Analysis Of Single-cell RNA-seq Data Research Articles

SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species.

Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is notquantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.

A case study on the detailed reproducibility of a Human Cell Atlas project

BackgroundReproducibility is a defining feature of a scientific discovery. Reproducibility can be at different levels for different types of study. The purpose of the Human Cell Atlas (HCA) project is to build maps of molecular signatures of all human cell types and states to serve as references for future discoveries. Constructing such a complex reference atlas must involve the assembly and aggregation of data from multiple labs, probably generated with different technologies. It has much higher requirements on reproducibility than individual research projects. To add another layer of complexity, the bioinformatics procedures involved for single‐cell data have high flexibility and diversity. There are many factors in the processing and analysis of single‐cell RNA‐seq data that can shape the final results in different ways.MethodsTo study what levels of reproducibility can be reached in current practices, we conducted a detailed reproduction study for a well‐documented recent publication on the atlas of human blood dendritic cells as an example to break down the bioinformatics steps and factors that are crucial for the reproducibility at different levels.ResultsWe found that the major scientific discovery can be well reproduced after some efforts, but there are also some differences in some details that may cause uncertainty in the future reference. This study provides a detailed case observation on the on‐going discussions of the type of standards the HCA community should take when releasing data and publications to guarantee the reproducibility and reliability of the future atlas.ConclusionCurrent practices of releasing data and publications may not be adequate to guarantee the reproducibility of HCA. We propose building more stringent guidelines and standards on the information that needs to be provided along with publications for projects that evolved in the HCA program.

The barcode, UMI, set format and BUStools.

We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific and robust workflows for single-cell RNA-seq analysis. http://BUStools.github.io/ and http://pachterlab.github.io/kallisto/singlecell.html. Supplementary data are available at Bioinformatics online.

Analysis of Single-Cell RNA-seq Data by Clustering Approaches

Background: The recently developed single-cell RNA sequencing (scRNA-seq) has attracted a great amount of attention due to its capability to interrogate expression of individual cells, which is superior to traditional bulk cell sequencing that can only measure mean gene expression of a population of cells. scRNA-seq has been successfully applied in finding new cell subtypes. New computational challenges exist in the analysis of scRNA-seq data. Objective: We provide an overview of the features of different similarity calculation and clustering methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We would also like to show that feature selection methods are important to improve clustering performance. Results: We first described similarity measurement methods, followed by reviewing some new clustering methods, as well as their algorithmic details. This analysis revealed several new questions, including how to automatically estimate the number of clustering categories, how to discover novel subpopulation, and how to search for new marker genes by using feature selection methods. Conclusion: Without prior knowledge about the number of cell types, clustering or semisupervised learning methods are important tools for exploratory analysis of scRNA-seq data.</P>

ScRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions.

Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce single-cell RNA-seq assembler, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq. Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and CLASS2. In particular, it showed a remarkable capability of recovering unknown 'novel' isoforms and highly computational efficiency compared to other tools. scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/. Supplementary data are available at Bioinformatics online.

Liquid biopsy using plasma proteomic profiling to reveal predictors of immunotherapy response.

130 Background: The response of metastatic melanoma to anti-PD1 is heterogeneous. We performed proteomic profiling of patient plasma samples to build a predictor of immunotherapy response and uncover biological insights underlying primary resistance. Methods: 58 metastatic melanoma patients receiving anti-PD1 (Pembrolizumab or Nivolumab) at MGH comprised the initial cohort, and 150 additional patients comprised a validation cohort. Plasma samples were collected (MGH IRB #11-181) at baseline and several on-treatment time-points. Samples were analyzed for 1102 proteins by a multiplex proximity extension assay (Olink Proteomics). A subset of patients had single-cell RNA-seq (Smart-Seq2 protocol) performed on tumor tissue. Group differences and treatment effects were evaluated using linear mixed models with maximum likelihood estimation for model parameters, and Benjamini and Hochberg multiple hypothesis correction. Results: 70 significantly differentially expressed (DE) proteins were identified across the treatment period, including markers of immune activation (PD1, CXCL9, CXCL10, CD25, IL-17a, among others). 38 significantly DE proteins were identified with on-treatment time points between anti-PD1 responders (R) and non-responders (NR), including several implicated in primary or acquired resistance (IL8, MIA, ERBB2, among others). Importantly, we demonstrate the relationship of these serum biomarkers to overall and progression-free survival, and employed statistical learning approaches to build classifiers of treatment response, leveraging early and late on-treatment time points. Analysis of single-cell RNA-seq data (Sade-Feldman et al, Cell, 2018) of tumor tissue from a subset of these patients revealed that gene expression of most proteins predictive of response were enriched among tumor myeloid cells, with the remainder of proteins being reflective of exhausted T cell states. Conclusions: Whole plasma proteomic profiling of anti-PD1 treated patients revealed DE proteins between R and NR that may enable a liquid biopsy to predict anti-PD1 response. These results unveil a putative role of myeloid cells within the tumor microenvironment in anti-PD1 response or primary resistance.

