Joint dimension reduction and clustering analysis for single-cell RNA-seq and spatial transcriptomics data

Dimension reduction and (spatial) clustering are two key steps for the analysis of both single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics data collected from different platforms. Most existing methods perform dimension reduction and (spatial) clustering sequentially, treating them as two consecutive stages in tandem analysis. However, the low-dimensional embeddings estimated in the dimension reduction step may not necessarily be relevant to the class labels inferred in the clustering step and thus may impair the performance of the clustering and other downstream analysis. Here, we develop a computation method, DR-SC, to perform both dimension reduction and (spatial) clustering jointly in a unified framework. Joint analysis in DR-SC ensures accurate (spatial) clustering results and effective extraction of biologically informative low-dimensional features. Importantly, DR-SC is not only applicable for cell type clustering in scRNA-seq studies but also applicable for spatial clustering in spatial transcriptimics that characterizes the spatial organization of the tissue by segregating it into multiple tissue structures. For spatial transcriptoimcs analysis, DR-SC relies on an underlying latent hidden Markov random field model to encourage the spatial smoothness of the detected spatial cluster boundaries. We also develop an efficient expectation-maximization algorithm based on an iterative conditional mode. DR-SC is not only scalable to large sample sizes, but is also capable of optimizing the spatial smoothness parameter in a data-driven manner. Comprehensive simulations show that DR-SC outperforms existing clustering methods such as Seurat and spatial clustering methods such as BayesSpace and SpaGCN and extracts more biologically relevant features compared to the conventional dimension reduction methods such as PCA and scVI. Using 16 benchmark scRNA-seq datasets, we demonstrate that the low-dimensional embeddings and class labels estimated from DR-SC lead to improved trajectory inference. In addition, analyzing three published scRNA-seq and spatial transcriptomics data in three platforms, we show DR-SC can improve both the spatial and non-spatial clustering performance, resolving a low-dimensional representation with improved visualization, and facilitate the downstream analysis such as trajectory inference.

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A Bayesian nonparametric semi-supervised model for integration of multiple single-cell experiments

10.1101/2020.01.14.906313 ◽

2020 ◽

Author(s):

Archit Verma ◽

Barbara Engelhardt

Keyword(s):

Single Cell ◽

Latent Variable ◽

Environmental Variability ◽

Simulated Data ◽

Joint Analysis ◽

Variable Model ◽

Manifold Alignment ◽

Multiple Data Sets ◽

Sequencing Platforms ◽

Low Dimensional

Joint analysis of multiple single cell RNA-sequencing (scRNA-seq) data is confounded by technical batch effects across experiments, biological or environmental variability across cells, and different capture processes across sequencing platforms. Manifold alignment is a principled, effective tool for integrating multiple data sets and controlling for confounding factors. We demonstrate that the semi-supervised t-distributed Gaussian process latent variable model (sstGPLVM), which projects the data onto a mixture of fixed and latent dimensions, can learn a unified low-dimensional embedding for multiple single cell experiments with minimal assumptions. We show the efficacy of the model as compared with state-of-the-art methods for single cell data integration on simulated data, pancreas cells from four sequencing technologies, induced pluripotent stem cells from male and female donors, and mouse brain cells from both spatial seqFISH+ and traditional scRNA-seq.Code and data is available at https://github.com/architverma1/sc-manifold-alignment

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A learned embedding for efficient joint analysis of millions of mass spectra

10.1101/483263 ◽

2018 ◽

Cited By ~ 4

Author(s):

Damon H. May ◽

Jeffrey Bilmes ◽

William S. Noble

Keyword(s):

Mass Spectra ◽

Large Scale ◽

Dimensional Space ◽

Software Implementation ◽

Mass Spectrometry Data ◽

Joint Analysis ◽

Clustering Methods ◽

Peptide Mass ◽

Public Repositories ◽

Low Dimensional

AbstractDespite an explosion of data in public repositories, peptide mass spectra are usually analyzed by each laboratory in isolation, treating each experiment as if it has no relationship to any others. This approach fails to exploit the wealth of existing, previously analyzed mass spectrometry data. Others have jointly analyzed many mass spectra, often using clustering. However, mass spectra are not necessarily best summarized as clusters, and although new spectra can be added to existing clusters, clustering methods previously applied to mass spectra do not allow new clusters to be defined without completely re-clustering. As an alternative, we propose to train a deep neural network, called “GLEAMS,” to learn an embedding of spectra into a low-dimensional space in which spectra generated by the same peptide are close to one another. We demonstrate empirically the utility of this learned embedding by propagating annotations from labeled to unlabeled spectra. We further use GLEAMS to detect groups of unidentified, proximal spectra representing the same peptide, and we show how to use these spectral communities to reveal misidentified spectra and to characterize frequently observed but consistently unidentified molecular species. We provide a software implementation of our approach, along with a tool to quickly embed additional spectra using a pre-trained model, to facilitate large-scale analyses.

