Bayesian inference of gene expression states from single-cell RNA-seq data

Analysis of a Single Cell RNA-seq Workflow by Random Matrix Theory Methods

Single cell RNA-seq (scRNAseq) workflows typically start with a count matrix and end with the clustering of sampled cells. While a range of methods have been developed to cluster scRNAseq datasets, no theoretical tools exist to explain why a particular cluster exists or why a hypothesized cluster is missing. Recently, several authors have shown that eigenvalues of scRNAseq count matrices can be approximated using random matrix models. In this work, we extend these previous works to the study of a scRNAseq workflow. We model scaled count matrices using random matrices with normally distributed entries. Using these random matrix models, we quantify the differential expression of a cluster and develop predictions for the workflow, and in particular clustering, as a function of the differential expression. We also use results from random matrix theory (RMT) to develop predictive formulas for portions of the scRNAseq workflow. Using simulated and real datasets, we show that our predictions are accurate if certain conditions hold on differential expression, with our RMT based predictions requiring particularly stringent condition. We find that real datasets violate these conditions, leading to bias in our predictions, but our predictions are better than a naive estimator and we point out future work that can improve the predictions. To our knowledge, our formulas represents the first predictive results for scRNAseq workflows.

Cell-specific priors rescue differential gene expression in spatial spot-based technologies

Spatial transcriptomics (ST), a breakthrough technology, captures the complex structure and state of tissues through the spatial profiling of gene expression. A variety of ST technologies have now emerged, most prominently spot-based platforms such as Visium. Despite the widespread use of ST and its distinct data characteristics, the vast majority of studies continue to analyze ST data using algorithms originally designed for older technologies such as single-cell (SC) and bulk RNA-seq-particularly when identifying differentially expressed genes (DEGs). However, it remains unclear whether these algorithms are still valid or appropriate for ST data. Therefore, here, we sought to characterize the performance of these methods by constructing an in silico simulator of ST data with a controllable and known DEG ground truth. Surprisingly, our findings reveal little variation in the performance of classic DEG algorithms-all of which fail to accurately recapture known DEGs to significant levels. We further demonstrate that cellular heterogeneity within spots is a primary cause of this poor performance and propose a simple gene-selection scheme, based on prior knowledge of cell-type specificity, to overcome this. Notably, our approach outperforms existing data-driven methods designed specifically for ST data and offers improved DEG recovery and reliability rates. In summary, our work details a conceptual framework that can be used upstream, agnostically, of any DEG algorithm to improve the accuracy of ST analysis and any downstream findings.

PolyASite v3.0: a multi-species atlas of polyadenylation sites inferred from single-cell RNA-sequencing data

The broadly used 10X Genomics technology for single-cell RNA sequencing (scRNA-seq) captures RNA 3' ends. Thus, some reads contain part of the non-templated polyadenosine tails, providing direct evidence for the sites of 3' end cleavage and polyadenylation on the respective RNAs. Taking advantage of this property, we recently developed the SCINPAS workflow to infer polyadenylation sites (PASs) from scRNA-seq data. Here, we used this workflow to construct version 3.0 (v3.0, https://polyasite.unibas.ch/) of the PolyASite Atlas from a big compendium of publicly available human, mouse and worm scRNA-seq datasets obtained from healthy tissues. As the resolution of scRNA-seq was too low for robust detection of cell-level differences in PAS usage, we aggregated samples based on their tissue-of-origin to construct tissue-level catalogs of PASs. These provide qualitatively new information about PAS usage, in comparison to the previous PAS catalogs that were based on bulk 3' end sequencing experiments primarily in cell lines. In the new version, we document stringency levels associated with each PAS so that users can balance sensitivity and specificity in their analysis. We also upgraded the integration with the UCSC Genome Browser and developed track hubs conveniently displaying pooled and tissue-specific expression of PASs.

