Dymension FAQs
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FAQ 1: How does background subtraction work and can we view it?
Background correction definition
This works by subtracting a slowly-varying background image (red line in the profile view, below) from the Noise-filtered image (which is always greater than or equal to the background) - thereby leaving just the spots. Two parameters control the process:
the Radius fraction (a percentage of the image size) and the Intensity fraction (expressed as a percentage of the image’s dynamic range). You should find that the default values are suitable for most images. Should you need to adjust them, however: Set the Radius fraction such that the radius is much bigger than any spot but smaller than the scale of any background variations. If you set it too large some smaller-scale background variations may be left; too small and you may remove part of a large spot. Increase the Intensity fraction to remove background variations of higher intensity. Decrease it if too much of a spot is being removed.
Viewing the line profile with background correction
In the results table on the right hand side select the Line Profile tab.
Perform a mouse right click on a replicate image and select Raw Image from the mouse right click context menu.
The red line represents the background that will be removed from the image.
Adjusting Background correction parameters
To access these parameters, mouse right click on the same replicate gel image and select Configure Background Correction for this gel. These values are percentages.
Viewing Raw Image, Noise Filtered Image, Background Image and Noise Filtered/Background corrected image in Dymension
Each of these views can be displayed by selection from the mouse right click context menu when performed on a replicate image. With Control 1S in the main pane, select Raw Image from the mouse right click context menu and zoom into the area shown below. Near the top you will notice some speckling (noise) which is more clearly seen as spikes on spots in the 3D view.
Now view the Noise filtered image and the Noise filtered/background corrected image.
The background can also be viewed – this is what is subtracted from the final image.
FAQ 2: How well does background subtraction cope with poor gels with varying background levels?
Background correction is based on two parameters as described in detail above. The background level estimated follows the large-scale, slow variations of intensity but omits the small-scale, rapid variations that are due to spots. This is clearly seen by choosing the option View background.
FAQ 3: How does normalisation work?
Normalisation allows you to normalise the volume of the spots in your gels, allowing you to accurately compare the measurements of spots in different gels. There are two methods in Dymension:
Total spot volume normalisation
The raw volume of each spot is divided by the total raw volume of all the spots on the replicate image and multiplied by 100 to give a normalised volume percentage and this value is displayed in the Results table.
Selected spot normalisation
Each gel may be spiked with a known amount of a marker protein and therefore the resulting protein spot will be a consensus spot – it will appear in all the replicates and all the samples. Select this spot in any of the images and from the Normalisation menu choose Selected Spot. You will then be asked to enter a value and this can be any value, for example, 1000. The numbers for normalised volumes displayed into the Results table is calculated as follows: Spot raw volume / selected spot raw volume value x assigned value for selected spot. Thus the reported volume of each spot is scaled by the same multiplier, which is chosen such that the reported volume for the selected spot is the value assigned. This method may be the preferred choice if one of the gels has a high level of contamination as this contamination will bias the normalised volume calculations of the ‘real spots’ if total spot volume normalisation is used.
FAQ 4: What are the spot detection parameters?
When the Dymension spot analyser scans an image, it draws non-overlapping boundaries around ‘patches’ of pixels that belong to each spot. These patches are used as the basis for later steps in the spot analysis. The identification of the patches and later analysis is controlled by a number of parameters that you can change using the Spot Detector Settings command. You should not normally need to change these parameters. The meaning and effect of each parameter is documented in the Help and the Manual, and brief explanations are provided as ‘tooltips’ on the dialog used to set them.
FAQ 5: Where are the spot boundaries drawn?
The spot boundaries are drawn at borderlines between adjacent spots, or at borderlines between a spot and the background.
FAQ 6: How repeatable is spot detection? (i.e. let’s say we put the same image into the software five times, but rotated slightly each time, would we get the same volume for every spot each time, or would there be some error?)
Spot detection, consensus spot generation and matching are reproducible and robust. A 'perfect' modification of the images (such as a shift, transpose or reflection) that leaves the pixel values unmodified (though in different positions) will give almost identical results (RMS ratiometric volume variation of 1.5x10-5 %). However, rotation (digitally or otherwise) will inevitably change some of the pixel values and give rise to some variation in results; in this case, we observe (averaged over all spots):
- RMS positional variations of 0.35 pixels (0.035% of image size);
- RMS ratiometric volume variation of 1.9%
The spot detection algorithm is also extremely linear, the volumes reflecting changes of intensity scale almost perfectly (RMS ratiometric volume variation of 1.5x10-5 % as the intensity varies over six orders of magnitude). This is important, as non-linearity is totally undesirable.
FAQ 7: How accurate is warping, and how well does it cope with pl shifts?
In Dymension automatic warping is able to cope with the normal variations encountered with virtually all gels. However, you may occasionally come across a gel that causes a problem, such as a gel with a tear in it or an area of the gel where the acrylamide has not polymerised correctly. In such cases, manual warping can be performed on the problem areas. If the first dimension (pI) for replicate gels is run for different time periods there will be an associated pI shift of one gel relative to the other. However the automatic warping procedure will be able to cope with such a shift because it matches patterns between the images.
FAQ 8: Will automatic warping fail on gels that are significantly different, sharing only a few spots?
Automatic warping matches patterns of spots between images. It will be more successful for pairs of images that contain more common patterns.
FAQ 9: Is it possible to choose the warping master within a set of replicates, or is it always gel one?
Yes you can change the warping reference gel.
FAQ 10: How does automatic warping adapt if you have a large number of gels in a sample and they differ more down the tree?
In general the automatic warping system is so successful that, provided the gels all belong to the same experiment and have a reasonable level of common pattern, you will have few problems. However, all warping is determined between a ‘reference’ gel and the others. It may be appropriate to select this reference carefully so that it shares the maximum amount of information with the other gels. Alternatively, in these rare cases it may be better to edit the warp manually.
FAQ 11: How often do we anticipate manual warping being necessary?
This will entirely depend on the gels being analysed but we estimate less than 5%.
FAQ 12: How accurate is matching - a percentage?
Impossible to answer such a question as there is no matching metric.
FAQ 13: How does matching accuracy differ as you add more gels to a sample?
This will depend on the replicate gel variation, but in general overall accuracy should improve as the number of replicates increases.
FAQ 14: Can you seed a gel prior to matching?
There is no need for seed matching as the warping drives the matching process.
FAQ 15: What ‘stats’ does Dymension have – explain t-test?
Student t-test
We use this test for comparing the means of two treatments, even if they have different numbers of replicates. In simple terms, the t-test compares the actual difference between two means in relation to the variation in the data (expressed as the standard deviation of the difference between the means). What does this mean in "real" terms? Statistical tests allow us to make statements with a degree of precision, but cannot actually prove or disprove anything. A significant result at the 95% probability level tells us that our data are good enough to support a conclusion with 95% confidence (but there is a 1 in 20 chance of being wrong). In biological work we accept this level of significance as being reasonable. A student’s t-test is used for populations where the sample size is small.
The P values range from 0 to 1 and the closer the value to 1 the more insignificant the match is deemed. If the results of the Dymension analysis indicate high P values then the user needs to take care when interpreting the data and/or repeat the experiment with more replicate gels.
We will be implementing additional statistical tests as the user will have his or her own preference.
FAQ 16: Can we pause the calculations during editing?
During spot editing and filtering the regeneration of consensus and matched spots can be suspended if the user so chooses. This can speed up the process if a large number of changes is undertaken.
FAQ 17: Can we add other metrics to the Spot filtering parameters?
This will depend on feedback from users so is entirely possible.
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