Bias, robustness and scalability in single-cell differential expression analysis

Clustering-independent analysis of genomic data using spectral simplicial theory

The prevailing paradigm for the analysis of biological data involves comparing groups of replicates from different conditions (e.g. control and treatment) to statistically infer features that discriminate them (e.g. differentially expressed genes). However, many situations in modern genomics such as single-cell omics experiments do not fit well into this paradigm because they lack true replicates. In such instances, spectral techniques could be used to rank features according to their degree of consistency with an underlying metric structure without the need to cluster samples. Here, we extend spectral methods for feature selection to abstract simplicial complexes and present a general framework for clustering-independent analysis. Combinatorial Laplacian scores take into account the topology spanned by the data and reduce to the ordinary Laplacian score when restricted to graphs. We demonstrate the utility of this framework with several applications to the analysis of gene expression and multi-modal genomic data. Specifically, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate differentially expressed genes according to the topology of the expression space (e.g. alternative paths of differentiation). We also apply this formalism to identify genes with spatial patterns of expression using fluorescence in-situ hybridization data and to establish associations between genetic alterations and global expression patterns in large cross-sectional studies. Our results provide a unifying perspective on topological data analysis and manifold learning approaches to the analysis of large-scale biological datasets.

Manifold learning methods have emerged as a way of analyzing the large high-dimensional data sets that are currently generated in many areas of science. They assume the data has been sampled from an unknown manifold which is approximated with a graph and utilize spectral graph techniques to perform unsupervised feature selection and dimensionality reduction. However, graphs provide only partial approximations to manifolds, precluding the application to features with a complex combinatorial structure. Relatedly, these methods cannot take into account the topology of the manifold. In this work, we extend spectral methods for feature selection to topological spaces built from data and present a general framework for feature selection. We present specific applications of this framework to clustering-independent analysis of gene expression and multi-modal genomic data. In particular, using these methods, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate the results according to topological features of the expression space. In addition, we identify genes with spatial patterns of expression using spatially-resolved transcriptomic data and establish associations between genetic alterations and global expression patterns in large cross-sectional cancer studies.

Reproducible and replicable comparisons using SummarizedBenchmark

Summary

Benchmark studies are widely used to compare and evaluate tools developed for answering various biological questions. Despite the popularity of these comparisons, the implementation is often ad hoc, with little consistency across studies. To address this problem, we developed SummarizedBenchmark, an R package and framework for organizing and structuring benchmark comparisons. SummarizedBenchmark defines a general grammar for benchmarking and allows for easier setup and execution of benchmark comparisons, while improving the reproducibility and replicability of such comparisons. We demonstrate the wide applicability of our framework using four examples from different applications.

Availability and implementation

SummarizedBenchmark is an R package available through Bioconductor (http://bioconductor.org/packages/SummarizedBenchmark).

Supplementary information

Supplementary data are available at Bioinformatics online.

Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for studying complex biological systems, such as tumor heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumor tissues and patient-derived mouse xenografts for scRNA-seq are not well understood.

RESULTS

We use low temperature (6 °C) protease and collagenase (37 °C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNA-seq dataset comprising 155,165 cells from patient cancer tissues, patient-derived breast cancer xenografts, and cancer cell lines. We observe substantial variation in standard quality control metrics of cell viability across conditions and tissues. From the contrast between tissue protease dissociation at 37 °C or 6 °C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37 °C), which are minimized by dissociation with a cold active protease (6 °C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues.

CONCLUSIONS

The method and conditions of tumor dissociation influence cell yield and transcriptome state and are both tissue- and cell-type dependent. Interpretation of stress pathway expression differences in cancer single-cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with the identification of such effects in dissociated scRNA-seq experiments.

Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells

Glioblastoma cell ability to adapt their functioning to microenvironment changes is a source of the extensive intra-tumor heterogeneity characteristic of this devastating malignant brain tumor. A systemic view of the metabolic pathways underlying glioblastoma cell functioning states is lacking. We analyzed public single cell RNA-sequencing data from glioblastoma surgical resections, which offer the closest available view of tumor cell heterogeneity as encountered at the time of patients’ diagnosis. Unsupervised analyses revealed that information dispersed throughout the cell transcript repertoires encoded the identity of each tumor and masked information related to cell functioning states. Data reduction based on an experimentally-defined signature of transcription factors overcame this hurdle. It allowed cell grouping according to their tumorigenic potential, regardless of their tumor of origin. The approach relevance was validated using independent datasets of glioblastoma cell and tissue transcriptomes, patient-derived cell lines and orthotopic xenografts. Overexpression of genes coding for amino acid and lipid metabolism enzymes involved in anti-oxidative, energetic and cell membrane processes characterized cells with high tumorigenic potential. Modeling of their expression network highlighted the very long chain polyunsaturated fatty acid synthesis pathway at the core of the network. Expression of its most downstream enzymatic component, ELOVL2, was associated with worsened patient survival, and required for cell tumorigenic properties in vivo. Our results demonstrate the power of signature-driven analyses of single cell transcriptomes to obtain an integrated view of metabolic pathways at play within the heterogeneous cell landscape of patient tumors.

A systematic evaluation of single cell RNA-seq analysis pipelines

The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

Nonparametric expression analysis using inferential replicate counts

A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the inherent uncertainty of the abundance estimates arising from the quantification step. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts for each gene or transcript, and a subset of methods can incorporate uncertainty. Previous work has shown that nonparametric models for RNA-seq differential expression may have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account inferential uncertainty, leading to an inflated false discovery rate, in particular at the transcript level. We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty. We compare our method, Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a single-cell RNA-seq dataset, assessing differential expression between sub-populations of cells, and compare its performance to the Wilcoxon test.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Self-assembling manifolds in single-cell RNA sequencing data

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

A comparison of automatic cell identification methods for single-cell RNA sequencing data

BACKGROUND

Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification.

RESULTS

Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods' sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments.

CONCLUSIONS

We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.

scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.

Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer

High numbers of tissue-resident memory T (TRM) cells are associated with better clinical outcomes in cancer patients. However, the molecular characteristics that drive their efficient immune response to tumors are poorly understood. Here, single-cell and bulk transcriptomic analysis of TRM and non-TRM cells present in tumor and normal lung tissue from patients with lung cancer revealed that PD-1-expressing TRM cells in tumors were clonally expanded and enriched for transcripts linked to cell proliferation and cytotoxicity when compared with PD-1-expressing non-TRM cells. This feature was more prominent in the TRM cell subset coexpressing PD-1 and TIM-3, and it was validated by functional assays ex vivo and also reflected in their chromatin accessibility profile. This PD-1+TIM-3+ TRM cell subset was enriched in responders to PD-1 inhibitors and in tumors with a greater magnitude of CTL responses. These data highlight that not all CTLs expressing PD-1 are dysfunctional; on the contrary, TRM cells with PD-1 expression were enriched for features suggestive of superior functionality.

Molecular mechanisms of lineage decisions in metabolite-specific T cells

Mucosal-associated invariant T cells (MAIT cells) recognize the microbial metabolite 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU) presented by the MHC class Ib molecule, MR1. MAIT cells acquire effector functions during thymic development, but the mechanisms involved are unclear. Here we used single-cell RNA-sequencing to characterize the developmental path of 5-OP-RU-specific thymocytes. In addition to the known MAIT1 and MAIT17 effector subsets selected on bone-marrow-derived hematopoietic cells, we identified 5-OP-RU-specific thymocytes that were selected on thymic epithelial cells and differentiated into CD44^- naive T cells. MAIT cell positive selection required signaling through the adapter, SAP, that controlled the expression of the transcription factor, ZBTB16. Pseudotemporal ordering of single cells revealed transcriptional trajectories of 5-OP-RU-specific thymocytes selected on either thymic epithelial cells or hematopoietic cells. The resulting model illustrates T cell lineage decisions.

Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data

Background

Single cell transcriptome sequencing has become an increasingly valuable technology for dissecting complex biology at a resolution impossible with bulk sequencing. However, the gap between the technical expertise required to effectively work with the resultant high dimensional data and the biological expertise required to interpret the results in their biological context remains incompletely addressed by the currently available tools.

