STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e1084"><mml:mi>k</mml:mi></mml:math>-sums clustering-Reference-Cited by-同舟云学术

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering

Published:2023-11 Issue: Volume:166 Page:107440
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Peng Lihong^ORCID,He Xianzhi,Peng Xinhuai,Li Zejun,Zhang Li

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference71 articles.

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3. Modeling and analyzing single-cell multimodal data with deep parametric inference;Hu;Brief. Bioinform.,2023

4. Statistical and machine learning methods for spatially resolved transcriptomics with histology;Hu;Comput. Struct. Biotechnol. J.,2021

5. CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types;Zhang;Bioinformatics,2022

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