A rank-based marker selection method for high throughput scRNA-seq data

Abstract Background High throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner. Results We introduce RankCorr, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RankCorr proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RankCorr to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. Conclusions According to the metrics introduced in this work, RankCorr is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RankCorr is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RankCorr software is available for download at https://github.com/ahsv/RankCorrwith extensive documentation.

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Comparison of marker selection methods for high throughput scRNA-seq data

10.1101/679761 ◽

2019 ◽

Author(s):

Anna C. Gilbert ◽

Alexander Vargo

Keyword(s):

Performance Measures ◽

Synthetic Data ◽

Large Data ◽

Ground Truth ◽

Selection Method ◽

Large Data Sets ◽

Data Sets ◽

Selection Methods ◽

Marker Selection

AbstractHere, we evaluate the performance of a variety of marker selection methods on scRNA-seq UMI counts data. We test on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. In addition, we propose several performance measures for evaluating the quality of a set of markers when there is no known ground truth. According to these metrics, most existing marker selection methods show similar performance on experimental scRNA-seq data; thus, the speed of the algorithm is the most important consid-eration for large data sets. With this in mind, we introduce RANKCORR, a fast marker selection method with strong mathematical underpinnings that takes a step towards sensible multi-class marker selection.

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Comparison of high-throughput single-cell RNA sequencing data processing pipelines

Briefings in Bioinformatics ◽

10.1093/bib/bbaa116 ◽

2020 ◽

Author(s):

Mingxuan Gao ◽

Mingyi Ling ◽

Xinwei Tang ◽

Shun Wang ◽

Xu Xiao ◽

...

Keyword(s):

Data Processing ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Large Scale ◽

Evaluation Framework ◽

Integrated Analysis ◽

Sequencing Data ◽

Single Experiment ◽

Single Cell Rna Sequencing

Abstract With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

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HD Spot: Interpretable Deep Learning Classification of Single Cell Transcript Data

10.1101/822759 ◽

2019 ◽

Cited By ~ 1

Author(s):

Eric Prince ◽

Todd C. Hankinson

Keyword(s):

Deep Learning ◽

Single Cell ◽

High Throughput ◽

Ground Truth ◽

Sequencing Technologies ◽

Bioinformatic Tool ◽

Complex Relationships ◽

Insight Into ◽

Generation Sequencing

ABSTRACTHigh throughput data is commonplace in biomedical research as seen with technologies such as single-cell RNA sequencing (scRNA-seq) and other Next Generation Sequencing technologies. As these techniques continue to be increasingly utilized it is critical to have analysis tools that can identify meaningful complex relationships between variables (i.e., in the case of scRNA-seq: genes) in a way such that human bias is absent. Moreover, it is equally paramount that both linear and non-linear (i.e., one-to-many) variable relationships be considered when contrasting datasets. HD Spot is a deep learning-based framework that generates an optimal interpretable classifier a given high-throughput dataset using a simple genetic algorithm as well as an autoencoder to classifier transfer learning approach. Using four unique publicly available scRNA-seq datasets with published ground truth, we demonstrate the robustness of HD Spot and the ability to identify ontologically accurate gene lists for a given data subset. HD Spot serves as a bioinformatic tool to allow novice and advanced analysts to gain complex insight into their respective datasets enabling novel hypotheses development.

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Simultaneous Droplet Generation with In-Series Droplet T-Junctions Induced by Gravity-Induced Flow

Micromachines ◽

10.3390/mi12101211 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1211

Author(s):

Khashayar Bajgiran ◽

Alejandro Cordova ◽

Riad Elkhanoufi ◽

James Dorman ◽

Adam Melvin

Keyword(s):