Results

Single Cell Explorer is a Python-based web server application we developed to enable computational and experimental scientists to iteratively and collaboratively annotate cell expression phenotypes within a user-friendly and visually appealing platform. These annotations can be modified and shared by multiple users to allow easy collaboration between computational scientists and experimental biologists. Data processing and analytic workflows can be integrated into the system using Jupyter notebooks. The application enables powerful yet accessible features such as the identification of differential gene expression patterns for user-defined cell populations and convenient annotation of cell types using marker genes or differential gene expression patterns. Users are able to produce plots without needing Python or R coding skills. As such, by making single cell RNA-seq data sharing and querying more user-friendly, the software promotes deeper understanding and innovation by research teams applying single cell transcriptomic approaches.

Conclusions

Single cell explorer is a freely-available single cell transcriptomic analysis tool that enables computational and experimental biologists to collaboratively explore, annotate, and share results in a flexible software environment and a centralized database server that supports data portal functionality.

Rfx6 promotes the differentiation of peptide-secreting enteroendocrine cells while repressing genetic programs controlling serotonin production

Objective

Enteroendocrine cells (EECs) of the gastro-intestinal tract sense gut luminal factors and release peptide hormones or serotonin (5-HT) to coordinate energy uptake and storage. Our goal is to decipher the gene regulatory networks controlling EECs specification from enteroendocrine progenitors. In this context, we studied the role of the transcription factor Rfx6 which had been identified as the cause of Mitchell–Riley syndrome, characterized by neonatal diabetes and congenital malabsorptive diarrhea. We previously reported that Rfx6 was essential for pancreatic beta cell development and function; however, the role of Rfx6 in EECs differentiation remained to be elucidated.

Methods

We examined the molecular, cellular, and metabolic consequences of constitutive and conditional deletion of Rfx6 in the embryonic and adult mouse intestine. We performed single cell and bulk RNA-Seq to characterize EECs diversity and identify Rfx6-regulated genes.

Results

Rfx6 is expressed in the gut endoderm; later, it is turned on in, and restricted to, enteroendocrine progenitors and persists in hormone-positive EECs. In the embryonic intestine, the constitutive lack of Rfx6 leads to gastric heterotopia, suggesting a role in the maintenance of intestinal identity. In the absence of intestinal Rfx6, EECs differentiation is severely impaired both in the embryo and adult. However, the number of serotonin-producing enterochromaffin cells and mucosal 5-HT content are increased. Concomitantly, Neurog3-positive enteroendocrine progenitors accumulate. Combined analysis of single-cell and bulk RNA-Seq data revealed that enteroendocrine progenitors differentiate in two main cell trajectories, the enterochromaffin (EC) cells and the Peptidergic Enteroendocrine (PE) cells, the differentiation programs of which are differentially regulated by Rfx6. Rfx6 operates upstream of Arx, Pax6 and Isl1 to trigger the differentiation of peptidergic EECs such as GIP-, GLP-1-, or CCK-secreting cells. On the contrary, Rfx6 represses Lmx1a and Tph1, two genes essential for serotonin biosynthesis. Finally, we identified transcriptional changes uncovering adaptive responses to the prolonged lack of enteroendocrine hormones and leading to malabsorption and lower food efficiency ratio in Rfx6-deficient mouse intestine.

Conclusion

These studies identify Rfx6 as an essential transcriptional regulator of EECs specification and shed light on the molecular mechanisms of intestinal failures in human RFX6-deficiencies such as Mitchell–Riley syndrome.

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The lack of Rfx6 impairs the differentiation of peptide-producing enteroendocrine cells.

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The number of 5-HT-expressing-cells is increased in Rfx6-deficient intestine.

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Intestinal inactivation of Rfx6 leads to lipid malabsorption and decreased food efficiency.

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

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.

