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SameSpots – Technical Specification

Technical Specification

SameSpots allows you to quickly and reliably analyse single stain, multiple stained and DIGE 2D gel electrophoresis images for differential expression analysis. All common image formats are supported including .tiff, .img, .png, .gel and .mel.

PC Specifications

  • Windows Vista, Windows 7, 8 and 10. Performance depends on processing power.
  • SameSpots supports multi-core CPUs and 64bit versions of Windows, which are recommended for maximum performance.
Start your analysis the right way, with a choice of set ups or QC tools

SameSpots analysis features

What is Image QC?



Images are quality checked when uploaded to the software, any issues/notes are highlighted for you before any further analysis is attempted.

What problem does it solve?

Poor Image quality can make analysis of your gels difficult and can reduce the accuracy of quantification of results. It can also increase subjectivity of the analysis.

What checks does Image QC do?

Image QC looks at and checks that images:

  • Are greyscale
  • Have a high bit-depth
  • Have a high dynamic range
  • Are free from saturation
  • Have not been compressed or edited prior to analysis.

Can images be edited in the software?

Yes, tools are provided to complete simple edits to the images without changing the originals. Simple edits include:

  • Flip images horizontally and vertically
  • Rotate images 90 degrees clockwise and anti-clockwise
  • Invert image
  • Crop image 

Image QC




Results of each quality check are listed for each image.

Image editing tools





Tools are available within the software to edit images without changing the originals.

What is Alignment?



Alignment takes two images and overlays them in the same co-ordinate space so that a spot on image A will be in the same location as the matching spot on image B.

What problem does it solve?

Alignment makes it possible to accurately compare images by removing any positional variation introduced during the gel running and imaging processes.

Why is alignment better than spot matching techniques?

Alignment makes it possible to overlay spots from each image, allowing one spot pattern to be created. Using separate spot detection and then matching spots between images is very inefficient.

Do I need to align DiGE images?

For low MW proteins there may be a shift due to the different dye so all DiGE images are first aligned to the Cy2 internal standard image, usually with very small changes, and then the Cy2 images are aligned together.

How does Alignment work?

Image alignment produces a spatial transform at the pixel level. The transform maps every point on an image to the appropriate point on its reference image. The transform is calculated so as to minimize the length of the warp vectors; interpolation is used to calculate the appropriate positional transform between vectors. This method allows for much more variability than using a rigid grid.

An alignment vector associates a point on one image with the corresponding point on the second image. The alignment process uses information from many alignment vectors to create a mapping from one image to the other. The vectors can be automatically generated or manually added. For each pixel in the image, the mapping calculates the corresponding position in the aligned image.  

 image unaligned


image aligned


The reference image is the target image to which all other images are aligned to. Top image shows the reference image (magenta) and target image (green) unaligned, bottom image shows the images aligned.


What does Automatic Spot Detection do?

It takes a number of gel images and using advanced imaging techniques it creates a series of spot shapes which are used to create the spot pattern (the spot map) that is used for all the images in the experiment.

Which images are used to calculate the spots?

By default all of the images in the experiment are used as a basis for the spot detection. However before you detect the spots you can optionally deselect some images if you do not wish to use them to contribute to the spot pattern.

How us the spot volume calculated and why are spot volumes normalised?

The Volume is the integrated intensity of the spot with any calculated background subtracted. (This value is used in the calculation of Normalised volume). The Raw spot volume is the sum of the Volume + Background.

Normalisation is required in proteomics experiments to calibrate data between different sample runs. This corrects for systematic experimental variation when running samples (for example, differences in sample loading). The effect of such systematic errors can be corrected by a unique gain factor for each sample – a scalar multiple that is applied to each feature abundance measurement.

What is background subtraction and how is it calculated?

Background subtraction is necessary to accurately quantify the protein material in a spot.  It corrects for the intensity level of the scanner bed, for example, and staining variations, etc, across the gel.

In SameSpots, the background level for a spot is equal to the lowest intensity value of the image pixels outside the spot’s outline. The background level is then subtracted from the intensity value of every pixel inside the spot outline to determine the volume of the protein material in the spot.

Spot Montage


The spot pattern is co-detected on all images, this produces a single spot pattern which is the same on all images.

What statistical tests can be completed in SameSpots?

  • P-values (standard ANOVA, repeated measures ANOVA and two-way ANOVA)
  • q-values
  • Principle component analysis
  • Correlation analysis
  • Power analysis

What is Principle Components Analysis (PCA)?

Principal components analysis calculates a linear projection of the data such that the first axis (Principal component 1) will show the largest variance that could be represented by the transform (i.e. a linear combination of translation, rotation, scaling). The second principal component is the best direction orthogonal to the first axis that accounts for the next largest chunk of the variance in the data. This is an ‘unsupervised’ technique (i.e. it does not use any knowledge of the grouping of the data and as such is useful in finding if your data has the groupings you expect or if there are outliers in your data.

What is Correlation Analysis?