Investigation of the motif activity of transcription regulators in pancreatic β-like cell subpopulations differentiated from human induced pluripotent stem cells

Pancreatic β-cells are composed of different subtypes that play a key role in the control of insulin secretion and thereby control glucose homeostasis. In vitro differentiation of human induced pluripotent stem cells (hiPSCs) into 3D spheroids leads to the generation of β-cell subtypes and thus to the development of islet-like structures. Using this cutting-edge cell model, the aim of the study was to decipher the signaling signature that underlines β-cell subtypes, with a focus on the search for the activity of motifs of important transcription regulators (TRs). The investigation was performed using data from previous single-cell sequencing analysis introduced into the integrated system for motif activity response analysis (ISMARA) of transcription regulators. We extracted the matrix of important TRs activated in the β-cell subpopulation and bi-hormonal-like β-cells. Based on these TRs and their targets, we built specific regulatory networks for main cell subpopulations. Our data confirmed the transcriptomic heterogeneity of the β-cell subtype lineage and suggested a mechanism that could account for the differentiation of β-cell subtypes during pancreas development. We do believe that our findings could be instrumental for understanding the mechanisms that affect the balance of β-cell subtypes, leading to impaired insulin secretion in type 2 diabetes.

Scalable identification of lineage-specific gene regulatory networks from metacells with NetID

The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene-gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.

Physical modeling of embryonic transcriptomes identifies collective modes of gene expression

Starting from one totipotent cell, complex multicellular organisms form through a series of differentiation and morphogenetic events, culminating in a multitude of cell types arranged in a functional and intricate spatial pattern. To do so, cells coordinate with each other, resulting in dynamics which follow a precise developmental trajectory, constraining the space of possible embryo-to-embryo variation. Using recent single-cell sequencing data of early ascidian embryos, we leverage natural variation together with modeling and inference techniques from statistical physics to investigate development at the level of a complete interconnected embryo - an embryonic transcriptome. After developing a robust and biophysically motivated approach to identifying distinct transcriptomic states or cell types, a statistical analysis reveals correlations within embryos and across cell types demonstrating the presence of collective variation. From these intra-embryo correlations, we infer minimal networks of cell-cell interactions, which reveal the collective modes of gene expression. Our work demonstrates how the existence and nature of spatial interactions along with the collective modes of expression that they give rise to can be inferred from single-cell gene expression measurements, opening up a wider range of biological questions that can be addressed using sequencing-based modalities.

Compound models and Pearson residuals for single-cell RNA-seq data without UMIs

Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI data. To extend this approach to non-UMI data, we model the additional amplification step with a compound distribution: we assume that sequenced RNA molecules follow a negative binomial distribution, and are then replicated following an amplification distribution. We show how this model leads to compound Pearson residuals, which yield meaningful gene selection and embeddings of Smart-seq2 datasets. Further, we suggest that amplification distributions across several sequencing protocols can be described by a broken power law. The resulting compound model captures previously unexplained overdispersion and zero-inflation patterns in non-UMI data.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations

Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections

Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections

Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Differentiation is accompanied by a progressive loss in transcriptional memory

BACKGROUND

Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process.

RESULTS

In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq).

CONCLUSIONS

We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process.

An image-guided microfluidic system for single-cell lineage tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis.

ProtoCell4P: an explainable prototype-based neural network for patient classification using single-cell RNA-seq

MOTIVATION

The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients' phenotypes using scRNA-seq data. However, we are facing critical challenges in designing deep learning models for classifying patient samples due to (i) the samples collected in the same dataset contain a variable number of cells-some samples might only have hundreds of cells sequenced while others could have thousands of cells, and (ii) the number of samples available is typically small and the expression profile of each cell is noisy and extremely high-dimensional. Moreover, the black-box nature of existing deep learning models makes it difficult for the researchers to interpret the models and extract useful knowledge from them.

RESULTS

We propose a prototype-based and cell-informed model for patient phenotype classification, termed ProtoCell4P, that can alleviate problems of the sample scarcity and the diverse number of cells by leveraging the cell knowledge with representatives of cells (called prototypes), and precisely classify the patients by adaptively incorporating information from different cells. Moreover, this classification process can be explicitly interpreted by identifying the key cells for decision making and by further summarizing the knowledge of cell types to unravel the biological nature of the classification. Our approach is explainable at the single-cell resolution which can identify the key cells in each patient's classification. The experimental results demonstrate that our proposed method can effectively deal with patient classifications using single-cell data and outperforms the existing approaches. Furthermore, our approach is able to uncover the association between cell types and biological classes of interest from a data-driven perspective.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Teddy-XiongGZ/ProtoCell4P.

