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DeepLinc: deep-learning framework for landscapes of interacting cells

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 19 May 2022Publisher:Zenodo
Authors: Runze Li; Xuerui Yang;

doi: 10.5281/zenodo.6564072 , 10.5281/zenodo.6564143 , 10.5281/zenodo.6564073

DeepLinc: deep-learning framework for landscapes of interacting cells

Abstract

DeepLinc is a tool for de novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data. The source code of DeepLinc is also available on GitHub: https://github.com/xryanglab/DeepLinc

Keywords

single-cell spatial transcriptome, deep learning, cell interaction, variational graph autoencoder

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
1
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15