Quality Control of Single-Cell RNA-seq

2019 ◽  
pp. 1-9
Author(s):  
Peng Jiang
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Rna Seq

scholarly journals Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

2017 ◽  
pp. btw777 ◽  
Author(s):  
Davis J. McCarthy ◽  
Kieran R. Campbell ◽  
Aaron T. L. Lun ◽  
Quin F. Wills
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Processing Quality ◽  
Rna Seq

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 Sharq, A versatile preprocessing and QC pipeline for Single Cell RNA-seq

2018 ◽  
Author(s):  
Tito Candelli ◽  
Philip Lijnzaad ◽  
Mauro J Muraro ◽  
Hindrik Kerstens ◽  
Patrick Kemmeren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quality Control ◽  
Single Cell ◽  
Hierarchical Model ◽  
Live Cells ◽  
Rna Seq ◽  
The Individual ◽  
Innovative Approaches

AbstractDespite the meteoric rise of single cell RNA-seq, only a few preprocessing pipelines exist that are able to perform all steps from the original fastq files to a gene expression table ready for further analysis. Here we present Sharq, a versatile preprocessing pipeline designed to work with plate-based 3’-end protocols that include Unique Molecular Identifiers (UMIs). Sharq performs stringent step-wise trimming of reads, assigns them to features according to a flexible hierarchical model, and uses the barcode and UMI information to avoid amplification biases and produce gene expression tables. Additionally, Sharq provides an extensive plate diagnostics report for quality control and troubleshooting, including that of spatial artefacts. The diagnostics report includes measures of the quality of the individual plate wells as well as a robust assessment which of them contain material from live cells. Collectively, the innovative approaches presented here provide a valuable tool for processing and quality control of single cell RNA-seq data.

scholarly journals scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R

2016 ◽  
Author(s):  
Davis J. McCarthy ◽  
Kieran R. Campbell ◽  
Aaron T. L. Lun ◽  
Quin F. Wills
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Sequence Data ◽  
Supplementary Information ◽  
Processing Quality ◽  
Rna Seq ◽  
Study Gene Expression ◽  
Supplementary Material ◽  
Downstream Analysis ◽  
Cell Data

AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts, and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalisation.ResultsWe have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalisation and visualisation of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development.AvailabilityThe open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater.Supplementary informationSupplementary material is available online at bioRxiv accompanying this manuscript, and all materials required to reproduce the results presented in this paper are available at dx.doi.org/10.5281/zenodo.60139.

scholarly journals Quality control of single-cell RNA-seq by SinQC

2016 ◽  
Vol 32 (16) ◽  
pp. 2514-2516 ◽  
Author(s):  
Peng Jiang ◽  
James A. Thomson ◽  
Ron Stewart
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Rna Seq

scholarly journals SampleQC: robust multivariate, multi-celltype, multi-sample quality control for single cell data

2021 ◽  
Author(s):  
Will Macnair ◽  
Mark D Robinson
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Real Data ◽  
R Package ◽  
Gaussian Mixture ◽  
Model Fit ◽  
Rna Seq ◽  
Industry Standard ◽  
Multiple Samples ◽  
Cell Data

Quality control (QC) is a critical component of single cell RNA-seq processing pipelines. Many single cell methods assume that scRNA-seq data comprises multiple celltypes that are distinct in terms of gene expression, however this is not reflected in current approaches to QC. We show that the current widely-used methods for QC may have a bias towards exclusion of rarer celltypes, especially those whose QC metrics are more extreme, e.g. those with naturally high mitochondrial proportions. We introduce SampleQC, which improves sensitivity and reduces bias relative to current industry standard approaches, via a robust Gaussian mixture model fit across multiple samples simultaneously. We show via simulations that SampleQC is less susceptible than other methods to exclusion of rarer celltypes. We also demonstrate SampleQC on complex real data, comprising up to 867k cells over 172 samples. The framework for SampleQC is general, and has applications as an outlier detection method for data beyond single cell RNA-seq. SampleQC is parallelized and implemented in Rcpp, and is available as an R package.

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