Characterization of cell-fate decision landscapes by estimating transcription factor dynamics

Time-specific modulation of gene expression during differentiation by transcription factors promotes cell diversity. However, estimating their dynamic regulatory activity at the single-cell level and in a high-throughput manner remains challenging. We present FateCompass, an integrative approach that utilizes single-cell transcriptomics data to identify lineage-specific transcription factors throughout differentiation. By combining a probabilistic framework with RNA velocities or differentiation potential, we estimate transition probabilities, while a linear model of gene regulation is employed to compute transcription factor activities. Considering dynamic changes and correlations of expression and activities, FateCompass identifies lineage-specific regulators. Our validation using in silico data and application to pancreatic endocrine cell differentiation datasets highlight both known and potentially novel lineage-specific regulators. Notably, we uncovered undescribed transcription factors of an enterochromaffin-like population during in vitro differentiation toward ß-like cells. FateCompass provides a valuable framework for hypothesis generation, advancing our understanding of the gene regulatory networks driving cell-fate decisions.

EMBED: Essential MicroBiomE Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

Dimensionality reduction offers unique insights into high-dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa levels are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic nonlinear tensor factorization approach. Like normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. Using multiple real and synthetic datasets, we show that a very small number of ECNs can accurately approximate microbiome dynamics. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.

Emergence of division of labor in tissues through cell interactions and spatial cues

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

Comparison of transformations for single-cell RNA-seq data

The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here, we describe four transformation approaches based on the delta method, model residuals, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however, in benchmarks using simulated and real-world data, it turns out that a rather simple approach, namely, the logarithm with a pseudo-count followed by principal-component analysis, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.

Emergent statistical laws in single-cell transcriptomic data

Large-scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology, or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique.

Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data

Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.

Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data

The long-distance transport of messenger RNAs (mRNAs) has been shown to be important for several developmental processes in plants. A popular method for identifying travelling mRNAs is to perform RNA-Seq on grafted plants. This approach depends on the ability to correctly assign sequenced mRNAs to the genetic background from which they originated. The assignment is often based on the identification of single-nucleotide polymorphisms (SNPs) between otherwise identical sequences. A major challenge is therefore to distinguish SNPs from sequencing errors. Here, we show how Bayes factors can be computed analytically using RNA-Seq data over all the SNPs in an mRNA. We used simulations to evaluate the performance of the proposed framework and demonstrate how Bayes factors accurately identify graft-mobile transcripts. The comparison with other detection methods using simulated data shows how not taking the variability in read depth, error rates and multiple SNPs per transcript into account can lead to incorrect classification. Our results suggest experimental design criteria for successful graft-mobile mRNA detection and show the pitfalls of filtering for sequencing errors or focusing on single SNPs within an mRNA.

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis include many data types and file formats, and a wide variety of computational tools that can be applied alone or together as pipelines. This paper presents a review of the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. This review aims to address mainly the aspects involved in the differentially expressed gene (DEG) analysis from RNA sequencing data (RNA-seq), considering the computational methods. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.

Density-based detection of cell transition states to construct disparate and bifurcating trajectories

Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.

scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information

Background

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data.

Results

We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types.

Conclusions

scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00886-4.

Interpretable generative deep learning: an illustration with single cell gene expression data

Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered by various approaches, such as variational auto-encoders, are useful to get a better understanding of the relations between observed gene expressions and experimental factors or phenotypes. Furthermore, by providing a generative model for the latent and observed variables, deep generative models can generate synthetic observations, which allow us to assess the uncertainty in the learned representations. While deep generative models are useful to learn the structure of high-dimensional omics data by efficiently capturing non-linear dependencies between genes, they are sometimes difficult to interpret due to their neural network building blocks. More precisely, to understand the relationship between learned latent variables and observed variables, e.g., gene transcript abundances and external phenotypes, is difficult. Therefore, we also illustrate current approaches that allow us to infer the relationship between learned latent variables and observed variables as well as external phenotypes. Thereby, we render deep learning approaches more interpretable. In an application with single-cell gene expression data, we demonstrate the utility of the discussed methods.

