Arabidopsis Genetic Uniqueness and Redundancy Revealer

Arabidopsis Genetic Uniqueness and Redundancy Revealer is a tool for identifying and visualizing outliers in an expression data set across a multitude of experimental conditions. You can upload the results of your own microarray experiments or use this tool in conjunction with Expression Browser if you wish to query one of our built-in datasets. This tool enables you to find out in which samples your genes of interest are uniquely expressed. Powered by Python and SciPy.

  1. Upload the dataset which you want to query. Keep in mind that in order to identify in which samples your gene(s) of interest are uniquely expressed you cannot have more genes than samples. Follow the guidelines below:
    • The expression data file you are uploading or pasting in must be tab-delimited and follow the prescribed format. See the illustrated sample or click here to download a sample data file.
    • For this program to predict uniquely expressed genes correctly, you need to ensure that samples are averaged over replicates.
    • If you are querying ratiometric data, you need to ensure the data is log transformed either before being uploaded or by indicating so in the checkbox below.


    2. Paste in your data set (ensure the format of your file is the same as the sample file in the gray box on the left). 
    UNIQUE_ID<tab>NAME<tab>sample1<tab>sample2<tab>sample3
At1g49270<tab>annotation<tab>222.4<tab>1.0 <tab>139.7
At3g18810<tab>annotation<tab>701.3<tab>43.6<tab>906.9
At4g34440<tab>annotation<tab>260.4<tab>79.9<tab>85.71
At2g18470<tab>annotation<tab>571.5<tab>10.9<tab>251.8
At1g18470<tab>annotation<tab>571.5<tab>10.9<tab>251.8
...

    Or

    Browse for a file containing your data set.



  2. Identify and show positive outliers negative outliers.

  3. Apply global normalization (better suited to large gene families) local normalization (better suited to small gene families).

  4. Indicate threshold range for determining outliers.

  5. Log transform input values (useful if you are putting in ratios)? Input is already log transformed (important for accurate results)?

  6. Reveal redundancy (i.e. show similar gene expression patterns in a sample-to-sample fashion)?     Show up to redundant genes.

  7. Submit or reset to restart.