SCell: integrated analysis of single-cell RNA-seq data

被引：30

作者：

Diaz, Aaron ^{[1
,2
]}

Liu, Siyuan J. ^{[2
]}

Sandoval, Carmen ^{[2
]}

Pollen, Alex ^{[2
]}

Nowakowski, Tom J. ^{[2
]}

Lim, Daniel A. ^{[1
,2
,3
]}

Kriegstein, Arnold ^{[2
]}

机构：

[1] UCSF, Dept Neurol Surg, San Francisco, CA 94143 USA

[2] Eli & Edythe Broad Ctr Regenerat Med & Stem Cell, San Francisco, CA USA

[3] Vet Affairs Med Ctr, San Francisco, CA 94121 USA

来源：

BIOINFORMATICS | 2016年 / 32卷 / 14期

关键词：

SAMPLES;

D O I：

10.1093/bioinformatics/btw201

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Analysis of the composition of heterogeneous tissue has been greatly enabled by recent developments in single-cell transcriptomics. We present SCell, an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface. Scripts and protocols for the high-throughput pre-processing of large ensembles of single-cell, RNA-seq datasets are provided as an additional resource. Availability and Implementation: Binary executables for Windows, MacOS and Linux are available at http://sourceforge.net/projects/scell, source code and pre-processing scripts are available from https://github.com/diazlab/SCell.

引用

页码：2219 / 2220

页数：2

共 50 条

[1] Analysis of Single-Cell RNA-seq Data by Clustering Approaches
Zhu, Xiaoshu
Li, Hong-Dong
Guo, Lilu
Wu, Fang-Xiang
Wang, Jianxin
[J]. CURRENT BIOINFORMATICS, 2019, 14 (04) : 314 - 322
[2] Computational analysis of alternative polyadenylation from standard RNA-seq and single-cell RNA-seq data
Gao, Yipeng
Li, Wei
[J]. MRNA 3' END PROCESSING AND METABOLISM, 2021, 655 : 225 - 243
[3] SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
Peng, Tao
Zhu, Qin
Yin, Penghang
Tan, Kai
[J]. GENOME BIOLOGY, 2019, 20 (1)
[4] SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
Tao Peng
Qin Zhu
Penghang Yin
Kai Tan
[J]. Genome Biology, 20
[5] Comparison of transformations for single-cell RNA-seq data
Constantin Ahlmann-Eltze
Wolfgang Huber
[J]. Nature Methods, 2023, 20 : 665 - 672
[6] A web server for comparative analysis of single-cell RNA-seq data
Amir Alavi
Matthew Ruffalo
Aiyappa Parvangada
Zhilin Huang
Ziv Bar-Joseph
[J]. Nature Communications, 9
[7] Tumor genetic analysis from single-cell RNA-seq data
Nawy, Tal
[J]. NATURE METHODS, 2018, 15 (07) : 571 - 571
[8] A web server for comparative analysis of single-cell RNA-seq data
Alavi, Amir
Ruffalo, Matthew
Parvangada, Aiyappa
Huang, Zhilin
Bar-Joseph, Ziv
[J]. NATURE COMMUNICATIONS, 2018, 9
[9] Practical bioinformatics pipelines for single-cell RNA-seq data analysis
Jiangping He
Lihui Lin
Jiekai Chen
[J]. Biophysics Reports, 2022, 8 (03) : 158 - 169
[10] Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
Chen, Geng
Ning, Baitang
Shi, Tieliu
[J]. FRONTIERS IN GENETICS, 2019, 10

← 1 2 3 4 5 →