Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

/pdf/integrating-single-cell-transcriptomic-data-across-different-3dtsi5w7ob.pdf

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (
 https://github.com/theislab/Scanpy
 ). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (
 https://github.com/theislab/anndata
 ).

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SCANPY: large-scale single-cell gene expression data analysis

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenicaertslaborg) On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity

/pdf/scenic-single-cell-regulatory-network-inference-and-zvg4mq1xx7.pdf

SCENIC: single-cell regulatory network inference and clustering.

Respiratory immune characteristics associated with Coronavirus Disease 2019 (COVID-19) severity are currently unclear. We characterized bronchoalveolar lavage fluid immune cells from patients with varying severity of COVID-19 and from healthy people by using single-cell RNA sequencing. Proinflammatory monocyte-derived macrophages were abundant in the bronchoalveolar lavage fluid from patients with severe COVID-9. Moderate cases were characterized by the presence of highly clonally expanded CD8+ T cells. This atlas of the bronchoalveolar immune microenvironment suggests potential mechanisms underlying pathogenesis and recovery in COVID-19.

/pdf/single-cell-landscape-of-bronchoalveolar-immune-cells-in-2cbq61s5za.pdf

Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19.

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.

/pdf/normalization-and-variance-stabilization-of-single-cell-rna-55rlylmzlq.pdf

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST
 .

/pdf/mast-a-flexible-statistical-framework-for-assessing-2nt9ttfvra.pdf

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Single-cell transcriptomic profiling enables the unprecedented interrogation of gene expression heterogeneity in rare cell populations that would otherwise be obscured in bulk RNA sequencing experiments. The stochastic nature of transcription is revealed in the bimodality of single-cell transcriptomic data, a feature shared across single-cell expression platforms. There is, however, a paucity of computational tools that take advantage of this unique characteristic. We present a new methodology to analyze single-cell transcriptomic data that models this bimodality within a coherent generalized linear modeling framework. We propose a two-part, generalized linear model that allows one to characterize biological changes in the proportions of cells that are expressing each gene, and in the positive mean expression level of that gene. We introduce the cellular detection rate, the fraction of genes turned on in a cell, and show how it can be used to simultaneously adjust for technical variation and so-called “extrinsic noise” at the single-cell level without the use of control genes. Our model permits direct inference on statistics formed by collections of genes, facilitating gene set enrichment analysis. The residuals defined by such models can be manipulated to interrogate cellular heterogeneity and gene-gene correlation across cells and conditions, providing insights into the temporal evolution of networks of co-expressed genes at the single-cell level. Using two single-cell RNA-seq datasets, including newly generated data from Mucosal Associated Invariant T (MAIT) cells, we show how model residuals can be used to identify significant changes across biologically relevant gene sets that are missed by other methods and characterize cellular heterogeneity in response to stimulation.

/pdf/mast-a-flexible-statistical-framework-for-assessing-2pdbdc32tw.pdf

Jingyuan Deng

Papers

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA-seq data