Co-expression tool

This tool allows you to do co-expression analysis using either predefined or user-defined groups of micro array experiments.

HELP (If it is the first time that you are using our tool, please read this):

Step 1. INPUT: One or more genes or gene pairs can be given as input. Correlation coefficients can be calculated between the given genes, between the given genes and its co-expression neighbors and between the co-expression neighbors. One of the main features of this tool is that it allows to comprehensively visualize co-expression.


A. Predefined sets of microarray expression data:

  • AtGenExpress dataset
  • microarray compendium of about 500 experiments compiled from NASC, GEO and ArrayExpress oriented towards cell cycle, growth and development and the AtGenExpress datasets
  • microarray compendium of about 100 experiments compiled from GEO and AtGenExpress without bias towards particular types of experiments and after removal of very similar experiments
  • Subgroups of expression data compiled using our design type terms (see FAQ): abiotic stress, biotic stress, development, flower, genetic modification, hormone treatment, leaf, root, seed, stress (abiotic+biotic), whole plant
Expression data is processed using RMA from BioConductor and making use of the TAIR10 - v14 CDF downloaded from Brainarray.

B. You can temporarily (1 day) upload your personal microarray experiments and/or compile a user-defined set of microarray experiments. Experiments are annotated using ontology terms (MGED Ontology, Plant Environment Ontology, Plant Growth and Developmental Stages Ontology and Plant Structure Ontology).

Step 3. CALCULATION: Pearson correlation coefficients (parametric) or Spearman correlation coefficients (non-parametric) can be calculated.

Step 4. OUTPUT: The visualization is automatically generated through Cytoscape and can be the starting point for diverse analyses (e.g. delineating clusters of co-expressed genes using the Cytoscape plugin MCode). Alternatively, text output in tabular format is provided.

- View expression profiles of input genes
- Add localisation information
- Continue to PPI tool (add protein-protein interactions) or TF tool (add regulatory interactions)