High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.

Olfactory sensory neurons express just one out of a possible ∼ 1,000 odorant receptor genes, reflecting an exquisite mode of gene regulation. In one model, once an odorant receptor is chosen for expression, other receptor genes are suppressed by a negative feedback mechanism, ensuring a stable functional identity of the sensory neuron for the lifetime of the cell. The signal transduction mechanism subserving odorant receptor gene silencing remains obscure, however. Here, we demonstrate in the zebrafish that odorant receptor gene silencing is dependent on receptor activity. Moreover, we show that signaling through G protein βγ subunits is both necessary and sufficient to suppress the expression of odorant receptor genes and likely acts through histone methylation to maintain the silenced odorant receptor genes in transcriptionally inactive heterochromatin. These results link receptor activity with the epigenetic mechanisms responsible for ensuring the expression of one odorant receptor per olfactory sensory neuron.

A detailed understanding of the paths that stem cells traverse to generate mature progeny is vital for elucidating the mechanisms governing cell fate decisions and tissue homeostasis. Adult stem cells maintain and regenerate multiple mature cell lineages in the olfactory epithelium. Here we integrate single-cell RNA sequencing and robust statistical analyses with in vivo lineage tracing to define a detailed map of the postnatal olfactory epithelium, revealing cell fate potentials and branchpoints in olfactory stem cell lineage trajectories. Olfactory stem cells produce support cells via direct fate conversion in the absence of cell division, and their multipotency at the population level reflects collective unipotent cell fate decisions by single stem cells. We further demonstrate that Wnt signaling regulates stem cell fate by promoting neuronal fate choices. This integrated approach reveals the mechanisms guiding olfactory lineage trajectories and provides a model for deconstructing similar hierarchies in other stem cell niches.

The etiology of pediatric acute myeloid leukemia (AML) is largely unknown, but evidence for mutations in utero and long latency periods suggests that environmental factors play a role. Therefore, we used untargeted metabolomics of archived newborn dried blood spots (DBS) to investigate neonatal exposures as potential causal risk factors for AML. Untargeted metabolomics profiling was performed on DBS punches from 48 pediatric patients with AML and 46 healthy controls as part of the California Childhood Leukemia Study (CCLS). Because sex disparities are suggested by differences in AML incidence rates, metabolite features associated with AML were identified in analyses stratified by sex. There was no overlap between the 16 predictors of AML in females and 15 predictors in males, suggesting that neonatal metabolomic profiles of pediatric AML risk are sex-specific. In females, four predictors of AML were putatively annotated as ceramides, a class of metabolites that has been linked with cancer cell proliferation. In females, two metabolite predictors of AML were strongly correlated with breastfeeding duration, indicating a possible biological link between this putative protective risk factor and childhood leukemia. In males, a heterogeneous metabolite profile of AML predictors was observed. Replication with larger participant numbers is required to validate findings.

Drosophila melanogaster is one of the most well studied genetic model organisms; nonetheless, its genome still contains unannotated coding and non-coding genes, transcripts, exons and RNA editing sites. Full discovery and annotation are pre-requisites for understanding how the regulation of transcription, splicing and RNA editing directs the development of this complex organism. Here we used RNA-Seq, tiling microarrays and cDNA sequencing to explore the transcriptome in 30 distinct developmental stages. We identified 111,195 new elements, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events, and inferred protein isoforms that previously eluded discovery using established experimental, prediction and conservation-based approaches. These data substantially expand the number of known transcribed elements in the Drosophila genome and provide a high-resolution view of transcriptome dynamics throughout development.

Generation of cDNA using random hexamer priming induces biases in the nucleotide composition at the beginning of transcriptome sequencing reads from the Illumina Genome Analyzer. The bias is independent of organism and laboratory and impacts the uniformity of the reads along the transcriptome. We provide a read count reweighting scheme, based on the nucleotide frequencies of the reads, that mitigates the impact of the bias.

Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses.

