The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the ...study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.
The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable ...unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.
Kolodziejczyk et al. review the technical steps required for a successful single cell-RNA sequencing experiment from cell isolation through sequencing and analysis.
Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different ...technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.
Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and ...interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.
Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels ...of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell's lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach.
Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for ...computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.
Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets ...are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.
Synopsis
Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.
The inferred latent factors represent the underlying principal axes of heterogeneity across the samples. Factors can be shared by multiple data modalities or can be data‐type specific.
The model flexibly handles missing values and different data types.
In an application to Chronic Lymphocytic Leukaemia, MOFA discovers a low dimensional space spanned by known clinical markers and underappreciated axes of variation such as oxidative stress.
In an application to multi‐omics profiles from single‐cells, MOFA recovers differentiation trajectories and identifies coordinated variation between the transcriptome and the epigenome.
Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.
Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput of single-cell transcriptomics studies. A key computational challenge when processing these data is to ...distinguish libraries for real cells from empty droplets. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that EmptyDrops has greater power than existing approaches while controlling the false discovery rate among detected cells. Our method also retains distinct cell types that would have been discarded by existing methods in several real data sets.
Understanding cell type identity in a multicellular organism requires the integration of gene expression profiles from individual cells with their spatial location in a particular tissue. Current ...technologies allow whole-transcriptome sequencing of spatially identified cells but lack the throughput needed to characterize complex tissues. Here we present a high-throughput method to identify the spatial origin of cells assayed by single-cell RNA-sequencing within a tissue of interest. Our approach is based on comparing complete, specificity-weighted mRNA profiles of a cell with positional gene expression profiles derived from a gene expression atlas. We show that this method allocates cells to precise locations in the brain of the marine annelid Platynereis dumerilii with a success rate of 81%. Our method is applicable to any system that has a reference gene expression database of sufficiently high resolution.
The recent flood of single-cell data not only boosts our knowledge of cells and cell types, but also provides new insight into development and evolution from a cellular perspective. For example, ...assaying the genomes of multiple cells during development reveals developmental lineage trees-the kinship lineage-whereas cellular transcriptomes inform us about the regulatory state of cells and their gradual restriction in potency-the Waddington lineage. Beyond that, the comparison of single-cell data across species allows evolutionary changes to be tracked at all stages of development from the zygote, via different kinds of stem cells, to the differentiating cells. We discuss recent insights into the evolution of stem cells and initial attempts to reconstruct the evolutionary cell type tree of the mammalian forebrain, for example, by the comparative analysis of neuron types in the mesencephalic floor. These studies illustrate the immense potential of single-cell genomics to open up a new era in developmental and evolutionary research.
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