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单细胞分析器:云端单细胞RNA测序数据的交互式分析

SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud.

作者信息

Prieto Carlos, Barrios David, Villaverde Angela

机构信息

Bioinformatics Service, Nucleus, University of Salamanca, Salamanca, Spain.

出版信息

Front Bioinform. 2022 May 23;2:793309. doi: 10.3389/fbinf.2022.793309. eCollection 2022.

DOI:10.3389/fbinf.2022.793309
PMID:36304292
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9580930/
Abstract

Single-cell RNA sequencing (scRNA-Seq) enables researchers to quantify the transcriptomes of individual cells. The capacity of researchers to perform this type of analysis has allowed researchers to undertake new scientific goals. The usefulness of scRNA-Seq has depended on the development of new computational biology methods, which have been designed to meeting challenges associated with scRNA-Seq analysis. However, the proper application of these computational methods requires extensive bioinformatics expertise. Otherwise, it is often difficult to obtain reliable and reproducible results. We have developed SingleCAnalyzer, a cloud platform that provides a means to perform full scRNA-Seq analysis from FASTQ within an easy-to-use and self-exploratory web interface. Its analysis pipeline includes the demultiplexing and alignment of FASTQ files, read trimming, sample quality control, feature selection, empty droplets detection, dimensional reduction, cellular type prediction, unsupervised clustering of cells, pseudotime/trajectory analysis, expression comparisons between groups, functional enrichment of differentially expressed genes and gene set expression analysis. Results are presented with interactive graphs, which provide exploratory and analytical features. SingleCAnalyzer is freely available at https://singleCAnalyzer.eu.

摘要

单细胞RNA测序(scRNA-Seq)使研究人员能够对单个细胞的转录组进行定量分析。研究人员进行此类分析的能力使他们能够开展新的科学目标。scRNA-Seq的实用性依赖于新计算生物学方法的开发,这些方法旨在应对与scRNA-Seq分析相关的挑战。然而,正确应用这些计算方法需要广泛的生物信息学专业知识。否则,往往很难获得可靠且可重复的结果。我们开发了SingleCAnalyzer,这是一个云平台,它提供了一种手段,可在易于使用且具有自探索功能的网络界面中从FASTQ文件执行完整的scRNA-Seq分析。其分析流程包括FASTQ文件的解复用和比对、读段修剪、样本质量控制、特征选择、空滴检测、降维、细胞类型预测、细胞的无监督聚类、伪时间/轨迹分析、组间表达比较、差异表达基因的功能富集以及基因集表达分析。结果通过交互式图表呈现,这些图表提供了探索性和分析性功能。SingleCAnalyzer可在https://singleCAnalyzer.eu免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/607d/9580930/ba002aa07161/fbinf-02-793309-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/607d/9580930/99f8486cb292/fbinf-02-793309-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/607d/9580930/ba002aa07161/fbinf-02-793309-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/607d/9580930/99f8486cb292/fbinf-02-793309-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/607d/9580930/ba002aa07161/fbinf-02-793309-g002.jpg

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