scholarly journals A Galaxy-based training resource for single-cell RNA-seq quality control and analyses

2019 ◽  
Author(s):  
Graham J Etherington ◽  
Nicola Soranzo ◽  
Suhaib Mohammed ◽  
Wilfried Haerty ◽  
Robert P Davey ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Single Cell ◽  
Command Line ◽  
Rna Seq ◽  
Analysis Tools ◽  
Training Materials ◽  
Data Production

AbstractBackgroundIt is not a trivial step to move from single-cell RNA-seq (scRNA-seq) data production to data analysis. There is a lack of intuitive training materials and easy-to-use analysis tools, and researchers can find it difficult to master the basics of scRNA-seq quality control and analysis.ResultsWe have developed a range of easy-to-use scripts, together with their corresponding Galaxy wrappers, that make scRNA-seq training and analysis accessible to researchers previously daunted by the prospect of scRNA-seq analysis. The simple command-line tools and the point-and-click nature of Galaxy makes it easy to assess, visualise, and quality control scRNA-seq data.ConclusionWe have developed a suite of scRNA-seq tools that can be used for both training and more in-depth analyses.

scholarly journals A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses

GigaScience ◽  
2019 ◽  
Vol 8 (12) ◽  
Author(s):  
Graham J Etherington ◽  
Nicola Soranzo ◽  
Suhaib Mohammed ◽  
Wilfried Haerty ◽  
Robert P Davey ◽  
...  
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Short Read Sequencing ◽  
Training Materials ◽  
Single Cell Rna Sequencing ◽  
Data Production ◽  
Sequencing Quality ◽  
Exchange Data

Abstract Background It is not a trivial step to move from single-cell RNA-sequencing (scRNA-seq) data production to data analysis. There is a lack of intuitive training materials and easy-to-use analysis tools, and researchers can find it difficult to master the basics of scRNA-seq quality control and the later analysis. Results We have developed a range of practical scripts, together with their corresponding Galaxy wrappers, that make scRNA-seq training and quality control accessible to researchers previously daunted by the prospect of scRNA-seq analysis. We implement a “visualize-filter-visualize” paradigm through simple command line tools that use the Loom format to exchange data between the tools. The point-and-click nature of Galaxy makes it easy to assess, visualize, and filter scRNA-seq data from short-read sequencing data. Conclusion We have developed a suite of scRNA-seq tools that can be used for both training and more in-depth analyses.

scholarly journals User-friendly, scalable tools and workflows for single-cell analysis

2020 ◽  
Author(s):  
P. Moreno ◽  
N. Huang ◽  
J.R. Manning ◽  
S. Mohammed ◽  
A. Solovyev ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Programming Languages ◽  
Single Cell Analysis ◽  
Command Line ◽  
Cell Analysis ◽  
Rna Seq ◽  
Interactive Analysis ◽  
User Friendly ◽  
Analysis Environment

AbstractSingle-cell RNA-Seq (scRNA-Seq) data analysis requires expertise in command-line tools, programming languages and scaling on compute infrastructure. As scRNA-Seq becomes widespread, computational pipelines need to be more accessible, simpler and scalable. We introduce an interactive analysis environment for scRNA-Seq, based on Galaxy, with ~70 functions from major single-cell analysis tools, which can be run on compute clusters, cloud providers or single machines, to bring compute to the data in scRNA-Seq.

Joint CC and Bimax: A Biclustering Method for Single-Cell RNA-Seq Data Analysis

2021 ◽  
pp. 499-510
Author(s):  
He-Ming Chu ◽  
Xiang-Zhen Kong ◽  
Jin-Xing Liu ◽  
Juan Wang ◽  
Sha-Sha Yuan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Rna Seq

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2020 ◽  
Vol 30 (4) ◽  
pp. 611-621 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Data Analysis ◽  
Single Cell ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Rna Seq

scholarly journals Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

2020 ◽  
Vol 14 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Xin Zou ◽  
Ke Chen ◽  
Jiawei Zou ◽  
Peiyi Han ◽  
Jie Hao ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Potential Risk ◽  
Rna Seq ◽  
Human Organs

scholarly journals Full-Length Transcriptome: A Reliable Alternative for Single-Cell RNA-Seq Analysis in the Spleen of Teleost Without Reference Genome

2021 ◽  
Vol 12 ◽  
Author(s):  
Lixing Huang ◽  
Ying Qiao ◽  
Wei Xu ◽  
Linfeng Gong ◽  
Rongchao He ◽  
...  
Keyword(s):  
Data Analysis ◽  
B Cells ◽  
Single Cell ◽  
Reference Genome ◽  
Immune Cell ◽  
Cell Types ◽  
Full Length ◽  
Cell Populations ◽  
Epinephelus Coioides ◽  
Rna Seq

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.

scholarly journals SCOUT: Single-cell outlier analysis in cancer

2020 ◽  
Author(s):  
Giovana Ravizzoni Onzi ◽  
Juliano Luiz Faccioni ◽  
Alvaro G. Alvarado ◽  
Paula Andreghetto Bracco ◽  
Harley I. Kornblum ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Biological Markers ◽  
Rna Seq ◽  
Outlier Analysis ◽  
Mass Cytometry ◽  
Wide Range ◽  
Cell Data

Outliers are often ignored or even removed from data analysis. In cancer, however, single outlier cells can be of major importance, since they have uncommon characteristics that may confer capacity to invade, metastasize, or resist to therapy. Here we present the Single-Cell OUTlier analysis (SCOUT), a resource for single-cell data analysis focusing on outlier cells, and the SCOUT Selector (SCOUTS), an application to systematically apply SCOUT on a dataset over a wide range of biological markers. Using publicly available datasets of cancer samples obtained from mass cytometry and single-cell RNA-seq platforms, outlier cells for the expression of proteins or RNAs were identified and compared to their non-outlier counterparts among different samples. Our results show that analyzing single-cell data using SCOUT can uncover key information not easily observed in the analysis of the whole population.

scholarly journals Locality Sensitive Imputation for Single-Cell RNA-Seq Data

2018 ◽  
Author(s):  
Marmar Moussa ◽  
Ion I. Măndoiu
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Comprehensive Assessment ◽  
Sequencing Depth ◽  
Drop Out ◽  
Rna Seq ◽  
Imputation Methods ◽  
Drop Outs ◽  
Random Nature ◽  
Imputation Approach

AbstractOne of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of drop-outs, however, makes it possible to consider imputation methods as means of correcting for drop-outs. In this paper we study some existing scRNA-Seq imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present the results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq datasets with varying per cell sequencing depth.

Sign in / Sign up

Export Citation Format

Share Document