Musu Yuan
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View article: Smart spatial omics (S2-omics) optimizes region-of-interest selection to capture molecular heterogeneity in diverse tissues v2
Smart spatial omics (S2-omics) optimizes region-of-interest selection to capture molecular heterogeneity in diverse tissues v2 Open
A protocol describing the application of S2-omics on a colorectal cancer tissue section, designing 10x VisiumHD experiment. S2-omics is an end-to-end workflow that automatically selects regions of interest for spatial omics experiments usi…
View article: Designing smart spatial omics experiments with S2-omics v2
Designing smart spatial omics experiments with S2-omics v2 Open
A protocol describing the application of S2-omics on a colorectal cancer tissue section, designing 10x VisiumHD experiment. S2-omics is an end-to-end workflow that automatically selects regions of interest for spatial omics experiments usi…
View article: Designing smart spatial omics experiments with S2-omics v1
Designing smart spatial omics experiments with S2-omics v1 Open
A protocol describing the application of S2-omics on a colorectal cancer tissue section, designing 10x VisiumHD experiment. S2-omics is an end-to-end workflow that automatically selects regions of interest for spatial omics experiments usi…
View article: Designing smart spatial omics experiments with S2Omics
Designing smart spatial omics experiments with S2Omics Open
Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and functi…
View article: scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement
scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement Open
Motivation In the past decade, single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal method for transcriptomic profiling in biomedical research. Precise cell-type identification is crucial for subsequent analysis of single-cell d…
View article: Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data
Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data Open
Motivation Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but …
View article: SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data
SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data Open
Motivation The rapid development of spatial transcriptome technologies has enabled researchers to acquire single-cell-level spatial data at an affordable price. However, computational analysis tools, such as annotation tools, tailored for …
View article: Clustering single-cell multi-omics data with MoClust
Clustering single-cell multi-omics data with MoClust Open
Motivation Single-cell multi-omics sequencing techniques have rapidly developed in the past few years. Clustering analysis with single-cell multi-omics data may give us novel perspectives to dissect cellular heterogeneity. However, multi-o…
View article: Clustering CITE-seq data with a canonical correlation-based deep learning method
Clustering CITE-seq data with a canonical correlation-based deep learning method Open
Single-cell multiomics sequencing techniques have rapidly developed in the past few years. Among these techniques, single-cell cellular indexing of transcriptomes and epitopes (CITE-seq) allows simultaneous quantification of gene expressio…
View article: Clustering single cell CITE-seq data with a canonical correlation based deep learning method
Clustering single cell CITE-seq data with a canonical correlation based deep learning method Open
Single cell sequencing examines the sequence information from individual cells with optimized next generation sequencing (NGS) technologies. It provides researchers a higher resolution of cellular differences and a better understanding of …