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Empowering Integrative and Collaborative Exploration of Single-Cell and Spatial Multimodal Data with SGS

Xia, T.; Sun, J.; Lu, F.; Luo, Y.; Mao, Y.; Xu, L.; Wang, Y.

2024-07-23 bioinformatics
10.1101/2024.07.19.604227 bioRxiv
Show abstract

Recent advancements in single-cell and spatial omics technologies have revolutionized our ability to capture multiple modalities data at a genome-wide scale within individual cells. However, visualizing these large-scale, high-dimensional, and complex datasets poses significant challenges. Here, we present SGS, a user-friendly, collaborative and versatile browser for visualizing single-cell and spatial multiomics data. SGS incorporates a novel genome browser framework, flexible visualization modules and a multi-panel adaptive communication mechanism to enable the synchronous visualization of diverse datasets. Notably, SGS empowers users with advanced capabilities for comparative visualization, through features like scCompare, scMultiView, and dual-chromosome module. Additionally, by adopting the "workstation concept", SGS enables data fast visualization and collaborative exploration. We showcase the potential of SGS in the comparative visualization and coordinated exploration of mutlimodal data with two examples. SGS is publicly available at https://sgs.bioinfotoolkits.net/home.

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The top 6 journals account for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.