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<!DOCTYPE html>
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<meta charset="utf-8">
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<title>readme</title>
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<body class="fullcontent">
<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
<main class="content" id="quarto-document-content">
<section id="yalecollab_tape-krishnaswamy" class="level1">
<h1>yaleCollab_TAPE-KRISHNASWAMY</h1>
<p>Yale Collab with Aarthi (Smita Krishnaswamy group). KGE of multiomics single-cell communications and signalling</p>
<section id="kg-design" class="level2">
<h2 class="anchored" data-anchor-id="kg-design">KG design</h2>
<section id="what-we-need" class="level3">
<h3 class="anchored" data-anchor-id="what-we-need">What we need</h3>
<ul>
<li>Capture cell communications from L-R</li>
<li>Capture signalling changes via PTMs</li>
<li>Summarise above as pathways</li>
<li>Who sends, who receives?</li>
<li>Compare between cells, clusters, conditions
<ul>
<li>Sc distance metric</li>
</ul></li>
</ul>
<section id="summary" class="level4">
<h4 class="anchored" data-anchor-id="summary"><strong>Summary</strong></h4>
<p>L –> R –> PTMs</p>
<p>Idea of talking (Ligands), hearing (receptors), and listening (PTMs)</p>
</section>
</section>
<section id="resources" class="level3">
<h3 class="anchored" data-anchor-id="resources">Resources</h3>
<ul>
<li>NicheNet</li>
<li>CellChat</li>
<li>REACTOME</li>
</ul>
</section>
<section id="methods" class="level3">
<h3 class="anchored" data-anchor-id="methods">Methods</h3>
<ul>
<li>Pathways should be edge annotations (essentially, annotating the triples with a single/list of pathways)
<ul>
<li>Pathway annotations: From the 6 1st level REACTOME pathways (assuming most/all L, R, PTMS are present in those)
<ul>
<li>2nd level REACTOME pathway annotations (for visualisation?)</li>
<li>3rd level REACTOME pathway annotations for metadata?</li>
</ul></li>
<li>Per pathway: Measure GEx/intensity of entities
<ul>
<li>Analyse the 3 distinct layers: Formally define dominance?
<ul>
<li>Ligand dominance -> sender</li>
<li>Receiver dominance -> receiver</li>
<li>PTM dominance -> ????</li>
</ul></li>
<li>Summary pathway score:
<ul>
<li>Define cell/group of cells</li>
<li>Absolute value: Biased by pathway importance to cell</li>
<li>Relative value: (aka EMD with all cells as reference) Relative importance of pathway on different cells</li>
<li><del>Should PTMs provide positive/negative scores? (i.e. inhibitors/activators)</del></li>
</ul></li>
</ul></li>
</ul></li>
<li>KGE: Explore pathway annotations</li>
<li>Diffusion method on KG: How the L, R and PTMs relate to one another
<ul>
<li>Wavelets</li>
</ul></li>
<li>Cells as signals on the graph:
<ul>
<li>Project</li>
<li>Compare across cells</li>
</ul></li>
</ul>
</section>
<section id="process" class="level3">
<h3 class="anchored" data-anchor-id="process">Process</h3>
<ul>
<li>Handle all 3 sources of data at once: DBs, scRNAseq and cytof
<ul>
<li>Read databases: L and R list</li>
<li>Read cytof data: PTMs</li>
</ul></li>
<li>Build KG: From entities above, define 2 types of relations:
<ul>
<li>L-R cell commns</li>
<li>Entity-entity (L to R and R to PTM) interaction if both in the same reactome pathway level</li>
</ul></li>
<li>Process sc data:
<ul>
<li>Filter data to KG entities and process both modalities</li>
<li>All cells from both modalities should add up to same value</li>
</ul></li>
<li>Downstream analyses
<ul>
<li>Project sc data:
<ul>
<li>Compute distances between cells
<ul>
<li>DR for single modality</li>
<li>DR for integrated modalities</li>
</ul></li>
<li>Cluster cells based on said distance: Functional communication groups</li>
</ul></li>
<li>Compute pathway scores: <strong>Can we compute intensity of signal of cell on each node of the KG?</strong> How different would that be from just GEx/AbIntensity
<ul>
<li>Summary pathway score (per cell, per FC cluster)</li>
<li>Layer analysis: sender/receiver (per cell, per FC cluster)</li>
</ul></li>
<li>Inference:
<ul>
<li>In silico pathway ablation -> rebalancing of KG
<ul>
<li>New distance prediction</li>
</ul></li>
<li>In silico ligand modulation -> simulate ligand presence at diff intensities
<ul>
<li>New distance and pathway score predictions</li>
</ul></li>
</ul></li>
</ul></li>
</ul>
</section>
</section>
</section>
</main>
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