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scSemiAE: A Deep Model with Semi-Supervised Learning for Single-Cell Transcriptomics

10.21203/rs.3.rs-1037942/v1 ◽

2021 ◽

Author(s):

Jiayi Dong ◽

Yin Zhang ◽

Fei Wang

Keyword(s):

Dimensionality Reduction ◽

Single Cell ◽

Cell Subpopulation ◽

High Dimensions ◽

Deep Model ◽

Unsupervised Approach ◽

Cell Subpopulations ◽

Downstream Analysis ◽

Low Dimensional ◽

Public Datasets

Abstract Background: With the development of modern sequencing technology, hundreds of thousands of single-cell RNA-sequencing(scRNA-seq) profiles allow to explore the heterogeneity in the cell level, but it faces the challenges of high dimensions and high sparsity. Dimensionality reduction is essential for downstream analysis, such as clustering to identify cell subpopulations. Usually, dimensionality reduction follows unsupervised approach. Results: In this paper, we introduce a semi-supervised dimensionality reduction method named scSemiAE, which is based on an autoencoder model. It transfers the information contained in available datasets with cell subpopulation labels to guide the search of better low-dimensional representations, which can ease further analysis. Conclusions: Experiments on five public datasets show that, scSemiAE outperforms both unsupervised and semi-supervised baselines whether the transferred information embodied in the number of labeled cells and labeled cell subpopulations is much or less.

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MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

10.1101/837104 ◽

2019 ◽

Cited By ~ 8

Author(s):

Ricard Argelaguet ◽

Damien Arnol ◽

Danila Bredikhin ◽

Yonatan Deloro ◽

Britta Velten ◽

...

Keyword(s):

Factor Analysis ◽

Single Cell ◽

Cell Fate ◽

Joint Analysis ◽

Experimental Conditions ◽

Joint Modelling ◽

Stochastic Variational Inference ◽

Technological Advances ◽

Low Dimensional ◽

Cell Data

AbstractTechnological advances have enabled the joint analysis of multiple molecular layers at single cell resolution. At the same time, increased experimental throughput has facilitated the study of larger numbers of experimental conditions. While methods for analysing single-cell data that model the resulting structure of either of these dimensions are beginning to emerge, current methods do not account for complex experimental designs that include both multiple views (modalities or assays) and groups (conditions or experiments). Here we present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of structured single cell multi-modal data. MOFA+ builds upon a Bayesian Factor Analysis framework combined with fast GPU-accelerated stochastic variational inference. Similar to existing factor models, MOFA+ allows for interpreting variation in single-cell datasets by pooling information across cells and features to reconstruct a low-dimensional representation of the data. Uniquely, the model supports flexible group-level sparsity constraints that allow joint modelling of variation across multiple groups and views.To illustrate MOFA+, we applied it to single-cell data sets of different scales and designs, demonstrating practical advantages when analyzing datasets with complex group and/or view structure. In a multi-omics analysis of mouse gastrulation this joint modelling reveals coordinated changes between gene expression and epigenetic variation associated with cell fate commitment.

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Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequencing data analysis

10.1101/2020.12.11.421883 ◽

2020 ◽

Author(s):

Tamim Abdelaal ◽

Jeroen Eggermont ◽

Thomas Höllt ◽

Ahmed Mahfouz ◽

Marcel J.T. Reinders ◽

...

Keyword(s):

Data Analysis ◽

Single Cell ◽

Rna Sequencing ◽

Visual Exploration ◽

Sequencing Data ◽

Interactive Analysis ◽

Levels Of Detail ◽

Single Cell Rna Sequencing ◽

Downstream Analysis ◽

Low Dimensional

SummaryThe ever-increasing number of analyzed cells in Single-cell RNA sequencing (scRNA-seq) experiments imposes several challenges on the data analysis. Current analysis methods lack scalability to large datasets hampering interactive visual exploration of the data. We present Cytosplore-Transcriptomics, a framework to analyze scRNA-seq data, including data preprocessing, visualization and downstream analysis. At its core, it uses a hierarchical, manifold preserving representation of the data that allows the inspection and annotation of scRNA-seq data at different levels of detail. Consequently, Cytosplore-Transcriptomics provides interactive analysis of the data using low-dimensional visualizations that scales to millions of cells.AvailabilityCytosplore-Transcriptomics can be freely downloaded from [email protected]

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A robust nonlinear low-dimensional manifold for single cell RNA-seq data

10.1101/443044 ◽

2018 ◽

Cited By ~ 5

Author(s):

Archit Verma ◽

Barbara E. Engelhardt

Keyword(s):

Gaussian Process ◽

Dimension Reduction ◽

Single Cell ◽

Latent Variable ◽

Developmental Trajectories ◽

Latent Variable Model ◽

Variable Model ◽

Sequencing Technologies ◽

Heavy Tailed ◽

Low Dimensional

AbstractModern developments in single cell sequencing technologies enable broad insights into cellular state. Single cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden understanding of cell heterogeneity in tissues and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single cell data. However, methods have yet to be developed for unfiltered and unnormalized count data. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data. Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student’s t-distribution to estimate a manifold that is robust to technical and biological noise. We compare our approach to common dimension reduction tools to highlight our model’s ability to enable important downstream tasks, including clustering and inferring cell developmental trajectories, on available experimental data. We show that our robust nonlinear manifold is well suited for raw, unfiltered gene counts from high throughput sequencing technologies for visualization and exploration of cell states.

