1 Overview

This document gives an overview of the MANOR package, which is devoted to the normalization of Array Comparative Genomic Hybridization (array-CGH) data(8,7,4,3,9). Normalization is a crucial step of microarray analysis which aims at separating biologically relevant signal from experimental artifacts. Typical input data is a file generated by an image analysis software such as Genepix or SPOT (5), containing several measurements for each biological variable of interest, i.e. several replicated spots for each clone; this spot-level data is filtered with various statistical criteria (including a spatial bias detection step which is described in (6)), and aggregated into clean clone-level data.

Using the arrayCGH framework developped in the package GLAD, which is available under Bioconductor. We propose the formalism of flags to handle clone and spot filtering: the core of the normalization process consists in applying to an arrayCGH object a list of flags that successively exclude from the data all irrelevant spots or clones.

We also define quality scores (qscores) allowing to evaluate the quality of an array after normalization: these scores can be used directly to compare the quality of different arrays after the same normalization process, or to compare the efficiency of different normalization processes on a given array or on a given batch of arrays.

This document is organized as follows: after a short description of optional items we add to arrayCGH objects (section 2, we introduce the classes flag (section 3) and qscore (section 4) with their attributes and dedicated methods; then we describe two useful graphical representation functions (section 6), namely genome.plot and report.plot; Afterwards we give a short description of the array-CGH datasets we provide (section 5); finally we illustrate the usage of MANOR by a sample R script (section 7).