ScRNAPip: A systematic and dynamic pipeline for single-cell RNA sequencing analysis.

Xu, Limin; Zhang, Jing; He, Yiqian; Yang, Qianqian; Mu, Tianhao; Guo, Qiushi; Li, Yingqiang; Tong, Tian; Chen, Shifu; Ye, Richard D

Xu, Limin; Zhang, Jing; He, Yiqian; Yang, Qianqian; Mu, Tianhao; Guo, Qiushi; Li, Yingqiang; Tong, Tian; Chen, Shifu; Ye, Richard D.

Afiliación

Xu L; The Chinese University of Hong Kong Shenzhen Futian Biomedical Innovation R&D Center Shenzhen China.
Zhang J; School of Medicine The Chinese University of Hong Kong Shenzhen China.
He Y; HaploX Biotechnology Shenzhen China.
Yang Q; The Chinese University of Hong Kong Shenzhen Futian Biomedical Innovation R&D Center Shenzhen China.
Mu T; The Chinese University of Hong Kong Shenzhen Futian Biomedical Innovation R&D Center Shenzhen China.
Guo Q; HaploX Biotechnology Shenzhen China.
Li Y; The Chinese University of Hong Kong Shenzhen Futian Biomedical Innovation R&D Center Shenzhen China.
Tong T; School of Medicine The Chinese University of Hong Kong Shenzhen China.
Chen S; HaploX Biotechnology Shenzhen China.
Ye RD; HaploX Biotechnology Shenzhen China.

Imeta ; 2(4): e132, 2023 Nov.

Article en En | MEDLINE | ID: mdl-38868218

ABSTRACT

ABSTRACT

With the advancement of sequencing technology, cell separation, and whole-genome amplification techniques, single cell technology for genome sequencing is emerging gradually. In comparison to traditional genome sequencing at the multi-cellular level, single-cell sequencing can not only measure the gene expression level more accurately but also can detect a small amount of gene expression or rare noncoding RNA. This technology has garnered increasing interest among researchers engaged in single-cell studies in recent years. Here, we developed a reproducible computational workflow for scRNA-seq data analysis which including tasks like quality control, normalization, data correction, pseudotime analysis, copy number analysis, etc. We illustrate the application of these steps using publicly available datasets and provide practical recommendations for their implementation. This study serves as a comprehensive tutorial for researchers keen on single-cell data analysis, aiding users in constructing and refining their own analysis pipelines.

Palabras clave

bioinformatics; data analysis; singlecell RNAseq; singlecell browsers

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Imeta Año: 2023 Tipo del documento: Article Pais de publicación: Australia