RNA velocity unraveled

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

A novel approach to single-cell analysis reveals intrinsic differences in immune marker expression in unstimulated BALB/c and C57BL/6 macrophages

The origin of functional heterogeneity among macrophages, key innate immune system components, is still debated. While mouse strains differ in their immune responses, the range of gene expression variation among their pre-stimulation macrophages is unknown. With a novel approach to scRNA-seq analysis, we reveal the gene expression variation in unstimulated macrophage populations from BALB/c and C57BL/6 mice. We show that intrinsic strain-to-strain differences are detectable before stimulation and we place the unstimulated single cells within the gene expression landscape of stimulated macrophages. C57BL/6 mice show stronger evidence of macrophage polarization than BALB/c mice, which may contribute to their relative resistance to pathogens. Our computational methods can be generally adopted to uncover biological variation between cell populations.

Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Background

Single cell RNA-sequencing (scRNA-seq) has very rapidly become the new workhorse of modern biology providing an unprecedented global view on cellular diversity and heterogeneity. In particular, the structure of gene-gene expression correlation contains information on the underlying gene regulatory networks. However, interpretation of scRNA-seq data is challenging due to specific experimental error and biases that are unique to this kind of data including drop-out (or technical zeros).

Methods

To deal with this problem several methods for imputation of zeros for scRNA-seq have been developed. However, it is not clear how these processing steps affect inference of genetic networks from single cell data. Here, we introduce Biomodelling.jl, a tool for generation of synthetic scRNA-seq data using multiscale modelling of stochastic gene regulatory networks in growing and dividing cells.

Results

Our tool produces realistic transcription data with a known ground truth network topology that can be used to benchmark different approaches for gene regulatory network inference. Using this tool we investigate the impact of different imputation methods on the performance of several network inference algorithms.

Conclusions

Biomodelling.jl provides a versatile and useful tool for future development and benchmarking of network inference approaches using scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04778-9

Antibody repertoire sequencing analysis

High-throughput sequencing for B cell receptor (BCR) repertoire provides useful insights for the adaptive immune system. With the continuous development of the BCR-seq technology, many efforts have been made to develop methods for analyzing the ever-increasing BCR repertoire data. In this review, we comprehensively outline different BCR repertoire library preparation protocols and summarize three major steps of BCR-seq data analysis, i. e., V(D)J sequence annotation, clonal phylogenetic inference, and BCR repertoire profiling and mining. Different from other reviews in this field, we emphasize background intuition and the statistical principle of each method to help biologists better understand it. Finally, we discuss data mining problems for BCR-seq data and with a highlight on recently emerging multiple-sample analysis.

On the relation between input and output distributions of scRNA-seq experiments

MOTIVATION

Single-cell RNA sequencing determines RNA copy numbers per cell for a given gene. However, technical noise poses the question how observed distributions (output) are connected to their cellular distributions (input).

RESULTS

We model a single-cell RNA sequencing setup consisting of PCR amplification and sequencing, and derive probability distribution functions for the output distribution given an input distribution. We provide copy number distributions arising from single transcripts during PCR amplification with exact expressions for mean and variance. We prove that the coefficient of variation of the output of sequencing is always larger than that of the input distribution. Experimental data reveals the variance and mean of the input distribution to obey characteristic relations, which we specifically determine for a HeLa dataset. We can calculate as many moments of the input distribution as are known of the output distribution (up to all). This, in principle, completely determines the input from the output distribution.

AVAILABILITY AND IMPLEMENTATION

Source code freely available at https://github.com/danielschw188/InputOutputSCRNASeq.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

baredSC: Bayesian approach to retrieve expression distribution of single-cell data

Background

The number of studies using single-cell RNA sequencing (scRNA-seq) is constantly growing. This powerful technique provides a sampling of the whole transcriptome of a cell. However, sparsity of the data can be a major hurdle when studying the distribution of the expression of a specific gene or the correlation between the expressions of two genes.

