## Importing Agilent 1-color data

All files should be stored in a single folder that can then be imported to Chipster. Select File->Import files, and browse to the correct folder that contains the dataset. Select the files you want to import. You can select several files either by pressing down the Control (Ctrl) key or the Shift key.

In the next window you define whether your files should be imported directly or by using the Import tool. Typically you don't need to change anything, since the column Action lists all files as 'Use Import tool'. This means that you need to describe the datafile to the system once, and it loads all the other files in similarly by default. To get to define the file, click on OK.

The Import tool opens. First, you need to define the rows that are the header information. Click on the Mark header -button on the top, and paint the rows you want to mark as header. Next, click on the Mark title row -button, and click on the title row:

Clicking Next opens the second step of the Import tool, where you define the columns of your data file to be used in Chipster. You have to define the following columns:

- Identifier (ProbeName)
- Sample (rMeanSignal)
- Sample background (rBGMedianSignal)

(If you had green signal instead of red signal, you would mark the gMeanSignal column as sample and the gBGMedianSignal column as sample backgound.)
You can also mark columns Annotation (ControlType). Using the Annotation column allows you to remove the control probes from your dataset during the normalization step (by setting the 'remove control probes' parameter to yes). Please note that if you remove the control probes, you cannot use the data anymore for gene set test.

To define the columns, select the meaning by clicking on the button on the top of the page, and then click on one of the columns (not the greyed header).

To conclude the import, click on the Finish-button. After the data has been imported, it is displayed under Datasets and in the Workflow view:

Now the data has been successfully imported, and you should normalize it. You can also run some quality control checks on the raw data.