.png){kind=icon}
返回
MVCLST: A spatial transcriptome data analysis pipeline for cell type classification based on multi-view comparative learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ymeth.2024.11.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent advancements in spatial transcriptomics sequencing technologies can not only provide gene expression within individual cells or cell clusters (spots) in a tissue but also pinpoint the exact location of this expression and generate detailed images of stained tissue sections, which offers invaluable insights into cell type identification and cell function exploration. However, effectively integrating the gene expression data, spatial location information, and tissue images from spatial transcriptomics data presents a significant challenge for computational methods in cell classification. In this work, we propose MVCLST, a multi-view comparative learning method to analyze spatial transcriptomics data for accurate cell type classification. MVCLST constructs two views based on gene expression profiles, cell coordinates and image features. The multi-view method we proposed can significantly enhance the effectiveness of feature extraction while avoiding the impact of erroneous information in organizing image or gene expression data. The model employs four separate encoders to capture shared and unique features within each view. To ensure consistency and facilitate information exchange between the two views, MVCLST incorporates a contrastive learning loss function. The extracted shared and private features from both views are fused using corresponding decoders. Finally, the model utilizes the Leiden algorithm to cluster the learned features for cell type identification. Additionally, we establish a framework called MVCLST-CCFS for spatial transcriptomics data analysis based on MVCLST and consistent clustering. Our method achieves excellent results in clustering on human dorsolateral prefrontal cortex data and the mouse brain tissue data. It also outperforms state-of-the-art techniques in the subsequent search for highly variable genes across cell types on the mouse olfactory bulb data.
Keyword:
Spatial transcriptome data clustering
Cell type identification
Multi-view
Contrastive learning
Consensus clustering
期刊
IF:
4.3
论文数:
4.9K
被引数:
2.4W
机构
引用论文
Spatial organization of the somatosensory cortex revealed by osmFISHosmFISH揭示的体感皮层的空间组织.png)
NATURE METHODS
NATURE METHODSIF32.1
PREAI
分享收藏Latent feature extraction with a prior-based self-attention framework for spatial transcriptomicsGENOME RESEARCH
OAAI
GENOME RESEARCHIF5.5
OAAI分享收藏Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities在具有不均匀细胞密度的空间分辨的单细胞转录组数据中表征空间基因表达异质性GENOME RESEARCH
OAAI
GENOME RESEARCHIF5.5
OAAI分享收藏Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics通过空间分辨转录组学以单细胞分辨率揭示器官的分子结构TRENDS IN BIOTECHNOLOGYOAAI
TRENDS IN BIOTECHNOLOGYIF14.9
OAAI分享收藏Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST空间转录组学的空间信息聚类、整合和反卷积NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics datascMLC: 一种用于单细胞多组学数据的准确且稳健的多重社区检测方法OAAI
OAAI分享收藏Hotspot identifies informative gene modules across modalities of single-cell genomicsHotspot识别跨单细胞基因组学模式的信息基因模块CELL SYSTEMSOAAI
CELL SYSTEMSIF7.7
OAAI分享收藏
查看更多内容