SfN19/nmf_net.html at master · dafishcode/SfN19 · GitHub

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			<tr><strong> Non-negative matrix factorisation identifies persistent network differences between genotypes</strong> </tr>
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					<strong><em>[NMF Analysis]</em></strong> Using a sliding window approach, we can identify time-varying functional connectivity, using simple correlation across the anatomical regions estimated within each time window. <br />
					We can then concatenate unique edges over time into a <em>n</em> by <em>k</em> array, where <em>n</em> is the number of unique edges and <em>k</em> the number of time windows sampled. Using sparsity constrained non-negative matrix factorisation, we can decompose the full set of connectivity edges into variably expressed subnetworks, and their time varying expression profiles.
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					<strong><em>[Left]</em></strong> We can represent factors derived from NMF as subnetworks that are variably expressed over time. Networks derived from a four-factor factorisation are shown here.
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					<strong><em>[Middle]</em></strong> The factors' subnetwork expression varies over time, but shows persistent differences across genotypes.
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					<strong><em>[Right]</em></strong> Full time varying connectivity matrices are shown here, with time points corresponding to the dashed line in the middle plot.
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