Correlation analysis is a technique which allows us to explore similarities between expression profiles of protein spots across multiple gels. Spots which vary in a similar way will have a high correlation value (with 1 indicating an exact match); spots which show opposing behaviour will have a large negative correlation value (with -1 indicating a perfect mismatch); spots which are not related will have a correlation value close to zero.

Correlation analysis results can be plotted as a dendrogram which shows hierarchical grouping of spots based on their correlation values.

What are p-values?

The object of differential 2D expression analysis is to find those spots which show expression difference between groups, thereby signifying that they may be involved in some biological process of interest to the researcher. Due to chance, there will always be some difference in expression between groups. However, it is the size of this difference in comparison to the variance (i.e. the range over which expression values fall) that will tell us if this expression difference is significant or not.

What are q-values, and why are they important?

Q-values are the name given to adjusted p-values. The method is used to account for the multiple testing problem and uses an optimised FDR approach to solve this problem.

What is Statistical Power, and why is it important?

ANOVA p-value gives you the probability that the difference you are seeing is not a real difference but just happened by chance.  But this is really only half the story.  Power gives you the probability that you’d be able to see the difference if there was one.  You need both values to complete the picture

Stats mode


All statistical analyses can be exported in high resolution using the clip gallery feature of SameSpots for use in reports and presentations.




PCA Graph


PCA helps to identify any possible outliers in the data.


Can you create spot picking files and choose which spots to include?



Yes you can. Spot Picking occurs near the end of the process and allows you to set up the spot picking to pick off either one of the gels in the experiment or from an additional gel run for the purpose of picking off. Using tags from the experiment you can choose which spots to pick to create the spot list.

Which Picking Robots does SameSpots support?

We produce picking lists for

  • GelPix
  • ProPic
  • Ettan
  • Proteineer
  • Generic Picking list in mm or x,y coordinates.

What is the Clip Gallery?

At every stage of the SameSpots workflow the images and data tables can be added to the Clip Gallery. The clip gallery makes it easier to capture images and tables from the software for the production of posters, publications and presentations. Images are saved as high resolution .png files (300 dpi).

The saved images are retained as part of the experiment and are stored accordingly.  This facility allows you to capture (high resolution) images that can be used in the development of specific reports and/or used as part of the process of publishing your experimental findings.

Can you export the Spot Measurements?

Yes you can export to a CSV file (Comma Separated Values) that can be opened in Excel or other spreadsheet applications.

Can I create an experiment report?

At the end of an experiment you can create an html report of the results. You can choose what to display including which spots to report on

There are 4 main sections to the report with multiple subsections:

  • Reference image
  • Experiment design
  • Spot table
  • Spot details
Spot Picking





Spot picking files can be created for use with the most common picking robots.


Clip gallery


Any images, graphs and tables can be exported from the analysis  using clip gallery, images can be dropped into reports for publication.




Generic reports, using pre-set options can be easily created and shared.


Can I check my image quality?


When adding images to the experiment they are checked for quality and any issues/notes are highlighted for you.

Can I edit my images?


If your images require minor changes (flipping etc.) then you can edit them within the software without changing the original images.

Can I check my gel running quality?


Our gel quality tool SpotCheck helps you objectively validate that your gel running meets your lab’s quality standards. It also highlights some common problems that occur during image capture.

Can I account for gel distortions?


Alignment makes it possible to accurately compare images by removing the positional variation introduced during the electrophoresis and imaging process.

Can I compare DIGE gels?


DIGE experiments can be setup before alignment and the software automatically deals with all measurement calculations.

How can I account for different Gel loading?

This is accounted for by using normalisation. Normalisation is required in proteomics experiments to calibrate data between different sample runs. This corrects for systematic experimental variation when running samples (for example, differences in sample loading).

The effect of such systematic errors can be corrected by a unique gain factor for each sample – a scalar multiple that is applied to each feature abundance measurement.

How can I check the spots created?

There are a number of modes available to check the results of the spot detection. Spots can be edited and/or filtered in the Filtering and Review Spots modes. Review Spots also allows you to review all the detected spots and look at their expression profiles.

What can I use to decide interesting spots?

There are many features to allow to decide on interesting spots including

  • Fold changes
  • P-values and Q-values
  • Correlation Analysis
  • Expression Profiles

How can I highlight interesting spots?

You can sort the spots on many criteria and use our “tagging” feature to group spots of interest together.

How can I pick interesting spots?

Using our picking method allows you to align a picking gel to the gels in the experiment. You can then produce picking lists for the spots you wish to mass spec.

Can I calculate pI/MW?

Yes. If one of the gels has a molecular weight marker on it and/or a pI strip or spots with known values then you can calculate the MW and pI for all spots.

Can I import interesting mass spectrometry data?

Yes. Results from Mass Spec, including MASCOT searches, can be imported into the program so you can look at and review this information in the program.

Can I export results?

All the calculations, spot numbers, images and output can be exported from the program. An HTML report can also be produced to show the results from the entire experiment.

Do you have any other questions?