Single Cell ◽

High Throughput ◽

Droplet Diameter ◽

Droplet Microfluidics ◽

Droplet Generation ◽

Single Experiment ◽

Flow Rates ◽

Wide Range ◽

Significant Difference ◽

Gravity Driven Flow

Droplet microfluidics offers a wide range of applications, including high-throughput drug screening and single-cell DNA amplification. However, these platforms are often limited to single-input conditions that prevent them from analyzing multiple input parameters (e.g., combined cellular treatments) in a single experiment. Droplet multiplexing will result in higher overall throughput, lowering cost of fabrication, and cutting down the hands-on time in number of applications such as single-cell analysis. Additionally, while lab-on-a-chip fabrication costs have decreased in recent years, the syringe pumps required for generating droplets of uniform shape and size remain cost-prohibitive for researchers interested in utilizing droplet microfluidics. This work investigates the potential of simultaneously generating droplets from a series of three in-line T-junctions utilizing gravity-driven flow to produce consistent, well-defined droplets. Implementing reservoirs with equal heights produced inconsistent flow rates that increased as a function of the distance between the aqueous inlets and the oil inlet. Optimizing the three reservoir heights identified that taller reservoirs were needed for aqueous inlets closer to the oil inlet. Studying the relationship between the ratio of oil-to-water flow rates () found that increasing resulted in smaller droplets and an enhanced droplet generation rate. An ANOVA was performed on droplet diameter to confirm no significant difference in droplet size from the three different aqueous inlets. The work described here offers an alternative approach to multiplexed droplet microfluidic devices allowing for the high-throughput interrogation of three sample conditions in a single device. It also has provided an alternative method to induce droplet formation that does not require multiple syringe pumps.

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Bayesian non-parametric clustering of single-cell mutation profiles

10.1101/2020.01.15.907345 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nico Borgsmüller ◽

Jose Bonet ◽

Francesco Marass ◽

Abel Gonzalez-Perez ◽

Nuria Lopez-Bigas ◽

...

Keyword(s):

Single Cell ◽

Dirichlet Process ◽

Tumor Heterogeneity ◽

Missing Values ◽

Parametric Method ◽

Simulated Data ◽

Error Rates ◽

Data Sets ◽

Dirichlet Process Mixture ◽

Non Parametric

AbstractThe high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intra-tumor heterogeneity by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq data sets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods. Here we introduce BnpC, a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles. BnpC employs a Dirichlet process mixture model coupled with a Markov chain Monte Carlo sampling scheme, including a modified split-merge move and a novel posterior estimator to predict clones and genotypes. We benchmarked our method comprehensively against state-of-the-art methods on simulated data using various data sizes, and applied it to three cancer scDNA-seq data sets. On simulated data, BnpC compared favorably against current methods in terms of accuracy, runtime, and scalability. Its inferred genotypes were the most accurate, and it was the only method able to run and produce results on data sets with 10,000 cells. On tumor scDNA-seq data, BnpC was able to identify clonal populations missed by the original cluster analysis but supported by supplementary experimental data. With ever growing scDNA-seq data sets, scalable and accurate methods such as BnpC will become increasingly relevant, not only to resolve intra-tumor heterogeneity but also as a pre-processing step to reduce data size. BnpC is freely available under MIT license at https://github.com/cbg-ethz/BnpC.

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Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

10.1101/2020.02.09.940221 ◽

2020 ◽

Cited By ~ 2

Author(s):

Mingxuan Gao ◽

Mingyi Ling ◽

Xinwei Tang ◽

Shun Wang ◽

Xu Xiao ◽

...

Keyword(s):

Data Processing ◽

Single Cell ◽

Rna Sequencing ◽

High Throughput ◽

Large Scale ◽

Evaluation Framework ◽

Integrated Analysis ◽

Sequencing Data ◽

Single Experiment ◽

Single Cell Rna Sequencing

AbstractWith the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

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Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning

10.1101/052225 ◽

2016 ◽

Cited By ~ 7

Author(s):

Bo Wang ◽

Junjie Zhu ◽

Emma Pierson ◽

Daniele Ramazzotti ◽

Serafim Batzoglou

Keyword(s):

Gene Expression ◽

Single Cell ◽

High Throughput ◽

Cell Populations ◽

Gene Expression Measurement ◽

Data Sets ◽

Similarity Learning ◽

Rna Seq ◽

High Level ◽

Cell Data

AbstractSingle-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical to identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. Here, we propose a novel similarity-learning framework, SIMLR (single-cell interpretation via multi-kernel learning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization applications. Benchmarking against state-of-the-art methods for these applications, we used SIMLR to re-analyse seven representative single-cell data sets, including high-throughput droplet-based data sets with tens of thousands of cells. We show that SIMLR greatly improves clustering sensitivity and accuracy, as well as the visualization and interpretability of the data.

