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<h2>Graph Based Methods</h2>
<p>Greetings! You've reached the landing page for my work in graph-based methods for spatial biological data. These projects all fall under this common theme of "graph-based", but work with different types of data and have different end-goals, so I've split them into different repositories.</p>
<p>NOTE: Some of the data and code originally used for these projects is unavailable for public upload. I've done my best to fill in any gaps in the documentation/README.</p>
<h3>Graph Classification</h3>
<p>View code <a href="https://github.com/morganoneka/CellGraphClassification">here</a></p>
<p>Giotto can tell us what cell pairs are enriched/depleted. We can then create networks. This code uses StellarGraph to classify cell interaction graphs into diagnostic groups. </p>
<h3>Spatial Gene Expression Prediction</h3>
<p>View code <a href="https://github.com/morganoneka/HEtoST">here</a></p>
<p>This code uses <a href="https://kimialab.uwaterloo.ca/kimia/index.php/data-and-code-2/kimia-net/">KimiaNet</a> to generate features from histology images, then uses this to predict spatial gene expression using Spatial Random Forest.</p>
<h3>High Attention Regions</h3>
<p>View code <a href="https://github.com/morganoneka/DensityAttention/tree/main/attention">here</a>.</p>
<p><a href="https://github.com/mahmoodlab/CLAM">CLAM</a> is a weakly supervised method for identifying "high attention" regions in pathology images, i.e. regions that help distinguish between diganostic groups. In this code, I adapt this workflow to work with raster renderings of density functions to identify high attention regions.</p>
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