There are 4 basic steps to get SEA running:

• Upload data into your work space at the Upload Data tab
• Select the algorithm you want to use
• Feed the input form with data and launch the job
• Visualize your results

1. You can work anonymously or by registering on “Register”.
2. Go to the Upload tab and upload your data. Two data files are required, Expression and Covariates. Click here to learn more on file formats.
3. You can also create projects to organize your data.

You can see details of these steps animated:

1. Once data is in your work space, go to the maSigPro tab and select your expression dataset and covariates. The covariates files is parsed and you are prompted to select quantitative factor, qualitative factor and control group (go to maSigPro help for more details of the algorithm).
2. Your job name appears at the Job list panel in red. Once it is completed, the name turns green.
3. Click on the job name to go to the results page.
4. You obtain lists of differentially expressed genes for each series comparison, i.e. ControlvsCold, ControlvsHeat, etc. You can download these files.
5. To visualize maSigPro results send these to the maSigVisualization module by clicking on the green indication.
6. Load maSigPro results at the visualization form and select a comparing series to see (i.e. ControlvsCold).
7. Choose a clustering method of split significant genes into groups of similar profile, and the number of clusters.
8. Name your job, launch it and wait until the process in complete.
9. You can visit results again by clicking on the green job name.
10. You obtain one graph per cluster displaying the mean expression profile of the clusters members in all experimental series. Also the name of the genes classified in each cluster is provided for download.

You can also see this animated:

1. Once data is in your work space, go to the ASCA-genes tab and select your expression dataset and covariates. You can run the program with the default parameters or change them. (Go to ASCA-genes help for more details of the algorithm).
2. Your job name appears at the Job list panel in red. Once it is completed, the name turns green.
3. Click on the job name to go to the results page.
4. The first result is the information about the variability associated to each factor in our data and the percentage of the variation explained with the ASCA-model.
5. The second output are text files with lists of genes associated to each ASCA-submodel.
6. SPE-leverage plots are useful to visualize the quantity of genes of interest.
7. Finally, trajectory plots show the general trends identified by the ASCA submodels.

You can also see this animated:

1. Once data is in your work space, go to the ASCA-functional tab and select your expression dataset and covariates. You can run the program with the default parameters or change them. (Go to ASCA-functional help for more details of the algorithm).
2. You can choose here the organism of your study to obtain the annotations.
3. If the species you are working with is not in the SEA-database you can select the annotations previously uploaded.
4. Your job name appears at the Job list panel in red. Once it is completed, the name turns green.
5. Click on the job name to go to the results page.
6. As in ASCA-genes, the first result is the information about the variability associated to each factor in our data and the percentage of the variation explained with the ASCA-model.
7. Secondly, trajectory plots show the general trends identified by the ASCA submodels.
8. A table for each principal component (PC) is showed with the Fatiscan results.
9. In these tables Functional Categories can be sorted by the adjusted p-values.

You can also see this animated: