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Quantitative assessment of single-cell RNA-sequencing methods.

ABSTRACT: Interest in single-cell whole-transcriptome analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. We compared commercially available single-cell RNA amplification methods with both microliter and nanoliter volumes, using sequence from bulk total RNA and multiplexed quantitative PCR as benchmarks to systematically evaluate the sensitivity and accuracy of various single-cell RNA-seq approaches. We show that single-cell RNA-seq can be used to perform accurate quantitative transcriptome measurement in individual cells with a relatively small number of sequencing reads and that sequencing large numbers of single cells can recapitulate bulk transcriptome complexity.

SUBMITTER: Wu AR

PROVIDER: S-EPMC4022966 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Quantitative assessment of single-cell RNA-sequencing methods.

Wu Angela R AR Neff Norma F NF Kalisky Tomer T Dalerba Piero P Treutlein Barbara B Rothenberg Michael E ME Mburu Francis M FM Mantalas Gary L GL Sim Sopheak S Clarke Michael F MF Quake Stephen R SR

Nature methods 20131020 1

Interest in single-cell whole-transcriptome analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. We compared commercially available single-cell RNA amplification methods with both microliter and nanoliter volumes, using sequence from bulk total RNA and multiplexed quantitative PCR as benchmarks to systematically evaluate the sensitivity and accuracy of various single-cell RNA-seq approaches. We show that single-cell RNA-seq can be used to perform accu ...[more]

PMID: 24141493

Similar Datasets

Quantitative assessment of single-cell RNA sequencing methods

Project description:We generated single-cell transcriptomes from a large number of single cells using several commercially available platforms, in both microliter and nanoliter volumes, and compared performance between them. We benchmarked each method to conventional RNA-seq of the same sample using bulk total RNA, as well as to multiplexed qPCR, which is the current gold standard for quantitative single-cell gene expression analysis. In doing so, we were able to systematically evaluate the sensitivity, precision, and accuracy of various approaches to single-cell RNA-seq. Our results show that it is possible to use single-cell RNA-seq to perform quantitative transcriptome measurements of individual cells, that it is possible to obtain quantitative and accurate gene expression measurements with a relatively small number of sequencing reads, and that when such measurements are performed on large numbers of cells, one can recapitulate the bulk transcriptome complexity, and the distributions of gene expression levels found by single-cell qPCR. 109 single-cell human transcriptomes were analyzed in total; 96 using nanoliter volume sample processing on a microfluidic platform, Nextera library prep (biological replicates); 3 using the SMARTer cDNA synthesis kit, Nextera library prep (biological replicates); 3 using the Transplex cDNA synthesis kit, Nextera library prep (biological replicates); 7 using the Ovation Nugen cDNA synthesis kit (biological replicates) where 3 used Nextera library prep and 4 used NEBNext library prep. In addition, 4 bulk RNA samples were sequenced: bulk RNA generated using ~1 million pooled cells was used to make bulk libraries, 2 of which were made using SMARTer cDNA synthesis kit (technical replicates) and 2 made using Superscript RT kit with no amplification (technical replicates). All 4 bulk samples were made into libraries using Nextera.

2013-10-20 | E-GEOD-51254 | biostudies-arrayexpress

Quantitative assessment of single-cell RNA sequencing methods

2013-10-20 | GSE51254 | GEO

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods.

Project description:The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have been proposed, many of them with the purpose of performing differentially expression analysis. In this work, we compare four different tools for single-cell RNA-sequencing differential expression, together with two popular methods originally developed for the analysis of bulk RNA-sequencing data, but largely applied to single-cell data. We discuss results obtained on two real and one synthetic dataset, along with considerations about the perspectives of single-cell differential expression analysis. In particular, we explore the methods performance in four different scenarios, mimicking different unimodal or bimodal distributions of the data, as characteristic of single-cell transcriptomics. We observed marked differences between the selected methods in terms of precision and recall, the number of detected differentially expressed genes and the overall performance. Globally, the results obtained in our study suggest that is difficult to identify a best performing tool and that efforts are needed to improve the methodologies for single-cell RNA-sequencing data analysis and gain better accuracy of results.

| S-EPMC5440469 | biostudies-literature

Assessment of Single Cell RNA-Seq Normalization Methods.

Project description:We have assessed the performance of seven normalization methods for single cell RNA-seq using data generated from dilution of RNA samples. Our analyses showed that methods considering spike-in External RNA Control Consortium (ERCC) RNA molecules significantly outperformed those not considering ERCCs. This work provides a guidance of selecting normalization methods to remove technical noise in single cell RNA-seq data.

| S-EPMC5499114 | biostudies-other

Automatic cell type identification methods for single-cell RNA sequencing.

