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ArrayAnalysis

ArrayAnalysis offers user-friendly solutions for gene expression data analysis, from raw data to biological pathways. It contains modules of three types that can be lauched individually or successively as an integrated workflow.

[QC & pre-processing] module gathers a complete panel of QC plots and indicators: a variety output plots or tables help you determine sample quality, hybridisation and overall signal quality, signal comparability and bias diagnostic and array correlation. Pre-processing methods combine probe set re-annotation, background correction and normalisation. Currently, modules are available for Affymetrix and Illumina arrays.

[Statistical analysis] module models your gene expression data using a linear model applied at the probe set level. You are given the possibility to custom your analysis and computing several models on a run. For a quick interpretation of the output result, P-Value and Fold change histograms can be computed as well as custom summary tables.

[Pathway analysis] module allows to quickly and easily visualise your statistics results on a biological pathway basis and identify significantly changed processes using PathVisio technology. This module will be activated soon, for now a mock-up module is in place that shows the possibilities using an example data sets.

Get started
Launch one of the analysis modules now!

Download sources
Code for local use and development

Module description
Interpretation guide for the outputs of the QC modules

Documentation
User guide, local installation, functions description

We gratefully acknowledge all authors of R/BioConductor packages used by ArrayAnalysis.org.