Getting Started With Gel IQ
Step 1: Download Gel IQ
Browse to www.ludesi.com/free-tools/geliq and click on the free download button.
Step 2: Start Gel IQ
Navigate to the Gel IQ program in your menu by clicking on Start > All Programs > Ludesi AB > Gel IQ
Step 3: Select "Import data" option
On the Gel IQ start page, select the radio button for "Import data from an external project" to upload a completed 2D gel image analysis file.
Click the Import Project button.
Step 4: Import your analysis data
From the dropdown menu, select the 2D gel image analysis software you used for your analysis.
Depending on the software you selected, a hint will appear telling you how to export the analysis data in your software. If you are unsure, please consult the manual for the software in question. Generally speaking, you need to save the experiment including all gel images. This will usually result a compressed zip file.
Once you have saved your experiment, browse to the saved location on your computer and upload the file.
Click the Import button.
Step 5: Getting familiar and adjusting the settings if needed (optional)
The next page presents you with general information about the Gel IQ evaluation procedure.
We strongly advise to read this overview as well as the detailed correctness criteria definitions before starting the evaluation.
Click Next.
On the next page you have the option to adjust the evaluation settings. Generally speaking, this will impact the time you spend on the evaluation as well as the robustness of the score you will calculate.
| Number of spots to check: | This determines how many randomly sampled spots you will evaluate for spot detection correctness. The optimal number of spots is dependent on the total number of spots in the experiment, but as a rule of thumb you should evaluate a minimum of 100. The more spots you evaluate, the more robust the spot detection correctness score will be, but the more time it will take to perform the evaluation. Our experience has shown that 200 is a good trade-off between time and robustness. |
| Number of matchings to check: | This detemines how many randomly sampled match pairs you will evaluate. Again, the optimal number is dependent on the total number of spots in the experiment, but we recommend a minimum of 100, optimally 200. |
| Size of spot detection area: | This determines how big the randomly selected area in the gel is that you will evaluate. If you have a very dense spot pattern on your gels you may want to decrease this value. If you have very few spots in your gels you may want to increase this value. If you are unsure, simply accept the default setting. |
To prevent computers with low memory from slowing down too much during the evaluation procedure, you can check the box next to Limit image cache to 100 MB. Note, that this is generally not required.
Once you are happy with your settings, click the Save settings and start evaluation button.
Step 6: Mark all spots in the gel area
The first task of the evaluation is to mark everything within a randomly selected gel area that you would class to be a spot.
This step is important in order to judge if the software has been missing any spots and will be factored into the overall correctness calculation.
Note, this is not about which spots you would consider for picking, and thus this should not be influencing your selection criteria.
Simply click on all features you consider being protein spots.
You can adjust the contrast and brightness of the image using the sliders to the right.
You can also switch on color mode for better visualization.
The spot sensitivity window is purely a visual aid, to help you be consistent about how faint a spot can before you no longer class it as a spot. This is especially useful when you would like to compare results between different users. Note, that the spot sensitivity window will automatically update itself when you adjust either contrast, brightness, or color mode, ensuring consistency throughout the evaluation.
Step 7: Rate the spot detection
Gel IQ will present you with randomly chosen spots, which you will rate to be either correct or not by clicking on the relevant radio button. If you are unsure, you can simply skip the spot in question by clicking on the "Not sure" radio button.
Make sure that you are familiar with the correctness criteria and to stay consistent throughout the evaluation. A definition of correctness criteria can be found on the definitions page.
Continue rating spots until you will automatically jump to the matching correctness phase.
Step 8: Rate the pair matching
As a final step you will be presented with randomly selected pair matches, which you need to rate as being either correct or incorrect by clicking the relevant radio button. You also have the option to class incorrect matching as a spot detection error by clicking "Incorrect spot detection" radio button.
The reason behind rating only pair matches, is that rating matchings across the entire experiment would bias the correctness score towards experiments with low sample number. Consequently, correctness scores could not be comparable between experiments with either many or few gels. By focusing only on randomly chosen match pairs the correctness score stays independent of sample number.
Tip: hovering over any spot border in the image will light up the respective match pair, helping you to determine if the match pair you are evaluation is indeed correctly matched or not.
Step 9: Receiving your correctness score
Once all spots and match pairs have been evaluated you will be presented with the correctness score in form of the Combined Correctness percentage.
The individual spot detection correctness or pair matching correctness scores tell you if you need to focus your attention on either of the two. Remember, that image analysis errors propagate through your analysis, which is why the final Combined Correctness score has to be a product of spot detection and matching correctness.
Note, that in our experience it is nearly impossible to achieve 100% Combined Correctness in 2D gel image analysis.
The following scale can be used as a rough guideline to help you interpret your results:
80% - 100%: very high quality image analysis
70% - 79%: high quality image analysis
60% - 69%: good quality but you may be missing out on potentially interesting findings. If either spot detection correctness or matching correctness is particularly low, try to improve that step in a focused manner.
50% - 59%: low quality image analysis. nearly half of the spots of some kind of image analysis error.
1% - 49%: very low quality image analysis. the majority of spots are either incorrectly detected or matched.