Results

We show that the main technical noise associated with these scRNA-seq experiments is due to the sampling, i.e., Poisson noise. We present a new tool named baredSC, for Bayesian Approach to Retrieve Expression Distribution of Single-Cell data, which infers the intrinsic expression distribution in scRNA-seq data using a Gaussian mixture model. baredSC can be used to obtain the distribution in one dimension for individual genes and in two dimensions for pairs of genes, in particular to estimate the correlation in the two genes’ expressions. We apply baredSC to simulated scRNA-seq data and show that the algorithm is able to uncover the expression distribution used to simulate the data, even in multi-modal cases with very sparse data. We also apply baredSC to two real biological data sets. First, we use it to measure the anti-correlation between Hoxd13 and Hoxa11, two genes with known genetic interaction in embryonic limb. Then, we study the expression of Pitx1 in embryonic hindlimb, for which a trimodal distribution has been identified through flow cytometry. While other methods to analyze scRNA-seq are too sensitive to sampling noise, baredSC reveals this trimodal distribution.

Conclusion

baredSC is a powerful tool which aims at retrieving the expression distribution of few genes of interest from scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04507-8.

Shortening of 3' UTRs in most cell types composing tumor tissues implicates alternative polyadenylation in protein metabolism

During pre-mRNA maturation 3' end processing can occur at different polyadenylation sites in the 3' untranslated region (3' UTR) to give rise to transcript isoforms that differ in the length of their 3' UTRs. Longer 3' UTRs contain additional cis-regulatory elements that impact the fate of the transcript and/or of the resulting protein. Extensive alternative polyadenylation (APA) has been observed in cancers, but the mechanisms and roles remain elusive. In particular, it is unclear whether the APA occurs in the malignant cells or in other cell types that infiltrate the tumor. To resolve this, we developed a computational method, called SCUREL, that quantifies changes in 3' UTR length between groups of cells, including cells of the same type originating from tumor and control tissue. We used this method to study APA in human lung adenocarcinoma (LUAD). SCUREL relies solely on annotated 3' UTRs and on control systems such as T cell activation, and spermatogenesis gives qualitatively similar results at much greater sensitivity compared to the previously published scAPA method. In the LUAD samples, we find a general trend toward 3' UTR shortening not only in cancer cells compared to the cell type of origin, but also when comparing other cell types from the tumor vs. the control tissue environment. However, we also find high variability in the individual targets between patients. The findings help in understanding the extent and impact of APA in LUAD, which may support improvements in diagnosis and treatment.

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

BACKGROUND

Standard preprocessing of single-cell RNA-seq UMI data includes normalization by sequencing depth to remove this technical variability, and nonlinear transformation to stabilize the variance across genes with different expression levels. Instead, two recent papers propose to use statistical count models for these tasks: Hafemeister and Satija (Genome Biol 20:296, 2019) recommend using Pearson residuals from negative binomial regression, while Townes et al. (Genome Biol 20:295, 2019) recommend fitting a generalized PCA model. Here, we investigate the connection between these approaches theoretically and empirically, and compare their effects on downstream processing.

RESULTS

We show that the model of Hafemeister and Satija produces noisy parameter estimates because it is overspecified, which is why the original paper employs post hoc smoothing. When specified more parsimoniously, it has a simple analytic solution equivalent to the rank-one Poisson GLM-PCA of Townes et al. Further, our analysis indicates that per-gene overdispersion estimates in Hafemeister and Satija are biased, and that the data are in fact consistent with the overdispersion parameter being independent of gene expression. We then use negative control data without biological variability to estimate the technical overdispersion of UMI counts, and find that across several different experimental protocols, the data are close to Poisson and suggest very moderate overdispersion. Finally, we perform a benchmark to compare the performance of Pearson residuals, variance-stabilizing transformations, and GLM-PCA on scRNA-seq datasets with known ground truth.

CONCLUSIONS

We demonstrate that analytic Pearson residuals strongly outperform other methods for identifying biologically variable genes, and capture more of the biologically meaningful variation when used for dimensionality reduction.