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Benchmarking UMI-based single cell RNA-sequencing preprocessing workflows

10.1101/2021.06.17.448895 ◽

2021 ◽

Author(s):

Yue You ◽

Luyi Tian ◽

Shian Su ◽

Xueyi Dong ◽

Jafar Sheikh Jabbari ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Rapid Development ◽

Ground Truth ◽

Clustering Methods ◽

Analysis Process ◽

Single Cell Rna Sequencing ◽

Systematic Biases ◽

Downstream Analysis ◽

Almost All

Single-cell RNA sequencing (scRNA-seq) technologies and associated analysis methods have undergone rapid development in recent years. This includes methods for data preprocessing, which assign sequencing reads to genes to create count matrices for downstream analysis. Several packaged preprocessing workflows have been developed that aim to provide users with convenient tools for handling this process. How different preprocessing workflows compare to one another and influence downstream analysis has been less well studied. Here, we systematically benchmark the performance of 9 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2 and scruff) using datasets with varying levels of biological complexity generated on the CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. We find that lowly expressed genes are discordant between workflows and observe that some workflows have systematic biases towards particular classes of genomics features. While the scRNA-seq preprocessing workflows compared varied in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produced clustering results that agreed well with the known cell type labels that provided the ground truth in our analysis. In summary, the choice of preprocessing method was found to be less influential than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNAseq preprocessing workflows and summarizes their characteristics to guide workflow users.

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Robust clustering and interpretation of scRNA-seq data using reference component analysis

10.1101/2021.02.16.431527 ◽

2021 ◽

Author(s):

Florian Schmidt ◽

Bobby Ranjan ◽

Quy Xiao Xuan Lin ◽

Vaidehi Krishnan ◽

Ignasius Joanito ◽

...

Keyword(s):

Single Cell ◽

De Novo ◽

Clustering Algorithms ◽

Cell Types ◽

Unsupervised Clustering ◽

Data Sets ◽

Clustering Methods ◽

Robust Clustering ◽

Supervised Clustering ◽

Downstream Analysis

MotivationThe transcriptomic diversity of the hundreds of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Though clustering of cellular transcriptomes is the default technique for defining cell types and subtypes, single cell clustering can be strongly influenced by technical variation. In fact, the prevalent unsupervised clustering algorithms can cluster cells by technical, rather than biological, variation.ResultsCompared to de novo (unsupervised) clustering methods, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects. To leverage the advantages of supervised clustering, we present RCA2, a new, scalable, and broadly applicable version of our RCA algorithm. RCA2 provides a user-friendly framework for supervised clustering and downstream analysis of large scRNA-seq data sets. RCA2 can be seamlessly incorporated into existing algorithmic pipelines. It incorporates various new reference panels for human and mouse, supports generation of custom panels and uses efficient graph-based clustering and sparse data structures to ensure scalability. We demonstrate the applicability of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Importantly, RCA2 facilitates cell-type-specific QC, which we show is essential for accurate clustering of SC data from heterogeneous tissues. In the era of cohort-scale SC analysis, supervised clustering methods such as RCA2 will facilitate unified analysis of diverse SC datasets.AvailabilityRCA2 is implemented in R and is available at github.com/prabhakarlab/RCAv2

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Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

10.1101/796029 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jovan Tanevski ◽

Thin Nguyen ◽

Buu Truong ◽

Nikos Karaiskos ◽

Mehmet Eren Ahsen ◽

...

Keyword(s):

Single Cell ◽

Spatial Organization ◽

Gene Selection ◽

Spatial Information ◽

Spatial Clustering ◽

Spatial Location ◽

Drosophila Embryo ◽

Rna Seq ◽

Hybridization Data ◽

Transcriptomics Data

AbstractSingle-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

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Clustering and visualization of single-cell RNA-seq data using path metrics

10.1101/2021.12.14.472627 ◽

2021 ◽

Author(s):

Andriana Manousidaki ◽

Anna Little ◽

Yuying Xie

Keyword(s):

Dimension Reduction ◽

Single Cell ◽

Cluster Structure ◽

Data Sets ◽

Sequencing Data ◽

Local Cluster ◽

Wide Range ◽

Clustering Quality ◽

Low Dimensional

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. This article explores the application of power-weighted path metrics for the analysis of single cell RNA data. Extensive experiments on single cell RNA sequencing data sets confirm the usefulness of path metrics for dimension reduction and clustering. Distances between cells are measured in a data- driven way which is both density sensitive (decreasing distances across high density regions) and respects the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which respects both the global geometry of the data and preserves cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms other algorithms on a wide range of bench marking data sets.

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