CleanEx Expression Data Analysis

1. Step-by-step expression pattern search
2. By class expression pattern search

CleanEx provides very powerful tools to extract expression measurements matrices from different datasets, and format them so that they can be directly imported in very powerfull expression data analysis tools, such as "R".
CleanEx offers anyway some quite handy and fast methods to compare gene expression levels in one single datasets, between datasets, or even across different datasets.
The first of these tools is a "step-by-step" method, which goes successively through different datasets, each time using the preceeding result to improve and refine the final set of differentially expressed genes.
The second one is more complex. Using the previously described method to extract heterogeneous data from different datasets, it generates two matrices representing two different biological conditions, and then compares the gene expression levels between the two pools.

Step-by-step expression pattern search

The step_by_step tool first generates a form for the selected dataset. From this form, the user can separate the experiments in two pools, usually representing two different conditions (for eample, the first pool could represent "prostate normal tissue", and the second could be "prostate cancer tissue"). One then selects the analysis to apply to these two experiment pools (over-expression in either the first or the second pool compared to the other one or co-expression levels in the two pools), and the number or percentage of features/genes to keep.
The comparison is currently based on the general mean difference ranking, where the mean expression is calculated for each gene and for each experiment pool, and the difference between the two pools'means for each gene is then ranked.
The following step displays the gene list according to the difference rank. The user can then select between two options :

1. Extract the promoter sequence in a fasta format for the shown over-expressed genes. This file can then be used for promoter analysis, for example from the SSA (Signal Search Analysis) online tool available at the Swiss Institute of Bioinformatics
2. Proceed to the next analysis step, by selecting a new comparable dataset, then generating the two comparison pools, and launch a new analysis. This new step will generate a gene ranking from the newly selected dataset, but it will show only the top genes which are common with the first analysis results.

By class expression pattern search

The MeSH-oriented data extraction and comparison module works on the same basis than the MeSH oriented data selection tools. It works as follows :

1. First, by walking down the MeSH categories, the user selects two pools of experiments, coming from different datasets, to compare. For example, one could compare prostate normal tissue (Prostate BUT NOT Neoplasms) versus prostate cancer tissue (Prostate AND Neoplasms BUT NOT Neoplasm Metastasis).
2. Once the experiments have been selected, the user stil can discard some data that he does not want to use for further analysis
3. Then, the program generates , for the two experiments pools, the three files, namely the matrix, the experiment and the feature files (see Data Extraction for details on the files format).
4. The next step is the analysis part. It uses the same rules as the step-by-step module, and produces a list of genes which are either :
   • Over-expressed in pool two compared with pool one
   • Under-expressed in pool two compared with pool one
   • Over- OR under-expressed in pool two compared with pool one

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