In this page, we have made available the input data files and the results of our analysis 11,760 microarray experiments on the model plant Arabidopsis thaliana drawn from public repositories. We have categorized these experiments under seven different tissue types and five different experimental conditions. Using this data, we generate genome scale networks of Arabidopsis using three different methods - mutual information, Pearson correlation and Gaussian graphical modeling.
Description of Data Files
All the twelve classified datasets (7 tissues ; 5 conditions) are can be downloaded from here . The dataset files are of the "exp" format. exp is a plain text format. It has (No. of experiments + 2) columns and (No. of genes + 3) rows. The first two columns contain the probe set name and the locus id (Arabidopsis Genome Identifier or AGI). From the third column onwards, each column contains the expression values corresponding to an experiment.
The rows are organized as follows: First row is a header; second and third rows are descriptions. Starting from the fourth row, each row is a vector to the expression values corresponding to a gene. The first two entries in each row are the probe id and the AGI (of the form ATXGXXXX) are respectively. The locus id value can use used to select the rows corresponding to the genes of interest.
Union Networks for all the three methods - Pearson, Mutual Information and Gaussian Graphical Models are available for download here . The network files are available in two format ".cys", which can be opened with Cytoscape 3 and ".sif" files, a text format that can be imported into other network analysis software.
NASC's Arabidopsis Microarray database has been shut down and hence the meta data is no longer available. Attached in this page is nasc.tar.gz, which contains all a snapshot of the metadata for all the NASC experiments used in our paper.
Sriram P. Chockalingam, Maneesha Aluru, Hongqing Guo, Yanhai Yin, and Srinivas Aluru. Reverse engineering gene networks: A comparative study at genome-scale. Under Review.