Integrate single-cell RNA, protein, and ATAC data with WNN and MOFA+ workflows.
npx skills add https://github.com/TianGzlab/OmicsClaw --skill sc-multiome
Please help me install this Agent Skill. Skill: sc-multiome Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/singlecell/sc-multiome Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill sc-multiome
This Skill streamlines the complex process of integrating and analyzing multiple types of single-cell omics data, such as RNA and protein or RNA and ATAC, enabling a more comprehensive understanding of cellular states.
Run the sc-multiome skill on your processed multi-omics data file.
High-intent search queries and answers about installing and using this skill.
Multi-omics integration combines RNA, protein (ADT), and ATAC modalities to provide a comprehensive understanding of cellular states. This Skill implements WNN analysis, MOFA+, and Muon workflows to jointly analyze multiple data types from technologies like CITE-seq and 10X Multiome.
Yes, this Skill performs multi-modal integration using scanpy and numpy, supporting RNA, protein (ADT), and ATAC modalities. It utilizes Weighted Nearest Neighbor (WNN) analysis, MOFA+, and Muon workflows to jointly analyze and visualize your single-cell data.
The best way to visualize joint UMAPs for 10X Multiome datasets is by using this Skill's Muon and MuData workflows. It generates joint UMAP plots and visualizes modality weights to help you explore integrated cellular states across multiple omics layers.
Yes, MOFA+ works alongside Weighted Nearest Neighbor (WNN) analysis for single-cell multiome integration. This Skill implements both methods to process multi-modal data, allowing you to simultaneously explore gene expression and surface protein levels.
You need scanpy and numpy as required Python packages for multi-omics single-cell integration. Optionally, you can install muon, Seurat, Signac, or ArchR to access advanced functionalities for analyzing RNA, protein, and ATAC modalities.
ya-wayIntegrate multi-modal single-cell RNA, protein, and ATAC data into unified representations.
crazymsnModel single-cell omics data with variational autoencoders for batch correction.
jaechang-hitsIntegrate RNA, ATAC, and protein modalities with WNN graphs in MuData.
KetomihineProvide offline ArchR documentation for scATAC-seq analysis workflows.
