Tzaferis Christos, Karatzas Evangelos, Baltoumas Fotis A, Pavlopoulos Georgios A, Kollias George, Konstantopoulos Dimitris
Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece.
Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece.
Comput Struct Biotechnol J. 2023 Oct 20;21:5382-5393. doi: 10.1016/j.csbj.2023.10.032. eCollection 2023.
Analysis and interpretation of high-throughput transcriptional and chromatin accessibility data at single-cell (sc) resolution are still open challenges in the biomedical field. The existence of countless bioinformatics tools, for the different analytical steps, increases the complexity of data interpretation and the difficulty to derive biological insights. In this article, we present SCALA, a bioinformatics tool for analysis and visualization of single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) datasets, enabling either independent or integrative analysis of the two modalities. SCALA combines standard types of analysis by integrating multiple software packages varying from quality control to the identification of distinct cell populations and cell states. Additional analysis options enable functional enrichment, cellular trajectory inference, ligand-receptor analysis, and regulatory network reconstruction. SCALA is fully parameterizable, presenting data in tabular format and producing publication-ready visualizations. The different available analysis modules can aid biomedical researchers in exploring, analyzing, and visualizing their data without any prior experience in coding. We demonstrate the functionality of SCALA through two use-cases related to TNF-driven arthritic mice, handling both scRNA-seq and scATAC-seq datasets. SCALA is developed in R, Shiny and JavaScript and is mainly available as a standalone version, while an online service of more limited capacity can be found at http://scala.pavlopouloslab.info or https://scala.fleming.gr.
在单细胞（sc）分辨率下对高通量转录和染色质可及性数据进行分析和解释，仍然是生物医学领域有待解决的挑战。针对不同分析步骤的无数生物信息学工具的存在，增加了数据解释的复杂性以及获得生物学见解的难度。在本文中，我们介绍了SCALA，这是一种用于分析和可视化单细胞RNA测序（scRNA-seq）以及使用测序法进行转座酶可及染色质分析（scATAC-seq）数据集的生物信息学工具，能够对这两种模式进行独立或综合分析。SCALA通过整合多个软件包（从质量控制到识别不同的细胞群体和细胞状态）来结合标准类型的分析。额外的分析选项支持功能富集、细胞轨迹推断、配体-受体分析和调控网络重建。SCALA是完全可参数化的，以表格形式呈现数据并生成可用于发表的可视化结果。不同的可用分析模块可以帮助生物医学研究人员探索、分析和可视化他们的数据，而无需任何编码方面的先验经验。我们通过两个与TNF驱动的关节炎小鼠相关的用例展示了SCALA的功能，处理了scRNA-seq和scATAC-seq数据集。SCALA是用R、Shiny和JavaScript开发的，主要以独立版本提供，而容量更有限的在线服务可在http://scala.pavlopouloslab.info或https://scala.fleming.gr找到。
