Microarray data


There is a suite of procedures for the design, analysis and visualization of two-colour and Affymetrix microarray data. These are used by the Microarray menus in GenStat for Windows.


AGBIB
generates balanced incomplete block designs

AGLOOP
generates loop designs e.g. for time-course microarray experiments

AGREFERENCE
generates reference-level designs e.g. for microarray experiments

MADESIGN
assesses the efficiency of a two-colour microarray design

MACALCULATE
corrects and transforms two-colour microarray differential expressions

MNORMALIZE
normalizes two-colour microarray data

MAESTIMATE
estimates treatment effects from a two-colour microarray design

AFFYMETRIX
estimates expression values for Affymetrix slides.

MABGCORRECT
performs background correction of Affymetrix slides

MAROBUSTMEANS
does a robust means analysis for Affymetrix slides

MARMA
calculates Affymetrix expression values

MAVDIFFERENCE
applies the average difference algorithm to Affymetrix data

DMADENSITY
plots the empirical CDF or PDF (kernel smoothed) by groups

MAHISTOGRAM
plots histograms of microarray data

MAPLOT
produces two-dimensional plots of microarray data

MASHADE
produces shade plots to display spatial variation of microarray data

MAVOLCANO
produces volcano plots of microarray data

MAPCLUSTER
clusters probes or genes with microarray data

MASCLUSTER
clusters microarray slides

MA2CLUSTER
performs a two-way clustering of microarray data by probes (or genes) and slides

FDRMIXTURE
estimates false discovery rates using mixture distributions

MAEBAYES
modifies t-values by an empirical Bayes method.

MPOLISH
performs a median polish of two-way data

QNORMALIZE
performs quantile normalization

THINPLATE
calculates the basis functions for thin-plate splines

TUKEYBIWEIGHT
estimates means using the Tukey biweight algorithm