The Experimental design step allows you to specify or edit information about your experimental design, such as the primary factors studied, the subjects (for within-subject designs), blocks (for blocked designs), or any other variables that you might want to investigate. This information is used throughout the workflow to propose specific alignment setups, image display layouts, appropriate statistical tests and data analysis tools.

This is where you specify a design, edit a design, assign images to the various factor levels and variables, and verify if you have a consistent and balanced experimental design matrix.

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Confused about what experimental design, factors, factor levels, subjects and blocks are? Read our Experimental design guide for a brief introduction on theses concepts.

How to

Specify an experimental design

If you imported factor data

Importing factor data from an Excel file bypasses the experimental design wizard and automatically defines your factors in the Factor table. You can then immediately edit your design by specifying each factor’s type, setting level colors, and building or adjusting the experimental design matrix.

If you did not import factor data

If this is your first time working through the Experimental design step, a wizard will guide you in defining one of the more common experimental designs.

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If the pre-defined designs in the wizard do not fit your experiment, simply Skip this step. You can define experimental design variables later in the process.

First choose whether your experiment includes one (top row) or two (bottom row) primary factors.

Then click on the thumbnail of the applicable design:

• Choose the …with subjects option if your experiment involves multiple measurements from each subject, such as taking samples before, during, and after a treatment.
• Pick the option …with blocking when your experimental units – whether samples on a gel or combinations of gel and dye in DIGE experiments – are organized into homogeneous blocks prior to their random assignment to treatment groups. This approach is beneficial for multi-day 2D gel experiments conducted with several treatments. It helps control day-to-day variability by treating each day as a separate, matched block.
• Select the default option if your experiment does not incorporate a within-subject or blocked design.

Click OK to proceed to the next step.

Name your factors and factor levels

In the second screen of the wizard, name your factors by modifying the default names.

For each factor:

• Name each factor level by double clicking on the default name.
• Add factor levels by clicking on the green plus sign.
• Remove factor levels by clicking on the red cross of the factor level.
• Change the color of a factor level by double-clicking on its color box.
• Optionally, in the String column, add a string that identifies the factor level in the image name (specify whether this should be the File name or Alias in the Source field). This setup automatically assigns images with this string in their file name to the corresponding factor level.
• For each subject, specify the Level for subject, indicating within which level of the second factor the subject is nested.

All changes you make will automatically update the experimental design matrix displayed at the bottom of the screen. If all factor levels are identified by strings in the file names, the images are automatically placed in the matrix. If not, you will need to manually assign the images to their factor levels in the subsequent step.

When you are finished, click the Create button to finalize the design.

Edit your experimental design

Add or delete factors/variables

If you have previously defined factors, either through importing or using the wizard, they will appear as headers in the Factor table at the top left of the screen. If no factors have been defined, the table will only display the list of your images in an empty table.

You can define (additional) factors, variables, or other relevant information fields for investigation during analysis. To create a new factor, click on the green plus sign. Note that the software generically refers to primary factors, variables, and other information fields as “factor.”

Upon creating a new factor, the software will open a Properties window for you to define the factor’s attributes:

• Name of the factor.
• Type of the factor. The available types are:
  • Factor – Select this for primary factors that you want to investigate.
  • Blocking – Choose this for identifying groups, or blocks, in a blocked design. The levels correspond to the different blocks.
  • Subject – Opt for this to denote subjects in a within-subject design where multiple measurements per subject are taken. The levels represent different subjects.
  • Information – Use this for other experimental variables of interest, such as the technician running the gels.
• Levels: After setting up the factor levels, click on the color box next to each level to modify its color.

Note that you can also access the Properties window by clicking the Properties icon located behind the name of a selected factor.

Remove a factor by clicking on the red cross next to the factor’s name.

Assign images to new factor levels

When you create a factor and select the corresponding column, the thumbnails of all validated images in the project will appear in the Images area in the middle of the screen. To create a new factor level, you can select images (they will be highlighted in green) and drag their thumbnails into the new level… box in the Levels area at the bottom of the screen.

Once you dragged images into the new level… box, the default level name is highlighted so you can edit it.

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Select images by entering a specific string from their names in the Select box at the top of the screen. If you prefer to search using the original file names rather than aliases, check the Show original file names option. Once you drag the images to the new level… box, the search string will be suggested as the level name.

Alternatively, you can manually select images by clicking on their thumbnails in the Images area or on their names in the Factor table. To select multiple images, hold the Shift or Ctrl key.

Edit factor levels

To edit factor levels, the corresponding factor must be selected.

• Remove images from a factor level by dragging their thumbnails back to the Images area or to another level.
• Rename a factor level by clicking on its name in the Levels area.
• Delete a factor level by clicking its red cross in the Levels area.
• Change the color of a factor level by clicking the Properties icon for the selected factor, and then clicking the color box for the factor level.

Run the wizard again

You can create a new design by clicking the Wizard button.

Verify your experimental design matrix

The Experimental design view or matrix at the top right of the screen summarizes the main factors in your experiment: up to two primary factors, as well as the subject or blocking factor.

Layout and colors of the experimental design matrix

If your screen does not display an Experimental design matrix, or if you wish to modify its layout, adjust the layout settings for your factors: In the Factor table, click on the Change display icon located below each factor. You have options to Display vertically, Display horizontally, or set as Not displayed; the changes will reflect immediately in the matrix. To revert to the original layout designed using the wizard, click the Reset display to wizard choice icon in the Factor table.

The color coding in the Experimental design matrix corresponds to the color coding for the selected factor. To select a factor, simply click on its name in the Factor table.

Consistency of the experimental design matrix

The Experimental design matrix helps ensure that your design is consistent and balanced, meaning it should have a similar number of biological replicates for each treatment.

Designs incorporating a subject or blocking factor must be fully balanced: missing values are not permitted. For example, if observations post-treatment are missing for a subject, the experiment cannot be analyzed as a within-subject design using repeated measures ANOVA. Instead, the subject factor will be disregarded, and the experiment will be analyzed as a between-subject design using ordinary ANOVA.

Note that certain specialized statistical packages can adjust for unbalanced designs. After completing alignment, detection, and normalization, you may export your quantification data for analysis with such third-party software.

Experimental design used for the statistical analysis

The default analysis in the Results step will use the experimental design you specified using the wizard.

If you bypassed the wizard and manually defined your design, the default analysis in the Results step will employ the design as visualized in the Experimental design matrix, at the moment you advanced to the next step in the workflow.