Computational and Structural Biotechnology Journal (Jan 2023)

scDrug: From single-cell RNA-seq to drug response prediction

  • Chiao-Yu Hsieh,
  • Jian-Hung Wen,
  • Shih-Ming Lin,
  • Tzu-Yang Tseng,
  • Jia-Hsin Huang,
  • Hsuan-Cheng Huang,
  • Hsueh-Fen Juan

Journal volume & issue
Vol. 21
pp. 150 – 157

Abstract

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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.

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