GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution

Abstract Motivation Many methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and/or over-confident gene clusters. Results The proposed method, GPseudoClust, is a novel approach that jointly infers pseudotemporal ordering and gene clusters, and quantifies the uncertainty in both. GPseudoClust combines a recent method for pseudotime inference with nonparametric Bayesian clustering methods, efficient MCMC sampling, and novel subsampling strategies which aid computation.We consider a broad array of simulated and experimental datasets to demonstrate the effectiveness of GPseudoClust in a range of settings. Availability An implementation is available on GitHub: https://github.com/magStra/nonparametricSummaryPSM and https://github.com/magStra/GPseudoClust. Supplementary Information Supplementary data are available at Bioinformatics online.

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GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution

10.1101/567115 ◽

2019 ◽

Author(s):

Magdalena E Strauss ◽

Paul DW Kirk ◽

John E Reid ◽

Lorenz Wernisch

Keyword(s):

Single Cell ◽

Time Course ◽

Gene Clusters ◽

Supplementary Information ◽

Clustering Methods ◽

Link Type ◽

Novel Approach ◽

Broad Array ◽

Recent Method ◽

Cell Data

AbstractMotivationMany methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data. However, pseudotime estimates are subject to high levels of uncertainty, and failing to account for this uncertainty is liable to lead to erroneous and/or over-confident gene clusters.ResultsThe proposed method, GPseudoClust, is a novel approach that jointly infers pseudotem-poral ordering and gene clusters, and quantifies the uncertainty in both. GPseudoClust combines a recent method for pseudotime inference with nonparametric Bayesian clustering methods, efficient MCMC sampling, and novel subsampling strategies which aid computation. We consider a broad array of simulated and experimental datasets to demonstrate the effectiveness of GPseudoClust in a range of settings.AvailabilityAn implementation is available on GitHub: https://github.com/magStra/nonparametricSummaryPSM and https://github.com/magStra/[email protected] informationSupplementary materials are available.

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Uncovering the Gene Regulatory Networks Underlying Macrophage Polarization Through Comparative Analysis of Bulk and Single-Cell Data

10.1101/2021.01.20.427499 ◽

2021 ◽

Author(s):

Klebea Carvalho ◽

Elisabeth Rebboah ◽

Camden Jansen ◽

Katherine Williams ◽

Andrew Dowey ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Course ◽

Macrophage Polarization ◽

Cell Polarization ◽

Rna Seq ◽

Gene Regulatory ◽

Cell Subpopulations ◽

Cell Data

SummaryGene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.

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Optimal Gene Filtering for Single-Cell data (OGFSC)—a gene filtering algorithm for single-cell RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/bty1016 ◽

2018 ◽

Vol 35 (15) ◽

pp. 2602-2609 ◽

Cited By ~ 3

Author(s):

Jie Hao ◽

Wei Cao ◽

Jian Huang ◽

Xin Zou ◽

Ze-Guang Han

Keyword(s):

Single Cell ◽

Supplementary Information ◽

Rna Seq ◽

Aging Research ◽

Technical Noise ◽

Transcriptomic Data ◽

Knowledge Based ◽

Gene Filtering ◽

Cell Data ◽

Gene Expression Levels

Abstract Motivation Single-cell transcriptomic data are commonly accompanied by extremely high technical noise due to the low RNA concentrations from individual cells. Precise identification of differentially expressed genes and cell populations are heavily dependent on the effective reduction of technical noise, e.g. by gene filtering. However, there is still no well-established standard in the current approaches of gene filtering. Investigators usually filter out genes based on single fixed threshold, which commonly leads to both over- and under-stringent errors. Results In this study, we propose a novel algorithm, termed as Optimal Gene Filtering for Single-Cell data, to construct a thresholding curve based on gene expression levels and the corresponding variances. We validated our method on multiple single-cell RNA-seq datasets, including simulated and published experimental datasets. The results show that the known signal and known noise are reliably discriminated in the simulated datasets. In addition, the results of seven experimental datasets demonstrate that these cells of the same annotated types are more sharply clustered using our method. Interestingly, when we re-analyze the dataset from an aging research recently published in Science, we find a list of regulated genes which is different from that reported in the original study, because of using different filtering methods. However, the knowledge based on our findings better matches the progression of immunosenescence. In summary, we here provide an alternative opportunity to probe into the true level of technical noise in single-cell transcriptomic data. Availability and implementation https://github.com/XZouProjects/OGFSC.git Supplementary information Supplementary data are available at Bioinformatics online.

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SCHNEL: Scalable clustering of high dimensional single-cell data

10.1101/2020.03.30.015925 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tamim Abdelaal ◽

Paul de Raadt ◽

Boudewijn P.F. Lelieveldt ◽

Marcel J.T. Reinders ◽

Ahmed Mahfouz

Keyword(s):

Single Cell ◽

Graph Clustering ◽

Original Data ◽

Large Datasets ◽

Supplementary Information ◽

High Dimensional ◽

Sequencing Data ◽

Novel Approach ◽

Cellular Markers ◽

Cell Data

AbstractMotivationSingle cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.ResultsWe developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable timeframes. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST.Availability and ImplementationImplementation is available on GitHub (https://github.com/paulderaadt/HSNE-clustering)[email protected] informationSupplementary data are available at Bioinformatics online.

