https://scholars.lib.ntu.edu.tw/handle/123456789/627566
標題: | scDrug: From single-cell RNA-seq to drug response prediction | 作者: | Hsieh, Chiao-Yu Wen, Jian-Hung Lin, Shih-Ming Tseng, Tzu-Yang Huang, Jia-Hsin Huang, Hsuan-Cheng HSUEH-FEN JUAN |
關鍵字: | Bioinformatics; Drug repositioning; Single-cell RNA-seq; Tumor cell subpopulations | 公開日期: | 2023 | 出版社: | ELSEVIER | 卷: | 21 | 起(迄)頁: | 150-157 | 來源出版物: | Computational and structural biotechnology journal | 摘要: | Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/627566 | ISSN: | 2001-0370 | DOI: | 10.1016/j.csbj.2022.11.055 |
顯示於: | 生命科學系 |
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