Yuge Wang

Dec 10, 2024 | biorxiv.org | Tianyu Liu |Yuge Wang |Hongyu Li |Hongyu Zhao

AbstractFoundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data.

Aug 27, 2024 | biorxiv.org | Tianyu Liu |Yuge Wang |Hongyu Li |Hongyu Zhao

AbstractFoundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data.

Feb 27, 2024 | biorxiv.org | Tianyu Liu |Yuge Wang |Hongyu Li |Hongyu Zhao

AbstractFoundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods.

Feb 7, 2024 | biorxiv.org | Yuge Wang |Hongyu Zhao

AbstractWith continuous progress of single-cell chromatin accessibility profiling techniques, scATAC-seq has become more commonly used in investigating regulatory genomic regions and their involvement in developmental, evolutionary, and disease-related processes. At the same time, accurate cell type annotation plays a crucial role in comprehending the cellular makeup of complex tissues and uncovering novel cell types.

Dec 18, 2023 | genomebiology.biomedcentral.com | Biqing Zhu |Yuge Wang |Le Fu Zhang |Hongyu Zhao |Li-Ting Ku

scTCR-seq data in the filtered_contig_annotations.csv files were collected from the two data sources as described in the manuscript. Only T cells with \(\alpha\) and \(\beta\) chains were kept and we further chose cells with both RNA and paired TCR data. scRNA-seq data were processed in accordance with the scanpy [49] (v1.7.2, RRID:SCR_018139) workflow for preprocessing. Next, ResPAN [10] (v0.1.0) was applied to remove batch effects.