices to this CGI-animated comedy based on the line of toys made by Lego. The wicked Lord Business (Will Ferrell) is determined to destroy the Lego universe and rebuild it using glue - which goes against the very nature of Lego. Mistaken as the 'Special', the only surviving Master Builder, the rather ordinary Emmet (Pratt) is selected to lead a group of figures on a mission to put a stop to Lord Business's evil plan. Emmet is helped by wise wizard Vitruvius (Freeman), tough girl Wyldstyle (Banks) and DC superhero Batman (Arnett), but can he find something extraordinary within himself in order to save the world? The film also features the voices of Channing Tatum, Liam Neeson, Nick Offerman, Alison Brie and Jonah Hill. The film won a BAFTA for Best Animated Film and was nominated for Best Original Song at the Oscars.

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students.

Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book.

Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include:

Multiplicity adjustment

Test statistics and procedures for the analysis of dose-response microarray data

Resampling-based inference and use of the SAM method for small-variance genes in the data

Identification and classification of dose-response curve shapes

Clustering of order-restricted (but not necessarily monotone) dose-response profiles

Gene set analysis to facilitate the interpretation of microarray results

Hierarchical Bayesian models and Bayesian variable selection

Non-linear models for dose-response microarray data

Multiple contrast tests

Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate

All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.

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