ArrayAssist® Advanced Software is a tool for analyzing high throughput microarray based gene transcription and genotyping data. The software can input and analyze data from several different commonly used commercial expression and genotyping platforms including Affymetrix, Illumina, and Agilent. ArrayAssist® can also analyze data from custom printed 2-color microarrays. Raw fluorescence intensity data files from scanned Affymetrix, Illumina, and Agilent arrays are seamlessly imported to rapidly generate project files that, depending on the platform used, allow downstream analyses of differential gene transcription, gene set enrichment, gene ontology assessment, gene copy number, gene splicing, SNPs, genomic sequencing, hybridization performance, amplification quality, and other QC metrics.
Imported data is automatically annotated and then experimentally grouped and labeled by clicking on appropriate commands in pull down menus that guide the user through each step of the data import and analysis process. Gene transcription levels can be determined following data import using one of several common algorithm methods including robust multichip analysis (RMA), GC-RMA, dChip style model-based expression, GeneChip Operating System (GCOS), PLIER, and Microarray Suite 5 (MAS5). Multiple statistical analyses of the array data can be performed using T-tests, Mann Whitney tests, multi-way ANOVA, and repeated measures tests through the R statistical package integrated into the software. P-values for significance can be generated with permutations or asymptotically, and comprehensive testing corrections (FDR and FWER) can be applied as desired.
Multiple methods of data presentation are available in the ArrayAssist® Advanced software package, including scatter plots, heat maps, histograms, box and whisker plots, matrix plots, pie charts, and venn diagrams, providing enhanced visualization and categorization of data in publication suitable figures. Multiple clustering options such as k-Means, hierarchical, EigenValue, self organizing maps (SOM), random walk and principal components analysis (PCA) are included. Interactive views of clustered data include the cluster set view, dendrogram view, and similarity image view, allowing the user to identify specific patterns of gene regulation and isolate that data for further analyses. Data subsets can also be generated through various other filtering parameters and viewed individually as well as within the larger context of data. Finally, a genome browser is incorporated into versions 5.5 and higher that allows the user to import, display, and track data based on the DNA strand.
I have used ArrayAssist® in my lab to analyze and publish microarray data generated from several studies of immunodeficiency virus infection in both humans (HIV) and in non-human primate models (SIV). I have found the software to be relatively easy to use with a comfortable and clear user interface and attractive graphical output. The pulldown menus that outline the workflow for the user are especially helpful and I was able to learn to use multiple components of the program without the need for outside training. These workflows take the user step-by-step through defining experimental groupings, performing primary algorithm analysis and QC, significance analysis of changes in expression, clustering the significant data to reveal common patterns of regulation, exploring the biological annotation for significant genes, and mapping of those genes onto the genome within the integrated genome browser.