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scSensitiveGeneDefine: sensitive gene detection in single-cell RNA sequencing data by Shannon entropy

BMC Bioinformatics ◽

10.1186/s12859-021-04136-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zechuan Chen ◽

Zeruo Yang ◽

Xiaojun Yuan ◽

Xiaoming Zhang ◽

Pei Hao

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Shannon Entropy ◽

Expression Patterns ◽

Ground Truth ◽

Unsupervised Clustering ◽

Gene Expression Patterns ◽

Data Sets ◽

Single Cell Rna Sequencing

Abstract Background Single-cell RNA sequencing (scRNA-seq) is the most widely used technique to obtain gene expression profiles from complex tissues. Cell subsets and developmental states are often identified via differential gene expression patterns. Most of the single-cell tools utilized highly variable genes to annotate cell subsets and states. However, we have discovered that a group of genes, which sensitively respond to environmental stimuli with high coefficients of variation (CV), might impose overwhelming influences on the cell type annotation. Result In this research, we developed a method, based on the CV-rank and Shannon entropy, to identify these noise genes, and termed them as “sensitive genes”. To validate the reliability of our methods, we applied our tools in 11 single-cell data sets from different human tissues. The results showed that most of the sensitive genes were enriched pathways related to cellular stress response. Furthermore, we noticed that the unsupervised result was closer to the ground-truth cell labels, after removing the sensitive genes detected by our tools. Conclusion Our study revealed the prevalence of stochastic gene expression patterns in most types of cells, compared the differences among cell marker genes, housekeeping genes (HK genes), and sensitive genes, demonstrated the similarities of functions of sensitive genes in various scRNA-seq data sets, and improved the results of unsupervised clustering towards the ground-truth labels. We hope our method would provide new insights into the reduction of data noise in scRNA-seq data analysis and contribute to the development of better scRNA-seq unsupervised clustering algorithms in the future.

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Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsity

10.1101/2021.04.02.438193 ◽

2021 ◽

Author(s):

Lisa Maria Steinheuer ◽

Sebastian Canzler ◽

Jörg Hackermüller

Keyword(s):

Single Cell ◽

Network Inference ◽

Cell Types ◽

Sparse Data ◽

Specific Cell ◽

Data Sets ◽

Data Imputation ◽

Gene Correlation ◽

Cell Type Specific ◽

Sparse Data Sets

AbstractGene correlation network inference from single-cell transcriptomics data potentially allows to gain unprecendented insights into cell type-specific regulatory programs. ScRNA-seq data is severely affected by dropout, which significantly hampers and restrains current downstream analysis. Although newly developed tools are capable to deal with sparse data, no appropriate single-cell network inference workflow has been established. A potential way to end this deadlock is the application of data imputation methods, which already proofed to be useful in specific contexts of single-cell data analysis, e.g., recovering cell clusters. In order to infer cell-type specific networks, two prerequisites must be met: the identification of cluster-specific cell-types and the network inference itself.Here, we propose a benchmarking framework to investigate both objections. By using suitable reference data with inherent correlation structure, six representative imputation tools and appropriate evaluation measures, we were able to systematically infer the impact of data imputation on network inference. Major network structures were found to be preserved in low dropout data sets. For moderately sparse data sets, DCA was able to recover gene correlation structures, although systematically introducing higher correlation values. No imputation tool was able to recover true signals from high dropout data. However, by using an additional biological data set we could show that cell-cell correlation by means of specific marker gene expression was not compromised through data imputation.Our analysis showed that network inference is feasible for low and moderately sparse data sets by using the unimputed and DCA-prepared data, respectively. High sparsity data, on the other side, still pose a major problem since current imputation techniques are not able to facilitate network inference. The annotation of cluster-specific cell-types as a prerequisite is not hampered by data imputation but their power to restore the deeply hidden correlation structures is still not sufficient enough.

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GeneTrail: A Framework for the Analysis of High-Throughput Profiles

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.716544 ◽

2021 ◽

Vol 8 ◽

Author(s):

Nico Gerstner ◽

Tim Kehl ◽

Kerstin Lenhof ◽

Lea Eckhart ◽

Lara Schneider ◽

...

Keyword(s):

Single Cell ◽

High Throughput ◽

Molecular Mechanisms ◽

State Of The Art ◽

High Dimensional ◽

Data Sets ◽

Biological Processes ◽

Data Set ◽

Rich Functionality ◽

Generation Sequencing

Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms.GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.

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