scholarly journals A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines

2019 ◽  
Author(s):  
Beate Vieth ◽  
Swati Parekh ◽  
Christoph Ziegenhain ◽  
Wolfgang Enard ◽  
Ines Hellmann
Keyword(s):  
Best Practices ◽  
Sample Size ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Systematic Evaluation ◽  
Library Preparation ◽  
Rna Seq ◽  
Rapid Spread ◽  
Single Cell Rna Sequencing

AbstractThe recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established, yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ∼ 3,000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

scholarly journals A systematic evaluation of single cell RNA-seq analysis pipelines

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Beate Vieth ◽  
Swati Parekh ◽  
Christoph Ziegenhain ◽  
Wolfgang Enard ◽  
Ines Hellmann
Keyword(s):  
Best Practices ◽  
Sample Size ◽  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Systematic Evaluation ◽  
Library Preparation ◽  
Rna Seq ◽  
Rapid Spread ◽  
Single Cell Rna Sequencing

Abstract The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

scholarly journals A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1947
Author(s):  
Samarendra Das ◽  
Anil Rai ◽  
Michael L. Merchant ◽  
Matthew C. Cave ◽  
Shesh N. Rai
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Differential Expression Analysis ◽  
Individual Performance ◽  
Rna Seq ◽  
Gene Expressions ◽  
Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

scholarly journals A Systematic Evaluation of Single-cell RNA-sequencing Imputation Methods

2020 ◽  
Author(s):  
Wenpin Hou ◽  
Zhicheng Ji ◽  
Hongkai Ji ◽  
Stephanie C. Hicks
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Rapid Development ◽  
Systematic Evaluation ◽  
Rna Seq ◽  
Experimental Protocol ◽  
Improve Performance ◽  
Imputation Methods ◽  
Single Cell Rna Sequencing ◽  
Experimental Protocols

ABSTRACTThe rapid development of single-cell RNA-sequencing (scRNA-seq) technology, with increased sparsity compared to bulk RNA-sequencing (RNA-seq), has led to the emergence of many methods for preprocessing, including imputation methods. Here, we systematically evaluate the performance of 18 state-of-the-art scRNA-seq imputation methods using cell line and tissue data measured across experimental protocols. Specifically, we assess the similarity of imputed cell profiles to bulk samples as well as investigate whether methods recover relevant biological signals or introduce spurious noise in three downstream analyses: differential expression, unsupervised clustering, and inferring pseudotemporal trajectories. Broadly, we found significant variability in the performance of the methods across evaluation settings. While most scRNA-seq imputation methods recover biological expression observed in bulk RNA-seq data, the majority of the methods do not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. Furthermore, we find that the performance of scRNA-seq imputation methods depends on many factors including the experimental protocol, the sparsity of the data, the number of cells in the dataset, and the magnitude of the effect sizes. We summarize our results and provide a key set of recommendations for users and investigators to navigate the current space of scRNA-seq imputation methods.

scholarly journals Simulation, power evaluation and sample size recommendation for single-cell RNA-seq

2020 ◽  
Vol 36 (19) ◽  
pp. 4860-4868
Author(s):  
Kenong Su ◽  
Zhijin Wu ◽  
Hao Wu
Keyword(s):  
Sample Size ◽  
Single Cell ◽  
Rna Sequencing ◽  
Statistical Significance ◽  
Sample Size Calculation ◽  
Supplementary Information ◽  
Design Stage ◽  
Rna Seq ◽  
Single Cell Rna Sequencing ◽  
Power Evaluation

Abstract Motivation Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. Results We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA-sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. The data simulator in POWSC outperforms two other state-of-art simulators in capturing key characteristics of real datasets. The power assessor in POWSC provides a variety of power evaluations including stratified and marginal power analyses for DEs characterized by two forms (phase transition or magnitude tuning), under different comparison scenarios. In addition, POWSC offers information for optimizing the tradeoffs between sample size and sequencing depth with the same total reads. Availability and implementation POWSC is an open-source R package available online at https://github.com/suke18/POWSC. Supplementary information Supplementary data are available at Bioinformatics online.

Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes and cell compositions

2019 ◽  
Vol 18 (5) ◽  
Author(s):  
Aslı Suner
Keyword(s):  
Performance Evaluation ◽  
Sample Size ◽  
Single Cell ◽  
Rna Sequencing ◽  
Performance Measures ◽  
Systematic Evaluation ◽  
Expression Data ◽  
Clustering Methods ◽  
Sample Sizes ◽  
Single Cell Rna Sequencing

Abstract A number of specialized clustering methods have been developed so far for the accurate analysis of single-cell RNA-sequencing (scRNA-seq) expression data, and several reports have been published documenting the performance measures of these clustering methods under different conditions. However, to date, there are no available studies regarding the systematic evaluation of the performance measures of the clustering methods taking into consideration the sample size and cell composition of a given scRNA-seq dataset. Herein, a comprehensive performance evaluation study of 11 selected scRNA-seq clustering methods was performed using synthetic datasets with known sample sizes and number of subpopulations, as well as varying levels of transcriptome complexity. The results indicate that the overall performance of the clustering methods under study are highly dependent on the sample size and complexity of the scRNA-seq dataset. In most of the cases, better clustering performances were obtained as the number of cells in a given expression dataset was increased. The findings of this study also highlight the importance of sample size for the successful detection of rare cell subpopulations with an appropriate clustering tool.

scholarly journals Comparative analysis of single-cell RNA Sequencing Platforms and Methods

2020 ◽  
Author(s):  
John M. Ashton ◽  
Hubert Rehrauer ◽  
Jason Myers ◽  
Jacqueline Myers ◽  
Michelle Zanche ◽  
...  
Keyword(s):  
Best Practices ◽  
Single Cell ◽  
Rna Sequencing ◽  
Histone Deacetylase Inhibitor ◽  
Biological Processes ◽  
Rna Seq ◽  
Deacetylase Inhibitor ◽  
Single Cell Rna Sequencing ◽  
Sequencing Platforms ◽  
Single Instrument

ABSTRACTSingle-cell RNA sequencing (scRNA-seq) offers great new opportunities for increasing our understanding of complex biological processes. In particular, development of an accurate Human Cell Atlas is largely dependent on the rapidly advancing technologies and molecular chemistries employed in scRNA-seq. These advances have already allowed an increase in throughput for scRNA-seq from 96 to 80,000 cells on a single instrument run by capturing cells within nano-liter droplets. While this increase in throughput is critical for many experimental questions, a thorough comparison between microfluidic-, plate-, and droplet-based technologies or between multiple available platforms utilizing these technologies is largely lacking. Here, we report scRNA-seq data from SUM149PT cells treated with the histone deacetylase inhibitor TSA vs. untreated controls across several scRNA-seq platforms (Fluidigm C1, WaferGen iCell8, 10X Genomics Chromium Controller, and Illumina/BioRad ddSEQ). The primary goal of this project was to demonstrate RNA sequencing (RNA-seq) methods for profiling the ultra-low amounts of RNA present in individual cells, and this report discusses the results of the study as well as technical challenges/lesson learned and present general guidelines for best practices in sample preparation and analysis.

scholarly journals UMI-count modeling and differential expression analysis for single-cell RNA sequencing

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Wenan Chen ◽  
Yan Li ◽  
John Easton ◽  
David Finkelstein ◽  
Gang Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Cell Rna Sequencing

scholarly journals SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

2018 ◽  
Author(s):  
Xianwen Ren ◽  
Liangtao Zheng ◽  
Zemin Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Random Projection ◽  
Rna Seq ◽  
Sequencing Data ◽  
Computational Framework ◽  
Human Blood Cells ◽  
Single Cell Rna Sequencing ◽  
Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

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