Fine-tuning analysis parameters

Here you can find additional information on the process of choosing the optimal parameters.

See also

One way to choose the parameters is to use the tools available on the GUI described in this section Tune parameters tab.

Before optmizing the parameters we need to identify the type of problem that we have. There are two types of problems:

  1. Too many spots detected (i.e., too many false positives)

  2. Too few spots detected (i.e, too few true positives)

Let’s start with problem number 1.

Too many spots detected

While there could be many reasons why SpotMAX is detecting too many spots, we can identify these main issues:

  1. Oversegmentation of the spots channel See Spots segmentation method parameter.

  2. Minimum spot size is too small See Spot (z, y, x) minimum dimensions (radius) parameter.

  3. Ineffective filtering with features See Features and thresholds for filtering true spots parameter.

  4. Oversegmentation of the reference channel Valid only when Keep only spots that are inside ref. channel mask is active)

Oversegmentation of the spots channel before spot detection

If you are having too many false positives the first thing to check is whether you have too many areas where we do not expect spots segmented using the method selected at the Spots segmentation method parameter.

To check if true, set up all the parameters and then click on the compute compute button beside the Spots segmentation method parameter. Inspect the results and if the problem is there you can try the following steps

Better pre-processing

  • Increase smoothing: try to increase Initial gaussian filter sigma parameter since it might help removing noise, hence reducing the area where SpotMAX will look for spots. Try values like 1.0, 2.0, or even 3.0 and beyond.

  • Activate or deactivate sharpening: try activating/deactivating Sharpen spots signal prior detection parameter

  • Activate aggreation: if you have multiple objects (e.g., cells) try activating Aggregate cells prior analysis parameter. This could help because thresholding on all the cells at once can help reducing the segmented areas where SpotMAX will look for spots.

Better spots segmentation method

This might sound trivial, but make sure that you are using the best Spots segmentation method. You can tune this in the Tune parameters tab or by visually inspecting the result of each one of the available methods.

Minimum spot size is too small

Another common issue for too many false positives is having a minimum spot size that is too small. This is the case when there are multiple detections within the same spot.

To fix this, increase Resolution multiplier in y- and x- direction and Spot minimum z-size (μm) parameters. You can visually tune this in the Tune parameters tab.

Ineffective filtering with features

Once you tried all of the above, it might be time to look into filtering valid spots using the features calculated by SpotMAX. You can set these at the Features and thresholds for filtering true spots parameter.

To better understand which feature to use, read their description in the section Single-spot features description.

Some of the most used features are the Effect size (vs. backgr.) and the Statistical test (vs. ref. ch.). For example, in the tutorial Count single mitochondrial DNA nucleoids and quantify mitochondrial network volume, we show that it is beneficial to filter those spots whose mean intensity is significantly higher than the same area in the reference channel.

On the other hand, if you want to get rid of dimmer spots (low signal-to-noise ratio (SNR)) any of the effect size described in the seciton Effect size (vs. backgr.) are good candidates, since the effect size is a measure of the SNR of the spot.

Another combination that we found working well, is to use an OR statement between global and local effect sizes. For example, you could filter spots whose global OR local Effect size (vs. backgr.) are higher than a specific value.

Tip

To understand what could be a good minimum effect size, run the analysis without filtering valid spots, load the results into the GUI and check what is the effect size of the spots you want to remove using the tools available in the Inspect and edit results.

Oversegmentation of the reference channel

If you have a reference channel it might be beneficial to use it. SpotMAX can automatically segment it and use it to filter valid spots.

Note

This applies only if you activate the Keep only spots that are inside ref. channel mask parameter.

However, oversegmentation can lead to keeping spots that are instead outside of the reference channel. Make sure that you are segmenting the reference channel correctly by testing with the compute compute button beside the Ref. channel segmentation method.

