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GUANACO enables interactive visualization of single-cell RNA-seq and ATAC-seq data, highlighting robust statistical testing.
While single-cell multi-omics has advanced our understanding of cellular heterogeneity, the complexity of these datasets remains a barrier for non-computational users. Here, we introduce GUANACO, a Dash-based Python package for interactive, code-free visualization of single-cell RNA-seq and ATAC-seq data. GUANACO integrates matrix- and genome-track views, supporting flexible cell/gene selection, statistical testing, and transcription factor binding site exploration. Its user-friendly interface offers colorblind-friendly palettes, intuitive controls, and options for generating publication-ready figures. With a low memory requirement and cost-effective deployment, GUANACO facilitates seamless sharing and reproducible research, empowering researchers to explore, interpret, and communicate single-cell insights without extra coding.
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Zhang et al. (2025) studied this question.
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