Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery and characterization of rare cell types. However, such assays raise challenging statistical and computational questions and require the development of novel methodology and software. Using stem cell differentiation in the mouse olfactory epithelium as a case study, this integrated workflow provides a step-by-step tutorial to the methodology and associated software for the following four main tasks: (1) dimensionality reduction accounting for zero inflation and over dispersion and adjusting for gene and cell-level covariates; (2) cell clustering using resampling-based sequential ensemble clustering; (3) inference of cell lineages and pseudotimes; and (4) differential expression analysis along lineages.

The original PDF version of this Article contained errors in two equations. In Eq. (1), all Γ symbols were inadvertently omitted. In the second equation in the subsection entitled '1. Dispersion optimization' within the Methods section 'ZINB-WaVE estimation procedure', all Ψ symbols were inadvertently omitted. These errors have been corrected in the PDF version of the Article; the HTML version was correct from the time of publication.

Understanding the genetic and molecular bases of the ability to distinguish self from nonself (allorecognition) and mechanisms underlying evolution of allorecognition systems is an important endeavor for understanding cases where it becomes dysfunctional, such as in autoimmune disorders. In filamentous fungi, allorecognition can result in vegetative or heterokaryon incompatibility, which is a type of programmed cell death that occurs following fusion of genetically different cells. Allorecognition is genetically controlled by het loci, with coexpression of any combination of incompatible alleles triggering vegetative incompatibility. Herein, we identified, characterized, and inferred the evolutionary history of candidate het loci in the filamentous fungus Neurospora crassa. As characterized het loci encode proteins carrying an HET domain, we annotated HET domain genes in 25 isolates from a natural population along with the N. crassa reference genome using resequencing data. Because allorecognition systems can be affected by frequency-dependent selection favoring rare alleles (i.e., balancing selection), we mined resequencing data for HET domain loci whose alleles displayed elevated levels of variability, excess of intermediate frequency alleles, and deep gene genealogies. From these analyses, 34 HET domain loci were identified as likely to be under balancing selection. Using transformation, incompatibility assays and genetic analyses, we determined that one of these candidates functioned as a het locus (het-e). The het-e locus has three divergent allelic groups that showed signatures of positive selection, intra- and intergroup recombination, and trans-species polymorphism. Our findings represent a compelling case of balancing selection functioning on multiple alleles across multiple loci potentially involved in allorecognition.

Motivation

Genome-wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single-nucleotide polymorphisms to mobile genetic elements. This approach does not require a reference genome, making it easier to account for accessory genes. However, a same gene can exist in slightly different versions across different strains, leading to diluted effects.

Results

Here, we overcome this issue by testing covariates built from closed connected subgraphs (CCSs) of the de Bruijn graph defined over genomic k-mers. These covariates capture polymorphic genes as a single entity, improving k-mer-based GWAS both in terms of power and interpretability. However, a method naively testing all possible subgraphs would be powerless due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all CCSs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. Our method integrates with existing visual tools to facilitate interpretation.

Availability and implementation

We provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_ISMB.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.