Revealing allele-specific gene expression by single-cell transcriptomics

Single-cell sequencing has emerged as a revolutionary method that reveals biological processes with unprecedented resolution and scale, and has already greatly impacted biology and medicine. To investigate processes such as alternative splicing, novel exon detection and allele-specific expression (ASE), full-length based single-cell RNA-seq methods are required for broad sequence coverage and single nucleotide polymorphism (SNP) identification. In this review, we revisit recent achievements from studies that used single-cell RNA-seq to advance our understanding of ASE in the context of both autosomal and X-chromosome genes. We also recapitulate useful bioinformatic tools developed to identify haplotype phase.     

scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

In gene expression profiling studies, including single-cell RNA sequencing(sc RNA-seq)analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in sc RNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model(sc LM), a gene coclustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, sc LM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. sc LM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that sc LM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of sc LM, we apply it to our in-house and public experimental sc RNA-seq datasets. sc LM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the sc LM method is available at https://github.com/QSong-github/sc LM.     

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method,reveals non-genetic gene-expression heterogeneity

Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogeneity, and can detect different cell types and different cell-cycle phases of a single cell type. Moreover, this method can comprehensively reveal gene-expression heterogeneity between single cells of the same cell type in the same cell-cycle phase.     

单细胞转录组技术及其在人类细胞图谱构建中的应用

单细胞转录组技术在单细胞水平上进行转录组测序,提供了单个细胞的基因表达差异信息,使在单细胞尺度下研究个体细胞、相关环境细胞及其相互作用的机理成为可能.近年来,单细胞转录组技术在c DNA扩增原理上经历了从末端加尾、体外逆转录到模板置换的方法发展,大大提高了基因检测的数量、基因表达的准确性等.同时,在单细胞选取方式上进行了从96/384孔板到油包水液滴以及纳米微孔的创新,在提高通量和重复性的同时降低了整体实验成本.单细胞转录组技术广泛应用于细胞群体分类和异质性研究,推动了从发育生物学到正常、病态组织细胞图谱的构建.本文对单细胞转录组技术近年的技术进展以及在人类细胞图谱构建中的应用进行了综述.     

Methods for predicting single-cell miRNA in breast cancer

It has been demonstrated that miRNAs are involved in many biological processes including cell proliferation and differentiation, apoptosis, and stress responses. Although single-cell RNA sequencing technology is prevailing nowadays, it still remains challenging in quantifying miRNA at the single-cell level. Herein, we present the computational methods to infer the single-cell miRNA expression level using its target gene abundances. Firstly, we developed an enrichment-based approach in estimating miRNA expression considering miRNA-mRNA regulation information and miRNA-mRNA correlation signal captured from existing TCGA datasets. Further efforts were made to infer the miRNA expression with machine learning models. The methods were applied to compare the accuracy and robustness with the simulated single-cell data. Finally, we applied the method in single-cell RNA-seq triple negative breast cancer (TNBC) patients to further discover miRNA marker at the single-cell level for the malignant cells. Our tool is available online at: https://github.com/ChengkuiZhao/Single-cell-miRNA-prediction.     

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here, we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.     

The importance of study design for detecting differentially abundant features in high-throughput experiments

High-throughput assays, such as RNA-seq, to detect differential abundance are widely used. Variable performance across statistical tests, normalizations, and conditions leads to resource wastage and reduced sensitivity. EDDA represents a first, general design tool for RNA-seq, Nanostring, and metagenomic analysis, that rationally selects tests, predicts performance, and plans experiments to minimize resource wastage. Case studies highlight EDDA’s ability to model single-cell RNA-seq, suggesting ways to reduce sequencing costs up to five-fold and improving metagenomic biomarker detection through improved test selection. EDDA’s novel mode-based normalization for detecting differential abundance improves robustness by 10% to 20% and precision by up to 140%.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0527-7) contains supplementary material, which is available to authorized users.     

Emerging deep learning methods for single-cell RNA-seq data analysis

Deep learning is making major breakthrough in several areas of bioinformatics. Anticipating that this will occur soon for the single-cell RNA-seq data analysis, we review newly published deep learning methods that help tackle computational challenges. Autoencoders are found to be the dominant approach. However, methods based on deep generative models such as generative adversarial networks (GANs) are also emerging in this area.     

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.