Summary of Sccdcg: Efficient Deep Structural Clustering For Single-cell Rna-seq Via Deep Cut-informed Graph Embedding, by Ping Xu et al.
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
by Ping Xu, Zhiyuan Ning, Meng Xiao, Guihai Feng, Xin Li, Yuanchun Zhou, Pengfei Wang
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Genomics (q-bio.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel framework called scCDCG for efficiently and accurately clustering single-cell RNA sequencing (scRNA-seq) data. The framework addresses the limitations of traditional methods by incorporating intercellular high-order structural information, using deep cut-informed techniques to capture complex relationships between cells. The method consists of three components: graph embedding, self-supervised learning guided by optimal transport, and autoencoder-based feature learning. Experimental results on 6 datasets demonstrate scCDCG’s superior performance and efficiency compared to 7 established models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary scRNA-seq helps us understand how different cells work together. Researchers have developed new ways to analyze this data, but they often ignore important information about how genes are connected inside cells. A team created a new method called scCDCG that uses deep learning techniques to group similar cells together while keeping track of these connections. This method is better than others at finding meaningful patterns in the data and can be used for various applications. |
Keywords
* Artificial intelligence * Autoencoder * Clustering * Deep learning * Embedding * Self supervised