Project description:Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for scientists of many research disciplines due to its ability to elucidate the heterogeneous and complex cell-type compositions of different tissues and cell populations. Traditional cell-type identification methods for scRNA-seq data analysis are time-consuming and knowledge-dependent for manual annotation. By contrast, automatic cell-type identification methods may have the advantages of being fast, accurate, and more user friendly. Here, we discuss and evaluate thirty-two published automatic methods for scRNA-seq data analysis in terms of their prediction accuracy, F1-score, unlabeling rate and running time. We highlight the advantages and disadvantages of these methods and provide recommendations of method choice depending on the available information. The challenges and future applications of these automatic methods are further discussed. In addition, we provide a free scRNA-seq data analysis package encompassing the discussed automatic methods to help the easy usage of them in real-world applications.

| S-EPMC8572862 | biostudies-literature

Assessing characteristics of RNA amplification methods for single cell RNA sequencing.

Project description:Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known.Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate measurements to be quantitative at an expression level greater than ~5-10 molecules.Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

| S-EPMC5122016 | biostudies-literature

Systematic comparison of single-cell and single-nucleus RNA-sequencing methods.

Project description:The scale and capabilities of single-cell RNA-sequencing methods have expanded rapidly in recent years, enabling major discoveries and large-scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single-cell and/or single-nucleus profiling-selecting representative methods based on their usage and our expertise and resources to prepare libraries-including two low-throughput and five high-throughput methods. We tested the methods on three types of samples: cell lines, peripheral blood mononuclear cells and brain tissue, generating 36 libraries in six separate experiments in a single center. To directly compare the methods and avoid processing differences introduced by the existing pipelines, we developed scumi, a flexible computational pipeline that can be used with any single-cell RNA-sequencing method. We evaluated the methods for both basic performance, such as the structure and alignment of reads, sensitivity and extent of multiplets, and for their ability to recover known biological information in the samples.

| S-EPMC7289686 | biostudies-literature

Statistical methods for analysis of single-cell RNA-sequencing data.

Project description:Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput genomic technology used to study the expression dynamics of genes at single-cell level. Analyzing the scRNA-seq data in presence of biological confounding factors including dropout events is a challenging task. Thus, this article presents a novel statistical approach for various analyses of the scRNA-seq Unique Molecular Identifier (UMI) counts data. The various analyses include modeling and fitting of observed UMI data, cell type detection, estimation of cell capture rates, estimation of gene specific model parameters, estimation of the sample mean and sample variance of the genes, etc. Besides, the developed approach is able to perform differential expression, and other downstream analyses that consider the molecular capture process in scRNA-seq data modeling. Here, the external spike-ins data can also be used in the approach for better results. The unique feature of the method is that it considers the biological process that leads to severe dropout events in modeling the observed UMI counts of genes. • The differential expression analysis of observed scRNA-seq UMI counts data is performed after adjustment for cell capture rates. • The statistical approach performs downstream differential zero inflation analysis, classification of influential genes, and selection of top marker genes. • Cell auxiliaries including cell clusters and other cell variables (e.g., cell cycle, cell phase) are used to remove unwanted variation to perform statistical tests reliably.

| S-EPMC8720898 | biostudies-literature

Clustering and classification methods for single-cell RNA-sequencing data.

Project description:Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.

| S-EPMC7444317 | biostudies-literature

A systematic evaluation of single-cell RNA-sequencing imputation methods.

Project description:BackgroundThe rapid development of single-cell RNA-sequencing (scRNA-seq) technologies has led to the emergence of many methods for removing systematic technical noises, including imputation methods, which aim to address the increased sparsity observed in single-cell data. Although many imputation methods have been developed, there is no consensus on how methods compare to each other.ResultsHere, we perform a systematic evaluation of 18 scRNA-seq imputation methods to assess their accuracy and usability. We benchmark these methods in terms of the similarity between imputed cell profiles and bulk samples and whether these methods recover relevant biological signals or introduce spurious noise in downstream differential expression, unsupervised clustering, and pseudotemporal trajectory analyses, as well as their computational run time, memory usage, and scalability. Methods are evaluated using data from both cell lines and tissues and from both plate- and droplet-based single-cell platforms.ConclusionsWe found that the majority of scRNA-seq imputation methods outperformed no imputation in recovering gene expression observed in bulk RNA-seq. However, the majority of the methods did not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. In addition, we found substantial variability in the performance of the methods within each evaluation aspect. Overall, MAGIC, kNN-smoothing, and SAVER were found to outperform the other methods most consistently.

| S-EPMC7450705 | biostudies-literature

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