Deciphering Brain Complexity Using Single-cell Sequencing

The human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the complexity of cell types, as well as connectivity and functions of the brain. The newly developed single-cell sequencing technology, which provides a comprehensive landscape of brain cell type diversity by profiling the transcriptome, genome, and/or epigenome of individual cells, has contributed substantially to revealing the complexity and dynamics of the brain and providing new insights into brain development and brain-related disorders. In this review, we first introduce the progresses in both experimental and computational methods of single-cell sequencing technology. Applications of single-cell sequencing-based technologies in brain research, including cell type classification, brain development, and brain disease mechanisms, are then elucidated by representative studies. Lastly, we provided our perspectives into the challenges and future developments in the field of single-cell sequencing. In summary, this mini review aims to provide an overview of how big data generated from single-cell sequencing have empowered the advancements in neuroscience and shed light on the complex problems in understanding brain functions and diseases.

Genome-wide analysis of androgen receptor binding and transcriptomic analysis in mesenchymal subsets during prostate development

Prostate development is controlled by androgens, the androgen receptor (AR) and mesenchymal–epithelial signalling. We used chromatin immunoprecipitation sequencing (ChIP-seq) to define AR genomic binding in the male and female mesenchyme. Tissue- and single-cell-based transcriptional profiling was used to define mesenchymal AR target genes. We observed significant AR genomic binding in females and a strong enrichment at proximal promoters in both sexes. In males, there was greater AR binding to introns and intergenic regions as well as to classical AR binding motifs. In females, there was increased proximal promoter binding and involvement of cofactors. Comparison of AR-bound genes with transcriptomic data enabled the identification of novel sexually dimorphic AR target genes. We validated the dimorphic expression of AR target genes using published datasets and confirmed regulation by androgens using ex vivo organ cultures. AR targets showed variable expression in patients with androgen insensitivity syndrome. We examined AR function at single-cell resolution using single-cell RNA sequencing (scRNA-seq) in male and female mesenchyme. Surprisingly, both AR and target genes were distributed throughout cell subsets, with few positive cells within each subset. AR binding was weakly correlated with target gene expression.

Summary: A study of how androgens lead to sexually dimorphic development of the prostate using transcription factor genome binding and transcriptome analysis in mesenchymal subsets.

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data

We develop CellSIUS (Cell Subtype Identification from Upregulated gene Sets) to fill a methodology gap for rare cell population identification for scRNA-seq data. CellSIUS outperforms existing algorithms for specificity and selectivity for rare cell types and their transcriptomic signature identification in synthetic and complex biological data. Characterization of a human pluripotent cell differentiation protocol recapitulating deep-layer corticogenesis using CellSIUS reveals unrecognized complexity in human stem cell-derived cellular populations. CellSIUS enables identification of novel rare cell populations and their signature genes providing the means to study those populations in vitro in light of their role in health and disease.

De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments

This study describes a computational method for determining statistical support to varying levels of heterogeneity provided by single-cell RNA-sequencing data with applications to tumor samples.

Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.

Current best practices in single-cell RNA-seq analysis: a tutorial

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

Simulating multiple faceted variability in single cell RNA sequencing

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios.

Simulated single cell RNA sequencing data is useful for method development and comparison. Here, the authors developed SymSim, a simulator that explicitly models the main factors of variation in single cell data.

Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk

Recent advances in the generation of kidney organoids and the culture of primary nephron progenitors from mouse and human have been based on knowledge of the molecular basis of kidney development in mice. Although gene expression during kidney development has been intensely investigated, single cell profiling provides new opportunities to further subsect component cell types and the signalling networks at play. Here, we describe the generation and analysis of 6732 single cell transcriptomes from the fetal mouse kidney [embryonic day (E)18.5] and 7853 sorted nephron progenitor cells (E14.5). These datasets provide improved resolution of cell types and specific markers, including subdivision of the renal stroma and heterogeneity within the nephron progenitor population. Ligand-receptor interaction and pathway analysis reveals novel crosstalk between cellular compartments and associates new pathways with differentiation of nephron and ureteric epithelium cell types. We identify transcriptional congruence between the distal nephron and ureteric epithelium, showing that most markers previously used to identify ureteric epithelium are not specific. Together, this work improves our understanding of metanephric kidney development and provides a template to guide the regeneration of renal tissue.

A practical guide to methods controlling false discoveries in computational biology

BACKGROUND

In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as informative covariates to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigate the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology.

RESULTS

Methods that incorporate informative covariates are modestly more powerful than classic approaches, and do not underperform classic approaches, even when the covariate is completely uninformative. The majority of methods are successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we find that the improvement of the modern FDR methods over the classic methods increases with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses.

CONCLUSIONS

Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

Single-cell transcriptomics unveils gene regulatory network plasticity

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.

RESULTS

We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.

CONCLUSIONS

Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.

Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments, laboratories and technologies can uncover biological insights, but current methods for scRNA-seq data integration are limited by a requirement for datasets to derive from functionally similar cells. We present Scanorama, an algorithm that identifies and merges the shared cell types among all pairs of datasets and accurately integrates heterogeneous collections of scRNA-seq data. We applied Scanorama to integrate and remove batch effects across 105,476 cells from 26 diverse scRNA-seq experiments representing 9 different technologies. Scanorama is sensitive to subtle temporal changes within the same cell lineage, successfully integrating functionally similar cells across time series data of CD14+ monocytes at different stages of differentiation into macrophages. Finally, we show that Scanorama is orders of magnitude faster than existing techniques and can integrate a collection of 1,095,538 cells in just ~9 h.

Exploratory Gene Ontology Analysis with Interactive Visualization

The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. At the same time, the sheer number of concepts (>30,000) and relationships (>70,000) presents a challenge: it can be difficult to draw a comprehensive picture of how certain concepts of interest might relate with the rest of the ontology structure. Here we present new visualization strategies to facilitate the exploration and use of the information in the GO. We rely on novel graphical display and software architecture that allow significant interaction. To illustrate the potential of our strategies, we provide examples from high-throughput genomic analyses, including chromatin immunoprecipitation experiments and genome-wide association studies. The scientist can also use our visualizations to identify gene sets that likely experience coordinated changes in their expression and use them to simulate biologically-grounded single cell RNA sequencing data, or conduct power studies for differential gene expression studies using our built-in pipeline. Our software and documentation are available at http://aegis.stanford.edu .

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing

Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeo-statically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.

Systematic benchmarking of omics computational tools

Computational omics methods packaged as software have become essential to modern biological research. The increasing dependence of scientists on these powerful software tools creates a need for systematic assessment of these methods, known as benchmarking. Adopting a standardized benchmarking practice could help researchers who use omics data to better leverage recent technological innovations. Our review summarizes benchmarking practices from 25 recent studies and discusses the challenges, advantages, and limitations of benchmarking across various domains of biology. We also propose principles that can make computational biology benchmarking studies more sustainable and reproducible, ultimately increasing the transparency of biomedical data and results.

Benchmarking studies are important for comprehensively understanding and evaluating different computational omics methods. Here, the authors review practices from 25 recent studies and propose principles to improve the quality of benchmarking studies.

Endothelial miR-30c suppresses tumor growth via inhibition of TGF-β-induced Serpine1

In tumors, extravascular fibrin forms provisional scaffolds for endothelial cell (EC) growth and motility during angiogenesis. We report that fibrin-mediated angiogenesis was inhibited and tumor growth delayed following postnatal deletion of Tgfbr2 in the endothelium of Cdh5-CreERT2 Tgfbr2fl/fl mice (Tgfbr2iECKO mice). ECs from Tgfbr2iECKO mice failed to upregulate the fibrinolysis inhibitor plasminogen activator inhibitor 1 (Serpine1, also known as PAI-1), due in part to uncoupled TGF-β-mediated suppression of miR-30c. Bypassing TGF-β signaling with vascular tropic nanoparticles that deliver miR-30c antagomiRs promoted PAI-1-dependent tumor growth and increased fibrin abundance, whereas miR-30c mimics inhibited tumor growth and promoted vascular-directed fibrinolysis in vivo. Using single-cell RNA-Seq and a NanoString miRNA array, we also found that subtypes of ECs in tumors showed spectrums of Serpine1 and miR-30c expression levels, suggesting functional diversity in ECs at the level of individual cells; indeed, fresh EC isolates from lung and mammary tumor models had differential abilities to degrade fibrin and launch new vessel sprouts, a finding that was linked to their inverse expression patterns of miR-30c and Serpine1 (i.e., miR-30chi Serpine1lo ECs were poorly angiogenic and miR-30clo Serpine1hi ECs were highly angiogenic). Thus, by balancing Serpine1 expression in ECs downstream of TGF-β, miR-30c functions as a tumor suppressor in the tumor microenvironment through its ability to promote fibrin degradation and inhibit blood vessel formation.

IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI's Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/.

A discriminative learning approach to differential expression analysis for single-cell RNA-seq

Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3' single-cell RNA-seq that can identify previously undetectable marker genes.

ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions

Motivation

Single cell RNA-Seq (scRNA-Seq) facilitates the characterization of cell type heterogeneity and developmental processes. Further study of single cell profiles across different conditions enables the understanding of biological processes and underlying mechanisms at the sub-population level. However, developing proper methodology to compare multiple scRNA-Seq datasets remains challenging.

Results

We have developed ClusterMap, a systematic method and workflow to facilitate the comparison of scRNA-seq profiles across distinct biological contexts. Using hierarchical clustering of the marker genes of each sub-group, ClusterMap matches the sub-types of cells across different samples and provides ‘similarity’ as a metric to quantify the quality of the match. We introduce a purity tree cut method designed specifically for this matching problem. We use Circos plot and regrouping method to visualize the results concisely. Furthermore, we propose a new metric ‘separability’ to summarize sub-population changes among all sample pairs. In the case studies, we demonstrate that ClusterMap has the ability to provide us further insight into the different molecular mechanisms of cellular sub-populations across different conditions.

Availability and implementation

ClusterMap is implemented in R and available at https://github.com/xgaoo/ClusterMap.

Supplementary information

Supplementary data are available at Bioinformatics online.

Stability of methods for differential expression analysis of RNA-seq data

Background

As RNA-seq becomes the assay of choice for measuring gene expression levels, differential expression analysis has received extensive attentions of researchers. To date, for the evaluation of DE methods, most attention has been paid on validity. Yet another important aspect of DE methods, stability, is overlooked and has not been studied to the best of our knowledge.

Results

In this study, we empirically show the need of assessing stability of DE methods and propose a stability metric, called Area Under the Correlation curve (AUCOR), that generates the perturbed datasets by a mixture distribution and combines the information of similarities between sets of selected features from these perturbed datasets and the original dataset.

Conclusion

Empirical results support that AUCOR can effectively rank the DE methods in terms of stability for given RNA-seq datasets. In addition, we explore how biological or technical factors from experiments and data analysis affect the stability of DE methods. AUCOR is implemented in the open-source R package AUCOR, with source code freely available at https://github.com/linbingqing/stableDE.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5390-6) contains supplementary material, which is available to authorized users.

Identification of Cell Types from Single-Cell Transcriptomic Data

Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.

Trajectory Algorithms to Infer Stem Cell Fate Decisions

Single-cell trajectory analysis is an active research area in single-cell genomics aiming at developing sophisticated algorithms to reconstruct complex cell-state transition trajectories. Here, we present a step-by-step protocol to use CellRouter, a multifaceted single-cell analysis platform that integrates subpopulation identification, gene regulatory networks, and trajectory inference to precisely and flexibly reconstruct complex single-cell trajectories. Subpopulations are either user-defined or identified by a graph-clustering approach in which a k-nearest neighbor graph (kNN) is created from cell-to-cell distances in a low-dimensional embedding. Edges in this graph are weighted by network similarity metrics (e.g., Jaccard index) to robustly encode phenotypic relatedness, creating a representation of single-cell transcriptomes suitable for community detection algorithms to identify clusters of densely connected cells. This subpopulation structure represents a map of putative cell-state transitions. CellRouter implements a flow network algorithm to explore this map and reconstruct cell-state transitions in complex single-cell, multidimensional omics datasets. We describe a step-by-step application of CellRouter to hematopoietic stem and progenitor cell differentiation toward four major lineages-erythrocytes, megakaryocytes, monocytes, and granulocytes-to demonstrate key components of CellRouter for single-cell trajectory analysis.

