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haozhu233 committed Mar 26, 2024
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Expand Up @@ -48,11 +48,14 @@ Understanding the Inferred Networks
After the ``RegDiffusion`` model converges, what you get is simply an
`adjacency` matrix. When you have thousands or tens of thousands of genes,
it's getting difficult to analyze matrix at that scale. In our paper, we
propose to analyze the local network by focusing on the local networks around
genes you care the most.
propose a way to analyze the local network by focusing on the genes you care
the most. With this package, you could visualize the network directly.
Alternatively, we can also extract the network first, identify some node
clusters, and then perform a gene set enrichment analysis to find out the
corresponding pathways.

.. image:: https://github.com/TuftsBCB/RegDiffusion/blob/master/resources/apoe_reg.png?raw=true
:width: 400
:width: 500
:alt: Inferred network around ApoE

Inference Speed
Expand All @@ -63,6 +66,20 @@ device. Even if you don't have access to those fancy GPU cards, RegDiffusion
still works. Inference on the same large network takes roughly 3 hours on a
mid-range 12-core CPU.

+--------------+-----------+-----------+-----------+-----------+
| | hESC | hHep | Hammond | Atlas |
+==============+===========+===========+===========+===========+
| # of Genes | 1,410 | 1,448 | 14,065 | 15,547 |
| # of Cells | 758 | 425 | 8,258 | 1,296 |
+--------------------------------------------------------------+
| GENIE3 | 1hr 58min | 48min 51s | - | - |
| GRNBoost2 | 34min 12s | 16min 32s | - | - |
| DeepSEM | 29s | 15s | 4hr 21min | 59min |
| DAZZLE | 31s | 16s | 4hr 47min | 1hr 10min |
+--------------------------------------------------------------|
| RegDiffusion | 7s | 8s | 3min 28s | 4min 10s |
+--------------+-----------+-----------+-----------+-----------+

Citation
--------
Please consider cite our work if you found it useful for your work:
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