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BackgroundThe aim of this study was to identify serum miRNAs that discriminate early gastric cancer (EGC) samples from non-cancer controls using a large cohort.MethodsThis retrospective case–control study included 1417 serum samples from patients with EGC (seen at the National Cancer Center Hospital in Tokyo between 2008 and 2012) and 1417 age- and gender-matched non-cancer controls. The samples were randomly assigned to discovery and validation sets and the miRNA expression profiles of whole serum samples were comprehensively evaluated using a highly sensitive DNA chip (3D-Gene®) designed to detect 2565 miRNA sequences. Diagnostic models were constructed using the levels of several miRNAs in the discovery set, and the diagnostic performance of the model was evaluated in the validation set.ResultsThe discovery set consisted of 708 samples from EGC patients and 709 samples from non-cancer controls, and the validation set consisted of 709 samples from EGC patients and 708 samples from non-cancer controls. The diagnostic EGC index was constructed using four miRNAs (miR-4257, miR-6785-5p, miR-187-5p, and miR-5739). In the discovery set, a receiver operating characteristic curve analysis of the EGC index revealed that the area under the curve (AUC) was 0.996 with a sensitivity of 0.983 and a specificity of 0.977. In the validation set, the AUC for the EGC index was 0.998 with a sensitivity of 0.996 and a specificity of 0.953.ConclusionsA novel combination of four serum miRNAs could be a useful non-invasive diagnostic biomarker to detect EGC with high accuracy. A multicenter prospective study is ongoing to confirm the present observations.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2 , TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2 , TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2 , TMPRSS2 and CTSL . Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2 + TMPRSS2 + cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial–macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention. An integrated analysis of over 100 single-cell and single-nucleus transcriptomics studies illustrates severe acute respiratory syndrome coronavirus 2 viral entry gene coexpression patterns across different human tissues, and shows association of age, smoking status and sex with viral entry gene expression in respiratory cell populations.

A comparison framework applied to 15 single-cell RNA-seq protocols reveals differences in accuracy and sensitivity and discusses the utility of RNA spike-in standards. Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers ( https://github.com/vals/umis/ ). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.

Salmon is a computational tool that uses sample-specific models and a dual-phase inference procedure to correct biases in RNA-seq data and rapidly quantify transcript abundances. We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA–seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.

One primary reason that makes single-cell RNA-seq analysis challenging is dropouts, where the data only captures a small fraction of the transcriptome of each cell. Almost all computational algorithms developed for single-cell RNA-seq adopted gene selection, dimension reduction or imputation to address the dropouts. Here, an opposite view is explored. Instead of treating dropouts as a problem to be fixed, we embrace it as a useful signal. We represent the dropout pattern by binarizing single-cell RNA-seq count data, and present a co-occurrence clustering algorithm to cluster cells based on the dropout pattern. We demonstrate in multiple published datasets that the binary dropout pattern is as informative as the quantitative expression of highly variable genes for the purpose of identifying cell types. We expect that recognizing the utility of dropouts provides an alternative direction for developing computational algorithms for single-cell RNA-seq analysis. The analysis of RNA-seq data is complicated by dropouts, and these are usually treated as a problem to be addressed. Here, Peng Qiu uses dropouts as a source of information and presents a co-occurrence clustering algorithm to cluster cells based on the dropout pattern; this could be a complementary approach to existing methods.

The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the underlying biology, from cell type composition to gene regulation to developmental dynamics. At the same time, rapid growth has forced continuous reevaluation of the underlying statistical models, experimental aims, and sheer volumes of data processing that are handled by these computational tools. Here, I review key computational steps of single-cell RNA sequencing (scRNA-seq) analysis, examine assumptions made by different approaches, and highlight successes, remaining ambiguities, and limitations that are important to keep in mind as scRNA-seq becomes a mainstream technique for studying biology.This review provides an overview of recent computational developments in scRNA-seq analysis and highlights packages and tools applied in executing these analyses.

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrate that our ROGUE metric is broadly applicable, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast, B cell and brain data, we identify additional subtypes and demonstrate the application of ROGUE-guided analyses to detect precise signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas. Single cell RNA-seq is a powerful method to assign cell identity, but the purity of cell clusters arising from this data is not clear. Here the authors present an entropy-based statistic called ROGUE to quantify the purity of cell clusters, and identify subtypes within clusters.

A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets. Missing values in scRNA-seq datasets can bias their analysis. Here, the authors threshold the low rank approximation of the expression matrix, so false zeros can be imputed while true zeros are preserved.

Artificial neural networks (ANN) are computing architectures with many interconnections of simple neural-inspired computing elements, and have been applied to biomedical fields such as imaging analysis and diagnosis. We have developed a new ANN framework called Cox-nnet to predict patient prognosis from high throughput transcriptomics data. In 10 TCGA RNA-Seq data sets, Cox-nnet achieves the same or better predictive accuracy compared to other methods, including Cox-proportional hazards regression (with LASSO, ridge, and mimimax concave penalty), Random Forests Survival and CoxBoost. Cox-nnet also reveals richer biological information, at both the pathway and gene levels. The outputs from the hidden layer node provide an alternative approach for survival-sensitive dimension reduction. In summary, we have developed a new method for accurate and efficient prognosis prediction on high throughput data, with functional biological insights. The source code is freely available at https://github.com/lanagarmire/cox-nnet.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems. Palantir uses single-cell RNA-seq data to generate continuous models of differentiation, infer developmental trajectories, and calculate the probabilities of cell fate choices.