ASAP：一个用于单细胞 RNA-seq 数据分析和交互式可视化的基于网络的平台。

ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.

机构信息

Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.

Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland.

出版信息

Bioinformatics. 2017 Oct 1;33(19):3123-3125. doi: 10.1093/bioinformatics/btx337.

DOI:10.1093/bioinformatics/btx337

PMID:28541377

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5870842/

Abstract

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets.

RESULTS

We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.

AVAILABILITY AND IMPLEMENTATION

The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP.

CONTACT

bart.deplancke@epfl.ch.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

单细胞 RNA 测序（scRNA-seq）允许对数千个单个细胞的整个转录组进行分析，从而能够在细胞水平上对组织进行分子探索。这种分析能力引起了世界上许多研究小组的极大兴趣，但这些小组通常缺乏处理复杂 scRNA-seq 数据集的专业知识。

结果

我们开发了一个完全集成的基于网络的平台，旨在对基因组比对后的 scRNA-seq 数据进行全面分析：从输入计数数据文件的解析、过滤和标准化，到数据的可视化表示、细胞群的识别、差异表达基因（包括特定于群的标记基因）以及功能基因集的富集。这个自动化单细胞分析管道（ASAP）结合了广泛使用的算法和复杂的可视化工具。与现有的 scRNA-seq 分析平台相比，研究人员（包括缺乏计算专业知识的研究人员）能够以直接和实时的方式与数据交互。此外，鉴于 scRNA-seq 和批量 RNA-seq 分析工作流程之间的重叠，ASAP 应该在概念上广泛适用于任何 RNA-seq 数据集。作为验证，我们展示了如何使用 ASAP 简单地重现涉及五种不同细胞类型的 91 个小鼠细胞的单细胞研究结果。