diff --git a/content/news/2025-02-13-cellxgene_vip/index.md b/content/news/2025-02-13-cellxgene_vip/index.md new file mode 100644 index 0000000000..85e9b98024 --- /dev/null +++ b/content/news/2025-02-13-cellxgene_vip/index.md @@ -0,0 +1,62 @@ +--- +title: CellxGene-VIP next-level scRNA-seq, spatial transcriptomics, and multiome data visualization +date: '2025-02-13' +tease: CELLxGENE-VIP is a frontend plugin of CELLxGENE, enabling users to generate multiple QC plots in high-resolution SVG or PNG format. + It can also visualize spatial transcriptomics and 10X multiome. +hide_tease: true +tags: [tools] +supporters: +- elixir +- denbi +- unifreiburg +authors: nilchia +authors_structured: +- github: nilchia +subsites: [global, all, eu] +main_subsite: eu +--- + +# CELLxGENE-VIP; A Major Step for Single-Cell, spatial transcriptomics, and multiome Data Analysis + +CELLxGENE-VIP (Visualization In Plugin) is an extension of the original [CELLxGENE](https://github.com/chanzuckerberg/cellxgene) tool developed by the Chan Zuckerberg Initiative. +It can efficiently extract deep insights from single-cell RNA sequencing, spatial transcriptomics, and emerging multiome datasets. + +This integration enhances the capabilities of high-throughput data exploration and visualization, bridging the gap between computational power and user-friendly analysis. +While CELLxGENE focuses on fast and interactive visualization of single-cell data, CELLxGENE-VIP goes a step further by integrating interactive processing and customized visual analytics, providing deeper insights into the data. + ++ It generates a comprehensive set of over eighteen commonly used quality control and analytical plots in high resolution with highly customizable settings. ++ It provides more advanced analytical functions to gain insights into cellular compositions and deep biology, such as marker gene identification, differential gene expression analysis, and gene set enrichment analysis. ++ It pioneers methods to visualize multi-modal data, such as spatial transcriptomics embedding aligned with histological images and the latest 10x Genomic Multiome dataset. + +