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

10.1101/069633 ◽

2016 ◽

Cited By ~ 10

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.

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Deep learning of gene relationships from single cell time-course expression data

10.1101/2020.09.21.306332 ◽

2020 ◽

Author(s):

Ye Yuan ◽

Ziv Bar-Joseph

Keyword(s):

Time Series ◽

Deep Learning ◽

Single Cell ◽

Time Course ◽

Expression Profiles ◽

Regulatory Gene ◽

Supplementary Information ◽

Expression Data ◽

Rna Seq ◽

Time Course Data

AbstractMotivationTime-course gene expression data has been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offers several advantages including the large number of expression profiles available and the ability to focus on individual cells rather than averages. However, this data also raises new computational challenges.ResultsUsing a novel encoding for scRNA-Seq expression data we develop deep learning methods for interaction prediction from time-course data. Our methods use a supervised framework which represents the data as a 3D tensor and train convolutional and recurrent neural networks (CNN and RNN) for predicting interactions. We tested our Time-course Deep Learning (TDL) models on five different time series scRNA-Seq datasets. As we show, TDL can accurately identify causal and regulatory gene-gene interactions and can also be used to assign new function to genes. TDL improves on prior methods for the above tasks and can be generally applied to new time series scRNA-Seq data.Availability and ImplementationFreely available at https://github.com/xiaoyeye/[email protected] informationSupplementary data are available at XXX online.

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SCHNEL: scalable clustering of high dimensional single-cell data

Bioinformatics ◽

10.1093/bioinformatics/btaa816 ◽

2020 ◽

Vol 36 (Supplement_2) ◽

pp. i849-i856

Author(s):

Tamim Abdelaal ◽

Paul de Raadt ◽

Boudewijn P F Lelieveldt ◽

Marcel J T Reinders ◽

Ahmed Mahfouz

Keyword(s):

Single Cell ◽

Graph Clustering ◽

Original Data ◽

Large Datasets ◽

Supplementary Information ◽

High Dimensional ◽

Sequencing Data ◽

Novel Approach ◽

Cellular Markers ◽

Cell Data

Abstract Motivation Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets. Results We developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable time frames. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST. Availability and implementation Implementation is available on GitHub (https://github.com/biovault/SCHNELpy). All datasets used in this study are publicly available. Supplementary information Supplementary data are available at Bioinformatics online.

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SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data

10.1101/215723 ◽

2017 ◽

Cited By ~ 8

Author(s):

Yuchen Yang ◽

Ruth Huh ◽

Houston W. Culpepper ◽

Yuan Lin ◽

Michael I. Love ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Supplementary Information ◽

Adjusted Rand Index ◽

Ensemble Clustering ◽

Rna Seq ◽

Clustering Methods ◽

Cluster Assignment ◽

Absolute Deviation ◽

Number Of Clusters

ABSTRACTMotivationAccurately clustering cell types from a mass of heterogeneous cells is a crucial first step for the analysis of single-cell RNA-seq (scRNA-Seq) data. Although several methods have been recently developed, they utilize different characteristics of data and yield varying results in terms of both the number of clusters and actual cluster assignments.ResultsHere, we present SAFE-clustering, Single-cell Aggregated (From Ensemble) clustering, a flexible, accurate and robust method for clustering scRNA-Seq data. SAFE-clustering takes as input, results from multiple clustering methods, to build one consensus solution. SAFE-clustering currently embeds four state-of-the-art methods, SC3, CIDR, Seurat and t-SNE + k-means; and ensembles solutions from these four methods using three hypergraph-based partitioning algorithms. Extensive assessment across 12 datasets with the number of clusters ranging from 3 to 14, and the number of single cells ranging from 49 to 32,695 showcases the advantages of SAFE-clustering in terms of both cluster number (18.9 - 50.0% reduction in absolute deviation to the truth) and cluster assignment (on average 28.9% improvement, and up to 34.5% over the best of the four methods, measured by adjusted rand index). Moreover, SAFE-clustering is computationally efficient to accommodate large datasets, taking <10 minutes to process 28,733 cells.Availability and implementationSAFE-clustering, including source codes and tutorial, is free available on the web at http://yunliweb.its.unc.edu/safe/[email protected] informationSupplementary data are available at Bioinformatics online.

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ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

Bioinformatics ◽

10.1093/bioinformatics/btab179 ◽

2021 ◽

Author(s):

Irzam Sarfraz ◽

Muhammad Asif ◽

Joshua D Campbell

Keyword(s):

Single Cell ◽

R Package ◽

Poor Quality ◽

Data Matrix ◽

Supplementary Information ◽

Data Provenance ◽

Rna Seq ◽

Efficient Management ◽

The Matrix ◽

The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

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Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽

10.3390/genes12020311 ◽

2021 ◽

Vol 12 (2) ◽

pp. 311

Author(s):

Zhenqiu Liu

Keyword(s):

Single Cell ◽

Free Parameter ◽

Graphical Model ◽

Expression Patterns ◽

Information Criterion ◽

Log P ◽

Rna Seq ◽

Clustering Methods ◽

Wide Range ◽

Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

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