ArrayStar® v2.1                            

Microarray Gene Expression Analysis

ArrayStar v2.1 provides a wide range of analytical and visualization tools that will assist researchers in interpreting results obtained from gene expression runs. Data files from Affymetrix, Illumina and Roche NimbleGen can be directly read. Importation of .txt files give users the capability of analyzing spotted array runs and data from other sources.

Several NEW Features have been added to v.2.1 that simplify the process of gene expression data analysis. Foremost of the Features is the expansion of the types of data that can be used with the system. As in the earlier version, Normalized data can be used. Version 2.1 has added a Normalization feature that permits users to easily use either RMA, PLIER ( for use with Affymetrix data) or Averaging Summarization (simple normalization).

As shown below, the selection of a Normalization method requires only the selection of the method itself from the drop down window. Preprocessing that can be selected varies depending on the normalization method selected.

Wizard Showing Normalization Options (Click page to make image larger)


ArrayStar v2.1 software enables researchers to perform a wide range of analyses on their data. Multi-functional scatter plots can be generated that allow the user to easily select groups of genes for analysis. The image shown depicts a group of selected genes (white) that have been selected from the overall experiment viewed on a scatter plot.

Click here for more information about the Scatter Plot diagram.

Scatter Plot Diagram (Click on picture for larger image)



Visualizations to assist in Gene Expression level analysis


For analysis across a series of experiments, such as a time series or a related set of conditions, two powerful clustering algorithms are available in ArrayStar: Hierarchical Clustering and k-Means Clustering.

The Hierarchical Clustering method groups data points by clustering them one-by-one into ever-growing groups. After grouping all of the data points, the resulting clusters are displayed in the Heat Map. Details on Heat Maps are below.

Heat Maps
Heat Maps illustrate expression levels of the genes across a number of experiments.

  • Genes can be selected within the Heat Map for additional analysis.
  • The Gene Tree to the left of the Heat Map reveals a sub-tree of genes. Clicking on branches reveals cluster information.
  • Gene Ontology information is easy to obtain by passing the cursor over any gene name or Heat Map location.
  • Selection of genes or gene clusters in the Heat Map is shown in the expression level histogram in gray and illustrates relative expression levels
    • Click here for more information about the Heat Map diagram.

    Heat Map (Click on picture for larger image)


    The k-Means Clustering method differs from the Heat Map method since it groups data points by partitioning them into a fixed number of arbitrary groups and then repeatedly refining the groups. The Line Graph Thumbnail view, shown below, is best used to display the k-Means Clustering.  Click here for more information on k-Means Clustering.

    Expression Level Changes – Line Graphs and Thumbnail Graphs


    Line Graphs
    ArrayStar allows users to easily visualize the expression level changes seen in individual genes over the course of the experiment through the use of Line Graphs. Any gene can be highlighted by passing cursor over it to generate the graphical representation of its expression. Analytical Features of this include:

  • Selection of desired gene reveals ontology information
  • View comparisons of different gene expression levels
    • Click here for additional information on Line Graph diagram.

    Line Graph (Click on picture for larger image)



    Thumbnail Line Graph


    ArrayStar’s Line Graph Thumbnails view displays a series of Line Graphs generated from a clustering. Each individual Line Graph shows a visualization of the data contained within one cluster.

    Expression levels are plotted vertically along the Y-axis, while the X-axis position for each point is determined by the experiment to which it belongs. Mouse-over a vertical gridline to view the experiment name.

    Click here for additional information on the Thumbnail Line Graph diagram.

    Thumbnail Line Graphs (Click on picture for larger image)


    Data Analysis

    ArrayStar provides a wide range of statistical analysis tools and techniques that can be used to assist researchers in their gene selection studies. In addition, it filtering capabilities permit users to quickly examine and re-examine data based on different experimental assumptions and beliefs. Additional detail can be obtained by clicking here.


    Gene Identification

    ArrayStar also offers an Advanced Filtering tool, which allows users to identify genes of interest based on a number of criteria, including gene annotations, expression level, gene classification/ontology, fold change, and statistical values. Results can subsequently be selected in all views, exported, or saved as a gene set for further analysis.

    Filtered Ontology Images (Click on picture for larger image)



    The Gene Table view contains detailed information for every gene in your project, including both the expression data (e.g. signal intensities and fold change values) as well as any annotations that are available from imported sources, such as the gene name(s) and gene ontology. Some annotations have special features, allowing you to hover over a term for more information, or click on a hot link to view detailed information online. Any gene subsets being investigated are indicated in the Gene Table, allowing you convenient access to key tabular information for the genes being visualized by other tools in the package.

    Gene Table (Click on picture for larger image)


    The data in the Gene Table may be searched, printed, copied as a text file, or exported as a tab-delimited or comma-delimited text file.