Too few spots detected

The reasons why SpotMAX does not detect all the true positives are essentially opposite to why it detects too many spots (explained above) and they are the followning:

  1. Undersegmentation of the spots channel See Spots segmentation method parameter.

  2. Minimum spot size is too large See Spot (z, y, x) minimum dimensions (radius) parameter.

  3. Too aggressive filtering with features See Features and thresholds for filtering true spots parameter.

  4. Undersegmentation of the reference channel Valid only when Keep only spots that are inside ref. channel mask is active)

Undersegmentation of the spots channel before spot detection

If you are having too many false positives the first thing to check is whether you have too many areas where we do not expect spots segmented using the method selected at the Spots segmentation method parameter.

To check if true, set up all the parameters and then click on the compute compute button beside the Spots segmentation method parameter. Inspect the results and if the problem is there you can try the following steps

Better pre-processing

  • Decrease smoothing: try to decrase Initial gaussian filter sigma parameter since the smoothing could be too aggressive resulting in dimmer spots being filtered out. Try also values below 1.0, like 0.75 or 0.5.

  • Activate or deactivate sharpening: try activating/deactivating Sharpen spots signal prior detection parameter

  • Deactivate aggreation: if you have multiple objects (e.g., cells) try deactivating Aggregate cells prior analysis parameter. This could help especially if you have large variation of the signal intensities between different cells.

  • Activate removal of hot pixels: try activating/deactivating Remove hot pixels parameter

Better spots segmentation method

See above Better spots segmentation method.

Minimum spot size is too large

Another common issue for not enough true positives is having a minimum spot size that is too large. This can lead to detecting a single spot where there are two or more, especially when they are very close to each other.

To fix this, decrease Resolution multiplier in y- and x- direction and Spot minimum z-size (μm) parameters. You can visually tune this in the Tune parameters tab.

Too aggressive filtering with features

If you are using features with the paramter Features and thresholds for filtering true spots, make sure that you are not removing too many spots.

To better understand which feature to use, read their description in the section Single-spot features description.

Some of the most used features are the Effect size (vs. backgr.) and the Statistical test (vs. ref. ch.). For example, in the tutorial Count single mitochondrial DNA nucleoids and quantify mitochondrial network volume, we show that it is beneficial to filter those spots whose mean intensity is significantly higher than the same area in the reference channel. However, if we choose a p-value that is too low we would remove what are instead true spots.

On the other hand, if you are getting rid of dimmer spots using the Effect size (vs. backgr.) try reducing the minimum allowed.

Tip

To understand what could be a good minimum effect size, run the analysis without filtering valid spots, load the results into the GUI and check what is the effect size of the spots you want to remove using the tools available in the Inspect and edit results.

Undersegmentation of the reference channel

If you have a reference channel it might be beneficial to use it. SpotMAX can automatically segment it and use it to filter valid spots.

Note

This applies only if you activate the Keep only spots that are inside ref. channel mask parameter.

However, undersegmentation can lead to removing spots that are inside the reference channel. Make sure that you are segmenting the reference channel correctly by testing with the compute compute button beside the Ref. channel segmentation method.

Nothing works

If you tried many combinations of parameters and nothing seem to work there are three options:

  1. Use external software for some of the analysis steps

  2. Train SpotMAX AI on your data

  3. Submit your case with some sample data

Use external software for some of the analysis steps

Some of the analysis steps within SpotMAX can be replaced with results you obtain with other software. For example, you could segment the spots or the reference channel with ilastik, CellProfiler, or TrackMate to cite a few, save the results to a TIFF file and provide this to SpotMAX at the parameters Spots channel segmentation end name and Ref. channel segmentation end name. If you do this, SpotMAX will not perform these steps and will instead use your external TIFF file.

Train SpotMAX AI on your data

If you have some experience with Python (and ideally access to a GPU) you can easily train the SpotMAX neural network on your data. Few manually annotated images could actually make a big difference.

See this repository for instructions on how to train the model on your data: SpotMAX AI.

Submit your case with some sample data

Feel free to submit your case with some sample data and the parameters you tried so far by opening an issue on our GitHub page or by sending me an email at .

Until next time!