SC1: A Tool for Interactive Web-Based Single-Cell RNA-Seq Data Analysis

被引:7
作者
Moussa, Marmar [1 ]
Mandoiu, Ion I. [2 ]
机构
[1] Univ Connecticut, Sch Med, Carole & Ray Neag Comprehens Canc Ctr, 263 Farmington Ave, Farmington, CT 06030 USA
[2] Univ Connecticut, Comp Sci & Engn Dept, Storrs, CT USA
关键词
cell cycle; clustering; scRNA-Seq; SC1; single-cell analysis; TF-IDF;
D O I
10.1089/cmb.2021.0051
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Single-cell RNA-Seq (scRNA-Seq) is critical for studying cellular function and phenotypic heterogeneity as well as the development of tissues and tumors. In this study, we present SC1 a web-based highly interactive scRNA-Seq data analysis tool publicly accessible at https://sc1.engr.uconn.edu. The tool presents an integrated workflow for scRNA-Seq analysis, implements a novel method of selecting informative genes based on term-frequency inverse-document-frequency scores, and provides a broad range of methods for clustering, differential expression analysis, gene enrichment, interactive visualization, and cell cycle analysis. The tool integrates other single-cell omics data modalities such as T-cell receptor (TCR)-Seq and supports several single-cell sequencing technologies. In just a few steps, researchers can generate a comprehensive analysis and gain powerful insights from their scRNA-Seq data.
引用
收藏
页码:820 / 841
页数:22
相关论文
共 38 条
[11]  
Hahsler M, 2008, J STAT SOFTW, V25, P1
[12]  
Immunai, 2020, IMM
[13]  
Kharchenko PV, 2014, NAT METHODS, V11, P740, DOI [10.1038/NMETH.2967, 10.1038/nmeth.2967]
[14]   Challenges in unsupervised clustering of single-cell RNA-seq data [J].
Kiselev, Vladimir Yu ;
Andrews, Tallulah S. ;
Hemberg, Martin .
NATURE REVIEWS GENETICS, 2019, 20 (05) :273-282
[15]  
Leskovec J, 2014, MINING OF MASSIVE DATASETS, 2ND EDITION, P1
[16]   Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19 [J].
Liao, Mingfeng ;
Liu, Yang ;
Yuan, Jing ;
Wen, Yanling ;
Xu, Gang ;
Zhao, Juanjuan ;
Cheng, Lin ;
Li, Jinxiu ;
Wang, Xin ;
Wang, Fuxiang ;
Liu, Lei ;
Amit, Ido ;
Zhang, Shuye ;
Zhang, Zheng .
NATURE MEDICINE, 2020, 26 (06) :842-+
[17]   Reconstructing cell cycle pseudo time-series via single-cell transcriptome data [J].
Liu, Zehua ;
Lou, Huazhe ;
Xie, Kaikun ;
Wang, Hao ;
Chen, Ning ;
Aparicio, Oscar M. ;
Zhang, Michael Q. ;
Jiang, Rui ;
Chen, Ting .
NATURE COMMUNICATIONS, 2017, 8
[18]   Detection of HPV E7 Transcription at Single-Cell Resolution in Epidermis [J].
Lukowski, Samuel W. ;
Tuong, Zewen K. ;
Noske, Katharina ;
Senabouth, Anne ;
Nguyen, Quan H. ;
Andersen, Stacey B. ;
Soyer, H. Peter ;
Frazer, Ian H. ;
Powell, Joseph E. .
JOURNAL OF INVESTIGATIVE DERMATOLOGY, 2018, 138 (12) :2558-2567
[19]  
McInnes L, 2020, Arxiv, DOI [arXiv:1802.03426, DOI 10.48550/ARXIV.1802.03426]
[20]  
Moussa Marmar, 2020, Bioinformatics Research and Applications. 16th International Symposium, ISBRA 2020. Proceedings. Lecture Notes in Bioinformatics. Subseries of Lecture Notes in Computer Science (LNBI 12304), P389, DOI 10.1007/978-3-030-57821-3_39