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

Microarray technology is increasingly being applied in biological and medical research to address a wide range of problems, such as the classification of tumors. An important statistical problem associated with tumor classification is the identification of new tumor classes using gene-expression profiles. Two essential aspects of this clustering problem are: to estimate the number of clusters, if any, in a dataset; and to allocate tumor samples to these clusters, and assess the confidence of cluster assignments for individual samples. Here we address the first of these problems.

Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.

Alternative mRNA splicing adds a layer of regulation to the expression of thousands of genes in Drosophila melanogaster. Not all alternative splicing results in functional protein; it can also yield mRNA isoforms with premature stop codons that are degraded by the nonsense-mediated mRNA decay (NMD) pathway. This coupling of alternative splicing and NMD provides a mechanism for gene regulation that is highly conserved in mammals. NMD is also active in Drosophila, but its effect on the repertoire of alternative splice forms has been unknown, as has the mechanism by which it recognizes targets. Here, we have employed a custom splicing-sensitive microarray to globally measure the effect of alternative mRNA processing and NMD on Drosophila gene expression. We have developed a new algorithm to infer the expression change of each mRNA isoform of a gene based on the microarray measurements. This method is of general utility for interpreting splicing-sensitive microarrays and high-throughput sequence data. Using this approach, we have identified a high-confidence set of 45 genes where NMD has a differential effect on distinct alternative isoforms, including numerous RNA-binding and ribosomal proteins. Coupled alternative splicing and NMD decrease expression of these genes, which may in turn have a downstream effect on expression of other genes. The NMD-affected genes are enriched for roles in translation and mitosis, perhaps underlying the previously observed role of NMD factors in cell cycle progression. Our results have general implications for understanding the NMD mechanism in fly. Most notably, we found that the NMD-target mRNAs had significantly longer 3' untranslated regions (UTRs) than the nontarget isoforms of the same genes, supporting a role for 3' UTR length in the recognition of NMD targets in fly.

Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.

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.

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.

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.

Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration after injury-induced cell death. In this study, we used single cell sequencing to identify major regulatory players in determining olfactory epithelial stem cell fate after acute injury. We combined gene expression and accessible chromatin profiles of individual lineage traced olfactory stem cells to predict transcription factor activity specific to different lineages and stages of recovery. We further identified a discrete stem cell state that appears poised for activation, characterized by accessible chromatin around wound response and lineage specific genes prior to their later expression in response to injury. Together these results provide evidence that a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

The genome of Arabidopsis thaliana encodes more than 600 receptor-like kinase (RLK) genes, by far the dominant class of receptors found in land plants. Although similar to the mammalian receptor tyrosine kinases, plant RLKs are serine/threonine kinases that represent a novel signaling innovation unique to plants and, consequently, an excellent opportunity to understand how extracellular signaling evolved and functions in plants as opposed to animals. RLKs are predicted to be major components of the signaling pathways that allow plants to respond to environmental and developmental conditions. However, breakthroughs in identifying these processes have been limited to only a handful of individual RLKs. Here, we used a Syngenta custom Arabidopsis GeneChip array to compile a detailed profile of the transcriptional activity of 604 receptor-like kinase genes after exposure to a cross-section of known signaling factors in plants, including abiotic stresses, biotic stresses, and hormones. In the 68 experiments comprising the study, we found that 582 of the 604 RLK genes displayed a two-fold or greater change in expression to at least one of 12 types of treatments, thereby providing a large body of experimental evidence for targeted functional screens of individual RLK genes. We investigated whether particular subfamilies of RLK genes are responsive to specific types of signals and found that each subfamily displayed broad ranges of expression, as opposed to being targeted towards particular signal classes. Finally, by analyzing the divergence of sequence and gene expression among the RLK subfamilies, we present evidence as to the functional basis for the expansion of the RLKs and how this expansion may have affected conservation and divergences in their function. Taken as a whole, our study represents a preliminary, working model of processes and interactions in which the members of the RLK gene family may be involved, where such information has remained elusive for so many of its members.

Epidemiologists are beginning to employ metabolomics and lipidomics with archived blood from incident cases and controls to discover causes of cancer. Although several such studies have focused on colorectal cancer (CRC), they all followed targeted or semi-targeted designs that limited their ability to find discriminating molecules and pathways related to the causes of CRC.