Differential Expression Analysis in Single-Cell Transcriptomics

Differential expression analysis is an important aspect of bulk RNA sequencing (RNAseq). A lot of tools are available, and among them DESeq2 and edgeR are widely used. Since single-cell RNA sequencing (scRNAseq) expression data are zero inflated, single-cell data are quite different from those generated by conventional bulk RNA sequencing. Comparative analysis of tools used to detect differentially expressed genes between two groups of single cells showed that edgeR with quasi-likelihood F-test (QLF) outperforms other methods.In bulk RNAseq, differential expression is mainly used to compare limited number of replicates of two or more biological conditions. However, scRNAseq differential expression analysis might be also instrumental to identify the main players of cells subpopulation organization, thus requiring the use of multiple comparisons tools. Nowadays, edgeR is one of the few tools that are able to handle both zero inflated matrices and multiple comparisons. Here, we provide a guide to the use of edgeR as a tool to detect differential expression in single-cell data.

Correction of gene model annotations improves isoform abundance estimates: the example of ketohexokinase ( Khk)

Next generation sequencing protocols such as RNA-seq have made the genome-wide characterization of the transcriptome a crucial part of many research projects in biology. Analyses of the resulting data provide key information on gene expression and in certain cases on exon or isoform usage. The emergence of transcript quantification software such as Salmon has enabled researchers to efficiently estimate isoform and gene expressions across the genome while tremendously reducing the necessary computational power. Although overall gene expression estimations were shown to be accurate, isoform expression quantifications appear to be a more challenging task. Low expression levels and uneven or insufficient coverage were reported as potential explanations for inconsistent estimates. Here, through the example of the ketohexokinase ( Khk) gene in mouse, we demonstrate that the use of an incorrect gene annotation can also result in erroneous isoform quantification results. Manual correction of the input Khk gene model provided a much more accurate estimation of relative Khk isoform expression when compared to quantitative PCR (qPCR measurements). In particular, removal of an unexpressed retained intron and a proper adjustment of the 5’ and 3’ untranslated regions both had a strong impact on the correction of erroneous estimates. Finally, we observed a better concordance in isoform quantification between datasets and sequencing strategies when relying on the newly generated Khk annotations. These results highlight the importance of accurate gene models and annotations for correct isoform quantification and reassert the need for orthogonal methods of estimation of isoform expression to confirm important findings.

VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.

Transcriptional Programming of Normal and Inflamed Human Epidermis at Single-Cell Resolution

Perturbations in the transcriptional programs specifying epidermal differentiation cause diverse skin pathologies ranging from impaired barrier function to inflammatory skin disease. However, the global scope and organization of this complex cellular program remain undefined. Here we report single-cell RNA sequencing profiles of 92,889 human epidermal cells from 9 normal and 3 inflamed skin samples. Transcriptomics-derived keratinocyte subpopulations reflect classic epidermal strata but also sharply compartmentalize epithelial functions such as cell-cell communication, inflammation, and WNT pathway modulation. In keratinocytes, ∼12% of assessed transcript expression varies in coordinate patterns, revealing undescribed gene expression programs governing epidermal homeostasis. We also identify molecular fingerprints of inflammatory skin states, including S100 activation in the interfollicular epidermis of normal scalp, enrichment of a CD1C+CD301A+ myeloid dendritic cell population in psoriatic epidermis, and IL1βhiCCL3hiCD14+ monocyte-derived macrophages enriched in foreskin. This compendium of RNA profiles provides a critical step toward elucidating epidermal diseases of development, differentiation, and inflammation.

scClustViz - Single-cell RNAseq cluster assessment and visualization

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data.

The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types.

scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

scClustViz - Single-cell RNAseq cluster assessment and visualization

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

Single-cell genomics to guide human stem cell and tissue engineering

To understand human development and disease, as well as to regenerate damaged tissues, scientists are working to engineer certain cell types in vitro and to create 3D microenvironments in which cells behave physiologically. Single-cell genomics (SCG) technologies are being applied to primary human organs and to engineered cells and tissues to generate atlases of cell diversity in these systems at unparalleled resolution. Moving beyond atlases, SCG methods are powerful tools for gaining insight into the engineering and disease process. Here we discuss how scientists can use single-cell sequencing to optimize human cell and tissue engineering by measuring precision, detecting inefficiencies, and assessing accuracy. We also provide a perspective on how emerging SCG methods can be used to reverse-engineer human cells and tissues and unravel disease mechanisms.