scholarly journals clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets

2018 ◽  
Author(s):  
Davide Risso ◽  
Liam Purvis ◽  
Russell Fletcher ◽  
Diya Das ◽  
John Ngai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Clustering Algorithms ◽  
Ensemble Methods ◽  
R Package ◽  
Model Organisms ◽  
Consensus Clustering ◽  
Tuning Parameters ◽  
Multiple Clusterings ◽  
Large Gene

AbstractClustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells.It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperimentprovides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers.The package clusterExperimentis publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.

scholarly journals Subpopulation identification for single-cell RNA-sequencing data using functional data analysis

2019 ◽  
Author(s):  
Kyungmin Ahn ◽  
Hironobu Fujiwara
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Gene Expression Data ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Clustering Algorithms ◽  
Expression Data ◽  
Clustering Methods ◽  
Single Cell Rna Sequencing

AbstractBackgroundIn single-cell RNA-sequencing (scRNA-seq) data analysis, a number of statistical tools in multivariate data analysis (MDA) have been developed to help analyze the gene expression data. This MDA approach is typically focused on examining discrete genomic units of genes that ignores the dependency between the data components. In this paper, we propose a functional data analysis (FDA) approach on scRNA-seq data whereby we consider each cell as a single function. To avoid a large number of dropouts (zero or zero-closed values) and reduce the high dimensionality of the data, we first perform a principal component analysis (PCA) and assign PCs to be the amplitude of the function. Then we use the index of PCs directly from PCA for the phase components. This approach allows us to apply FDA clustering methods to scRNA-seq data analysis.ResultsTo demonstrate the robustness of our method, we apply several existing FDA clustering algorithms to the gene expression data to improve the accuracy of the classification of the cell types against the conventional clustering methods in MDA. As a result, the FDA clustering algorithms achieve superior accuracy on simulated data as well as real data such as human and mouse scRNA-seq data.ConclusionsThis new statistical technique enhances the classification performance and ultimately improves the understanding of stochastic biological processes. This new framework provides an essentially different scRNA-seq data analytical approach, which can complement conventional MDA methods. It can be truly effective when current MDA methods cannot detect or uncover the hidden functional nature of the gene expression dynamics.

scholarly journals powerEQTL: An R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis

2020 ◽  
Author(s):  
Xianjun Dong ◽  
Xiaoqi Li ◽  
Tzuu-Wang Chang ◽  
Scott T Weiss ◽  
Weiliang Qiu
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Single Cell ◽  
Allele Frequency ◽  
R Package ◽  
Power Calculation ◽  
Eqtl Analysis ◽  
Genome Wide Association Studies ◽  
User Friendly ◽  
Bulk Tissue

Genome-wide association studies (GWAS) have revealed thousands of genetic loci for common diseases. One of the main challenges in the post-GWAS era is to understand the causality of the genetic variants. Expression quantitative trait locus (eQTL) analysis has been proven to be an effective way to address this question by examining the relationship between gene expression and genetic variation in a sufficiently powered cohort. However, it is often tricky to determine the sample size at which a variant with a specific allele frequency will be detected to associate with gene expression with sufficient power. This is particularly demanding with single-cell RNAseq studies. Therefore, a user-friendly tool to perform power analysis for eQTL at both bulk tissue and single-cell level will be critical. Here, we presented an R package called powerEQTL with flexible functions to calculate power, minimal sample size, or detectable minor allele frequency in both bulk tissue and single-cell eQTL analysis. A user-friendly, program-free web application is also provided, allowing customers to calculate and visualize the parameters interactively.

scholarly journals A systematic performance evaluation of clustering methods for single-cell RNA-seq data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1141 ◽  
Author(s):  
Angelo Duò ◽  
Mark D. Robinson ◽  
Charlotte Soneson
Keyword(s):  
Performance Evaluation ◽  
Single Cell ◽  
Clustering Algorithms ◽  
General Purpose ◽  
Data Sets ◽  
Consensus Clustering ◽  
Rna Seq ◽  
Clustering Methods ◽  
Run Time ◽  
The Stability

Subpopulation identification, usually via some form of unsupervised clustering, is a fundamental step in the analysis of many single-cell RNA-seq data sets. This has motivated the development and application of a broad range of clustering methods, based on various underlying algorithms. Here, we provide a systematic and extensible performance evaluation of 12 clustering algorithms, including both methods developed explicitly for scRNA-seq data and more general-purpose methods. The methods were evaluated using 9 publicly available scRNA-seq data sets as well as three simulations with varying degree of cluster separability. The same feature selection approaches were used for all methods, allowing us to focus on the investigation of the performance of the clustering algorithms themselves. We evaluated the ability of recovering known subpopulations, the stability and the run time of the methods. Additionally, we investigated whether the performance could be improved by generating consensus partitions from multiple individual clustering methods. We found substantial differences in the performance, run time and stability between the methods, with SC3 and Seurat showing the most favorable results. Additionally, we found that consensus clustering typically did not improve the performance compared to the best of the combined methods, but that several of the top-performing methods already perform some type of consensus clustering. The R scripts providing an extensible framework for the evaluation of new methods and data sets are available on GitHub (https://github.com/markrobinsonuzh/scRNAseq_clustering_comparison).

