This is the Data Center, where you can upload and display your data.
Please download our test dataset to get familiar with the data formats and continue the tutorial:

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Great! Now that you've downloaded the data, extract it to your hard drive. Pick one of the two test data folders. It contains two files: coordinates.csv and signatures.csv.

Let's first have a look at coordinates.csv: The comma-seperated-value file contains the x,y-coordinates obtained from your spatial sequencing experiment. It is formatted as follows:

The file contains three columns: The first one contains the molecule label, the other two contain x,y-coordinates per molecule. The column names do not matter, but you need to keep their order if you work with your own data.

It's now time to upload the data! Click on the 'Coordinates' button on the right and select the coordinates.csv for the upload.

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SSAM-lite lets you interactively investigate your data. You can navigate through the plots as follows:

• right drag: Zoom in
• shift+right drag: Drag
• double click: Original view
• click legend: Switch on/off
• double click legend: Switch all

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The signatures.csv is formated as follows:
The table contains cell type-wise expression expectations, with columns showing mRNA class and rows showing cell types.

Click on 'Signatures' to upload the signatures.csv and inspect the data.

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In the Parameters section you can hand-tune the most important SSAM parameters.

SSAM-lite provides a preview function that processes small tissue sub-sets on the fly to enable parameter optimization in real time.
Click 'Use preview generator for parameter search' to enable it!

The parameters to optimize are:

• Pixel width: The size of our discrete analysis and output area in pixels.
• KDE kernel bandwidth: The 'smoothing factor' applied to the mRNA locations.
• Expression threshold: Threshold for discarding areas with low expression signal.

We'll now have a look at each parameter in detail.

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The coordinates preview on the left lets you chose the subset of the tissue to calculate the preview on - simply click on the map to change it (but be patient as it might take a moment for the adjustment to be computed).

Below you can set the pixel width. This sets the size of the matrix the mRNAs are projected on. A larger value increases the resolution but more ressources will be required for the computations.

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Increasing the KDE kernel bandwidth (in μm) creates a smooting effect between the different mRNA spots, which integrates the mRNA signal. The center plot should help you optimize the value.

The rightmost plot shows the algorithm's final cell-type assignment.
Choose a bandwidth large enough to eliminate unwanted noise and at the same time retain the desired structural detail in the result.
Choose the threshold to remove unwanted, low-signal areas from the result.

As can be seen in the figure below, a small bandwidth (left column) will not integrate the signal enough (i.e. the resultant cell-type map is still grainy) while high values (right column) lead too oversmoothing.
If the expression threshold is too lenient, there will be no distinction between cells and background (top row) and if it is too rigid no/few cells will be detected.

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