FlexArray

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FlexArray is an R based,  Microsoft Windows software for statistical analysis of microarray data. Currently, the analysis of Affymetrix GeneChip ®, NimbleGen ® and Illumina BeadChip ® expression arrays is supported.

The main strong points of this software are

Runs on Microsoft Windows

Support for Affymetrix GeneChip ® arrays and support for Illumina BeadChip ® expression arrays

Easy to use data import

Intuitive visual representation of the data analysis process in the form of an "analysis pipeline"

Comprehensive collection of statistical data processing algorithms, built on the framework of Bioconductor:

Preprocessing and normalization: MAS 5, RMA, PLIER, GC-RMA, dChip, and Custom for Affymetrix, lumi for Illumina and others

Statistical testing: t-test, ANOVA, LPE, SAM, Empirical Bayes, Bayes T, cyber-T and many others

Fold-change and group statistics calculations

False discovery rate: FDR (Benjamini Hochberg and Benjamini Yekutieli), FWER (Holm, Hochberg, Sidak Single Step, Sidak Step Down, and Bonferroni)

Description of every algorithm and every parameter

If an algorithm is not available in a particular context, FlexArray will provide a detailed description of the reason why.

Plug-in architecture for algorithms: end-users can easily integrate new analysis methods into the software

A wide variety of plots applicable to every level of the analysis:

Quality Control plots for raw data, including spot intensity maps, QQ-plots, scatter plots, histograms, and box plots

Plots for normalized data and for sample means, including scatter plots, QQ plots, MvA plots, and histograms

PCA plots for quick assessment of the experiment

Plots for statistical test results, including Volcano plots, and histograms

A number of innovative plots, e.g. the shiv plot combining a box plot with a signal intentity histogram and a data variance curve

A number of plots specifically designed to facilitate comparisons between various analysis methods, e.g. the CAT plot, or the overabundance plot

Venn diagrams to compare gene lists

Description of every plot

Plots can be zoomed in, panned, and exported to a number of formats, including EPS

Plug-in architecture for plots: it is quite easy to add a new plot to the program

Context-sensitive data table with such features as sorting, filtering, search, export, column resizing and reordering, etc.

Flexible annotation import

Gene list generation, management, and visualization

Tools to compare outputs of multiple analysis methods

Full analysis history, including the values of all parameters used in algorithms, full execution log, and remembering the exact form of the R script used

Re-usable analysis protocols: create an analysis schema once and then re-use it for your subsequent experiments, or pass it on to your colleagues