scholarly journals SCeQTL: an R package for identifying eQTL from single-cell parallel sequencing data

2018 ◽  
Author(s):  
Yue Hu ◽  
Xuegong Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
R Package ◽  
Eqtl Analysis ◽  
Sequencing Data ◽  
Parallel Sequencing ◽  
Single Cell Sequencing ◽  
Cell Data

With the development of single-cell sequencing technologies, parallel sequencing the transcriptome and genome is becoming available and will bring us the opportunity to uncover association between genotype and phenotype at single-cell level. Due to the special characteristics of single-cell sequencing data, new method is needed to identify eQTL from single-cell data. We developed an R package SCeQTL that uses zero-inflated negative binomial regression to do eQTL analysis on single-cell data. It can distinguish two type of gene-expression differences among different genotype groups. It can also be used for finding gene expression variations associated with other grouping factors like cell lineages or cell types.

scholarly journals LTMG: A novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

2018 ◽  
Author(s):  
Changlin Wan ◽  
Wennan Chang ◽  
Yu Zhang ◽  
Fenil Shah ◽  
Xiaoyu Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
R Package ◽  
Data Sets ◽  
Rna Seq ◽  
Cell Functions ◽  
Transcriptional Regulatory ◽  
A Cell

ABSTRACTA key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a gene’s diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

scholarly journals Nebulosa recovers single cell gene expression signals by kernel density estimation

2020 ◽  
Author(s):  
Jose Alquicira-Hernandez ◽  
Joseph E. Powell
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Dimensional Space ◽  
Kernel Density ◽  
R Package ◽  
Drop Out ◽  
Weighted Kernel ◽  
Low Dimensional

AbstractSummaryData sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualised in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression.Availability and implementationNebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa

scholarly journals Improving replicability in single-cell RNA-Seq cell type discovery with Dune

2020 ◽  
Author(s):  
Hector Roux de Bézieux ◽  
Kelly Street ◽  
Stephan Fischer ◽  
Koen Van den Berge ◽  
Rebecca Chance ◽  
...  
Keyword(s):  
Single Cell ◽  
Ad Hoc ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Optimal Choice ◽  
Number Of Clusters ◽  
Trade Off ◽  
Tuning Parameters ◽  
Original Dataset ◽  
Cluster Resolution

AbstractSingle-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable.Here, we propose a new method called Dune for optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets.

scholarly journals Normalization by distributional resampling of high throughput single-cell RNA-sequencing data

2020 ◽  
Author(s):  
Jared Brown ◽  
Zijian Ni ◽  
Chitrasen Mohanty ◽  
Rhonda Bacher ◽  
Christina Kendziorski
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Negative Binomial ◽  
R Package ◽  
Sequencing Depth ◽  
Sequencing Data ◽  
Specific Expression ◽  
Single Cell Rna Sequencing ◽  
Binomial Mixture

AbstractMotivationNormalization to remove technical or experimental artifacts is critical in the analysis of single-cell RNA-sequencing experiments, even those for which unique molecular identifiers (UMIs) are available. The majority of methods for normalizing single-cell RNA-sequencing data adjust average expression in sequencing depth, but allow the variance and other properties of the gene-specific expression distribution to be non-constant in depth, which often results in reduced power and increased false discoveries in downstream analyses. This problem is exacerbated by the high proportion of zeros present in most datasets.ResultsTo address this, we present Dino, a normalization method based on a flexible negative-binomial mixture model of gene expression. As demonstrated in both simulated and case study datasets, by normalizing the entire gene expression distribution, Dino is robust to shallow sequencing depth, sample heterogeneity, and varying zero proportions, leading to improved performance in downstream analyses in a number of settings.Availability and implementationThe R package, Dino, is available on GitHub at https://github.com/JBrownBiostat/[email protected], [email protected]

scholarly journals scLM: automatic detection of consensus gene clusters across multiple single-cell datasets

2020 ◽  
Author(s):  
Qianqian Song ◽  
Jing Su ◽  
Lance D. Miller ◽  
Wei Zhang
Keyword(s):  
Single Cell ◽  
Clustering Algorithm ◽  
Gene Clusters ◽  
R Package ◽  
Consensus Clustering ◽  
Multiple Sources ◽  
And Function ◽  
User Friendly ◽  
Improved Accuracy ◽  
Cell Data

AbstractIn gene expression profiling studies, including single-cell RNA-seq (scRNAseq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present scLM, a gene co-clustering algorithm tailored to single cell data that performs well at detecting gene clusters with significant biologic context. scLM can simultaneously cluster multiple single-cell datasets, i.e. consensus clustering, enabling users to leverage single cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variations without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-WF/scLM.

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