WebCEMiTool: Co-expression Modular Analysis Made Easy.

Co-expression analysis has been widely used to elucidate the functional architecture of genes under different biological processes. Such analysis, however, requires substantial knowledge about programming languages and/or bioinformatics skills. We present webCEMiTool,1 a unique online tool that performs comprehensive modular analyses in a fully automated manner. The webCEMiTool not only identifies co-expression gene modules but also performs several functional analyses on them. In addition, webCEMiTool integrates transcriptomic data with interactome information (i.e., protein-protein interactions) and identifies potential hubs on each network. The tool generates user-friendly html reports that allow users to search for specific genes in each module, as well as check if a module contains genes overrepresented in specific pathways or altered in a specific sample phenotype. We used webCEMiTool to perform a modular analysis of single-cell RNA-seq data of human cells infected with either Zika virus or dengue virus.

Locality Sensitive Imputation for Single Cell RNA-Seq Data.

One of the most notable challenges in single cell RNA-Seq data analysis is the so called drop-out effect, where only a fraction of the transcriptome of each cell is captured. The random nature of dropouts, however, makes it possible to consider imputation methods as means of correcting for dropouts. In this article, we study some existing single cell RNA sequencing (scRNA-Seq) imputation methods and propose a novel iterative imputation approach based on efficiently computing highly similar cells. We then present the results of a comprehensive assessment of existing and proposed methods on real scRNA-Seq data sets with varying per cell sequencing depth.

Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data.

Characterization of intratumoral heterogeneity is critical to cancer therapy, as the presence of phenotypically diverse cell populations commonly fuels relapse and resistance to treatment. Although genetic variation is a well-studied source of intratumoral heterogeneity, the functional impact of most genetic alterations remains unclear. Even less understood is the relative importance of other factors influencing heterogeneity, such as epigenetic state or tumor microenvironment. To investigate the relationship between genetic and transcriptional heterogeneity in a context of cancer progression, we devised a computational approach called HoneyBADGER to identify copy number variation and loss of heterozygosity in individual cells from single-cell RNA-sequencing data. By integrating allele and normalized expression information, HoneyBADGER is able to identify and infer the presence of subclone-specific alterations in individual cells and reconstruct the underlying subclonal architecture. By examining several tumor types, we show that HoneyBADGER is effective at identifying deletions, amplifications, and copy-neutral loss-of-heterozygosity events and is capable of robustly identifying subclonal focal alterations as small as 10 megabases. We further apply HoneyBADGER to analyze single cells from a progressive multiple myeloma patient to identify major genetic subclones that exhibit distinct transcriptional signatures relevant to cancer progression. Other prominent transcriptional subpopulations within these tumors did not line up with the genetic subclonal structure and were likely driven by alternative, nonclonal mechanisms. These results highlight the need for integrative analysis to understand the molecular and phenotypic heterogeneity in cancer.

Low Quality Cells Should Be Removed from Single-Cell RNA-Seq Data Analysis

Single-cell RNA-seq (scRNA-seq) technologies are increasingly popular for transcriptomic profiling at single-cell resolution. However, scRNA-seq data are noisy, which needs appropriate quality control analysis to remove those low-quality cells. A recent study analyzed the transcriptome data of 3589 single cells of human glioblastoma sequenced by Smart-seq2 technology, but we found that their scRNA-seq data contained a significant portion of low quality samples. Those low quality cells were with unqualified profiles in terms of sequencing depth, mapping ratio or number of detectable genes. Surprisingly, we observed that 74 (2.06%) samples were with < 500,000 reads and 31 samples had even <10,000 reads. Moreover, 126 (3.51%) samples had the mapping ratio lower than 60%, indicating that those scRNA-seq samples contained a lot of unmapped reads resulted from low quality and/or RNA degradation. We also found that 507 (14.13%) and 834 (23.24%) samples had the number of detectable genes low than 2000 for 0.1 and 1 FPKM as threshold, respectively. Using the criteria of < 500,000 reads or < 60% mapping ratio or < 2000 detectable genes defined by 1 FPKM, a total of 932 (25.97%) samples were grouped into low quality cells. Our result indicates that those low quality samples could influence subpopulation identification of cells, which may result in inaccurate results and misinterpretation of the data. Collectively, our findings highlight that appropriate quality control analysis should be conducted to remove the low quality cells from scRNA-seq studies to get convincible result and reliable conclusion.

Single-Cell Transcriptome Analysis Using SINCERA Pipeline.

Genome-scale single-cell biology has recently emerged as a powerful technology with important implications for both basic and medical research. There are urgent needs for the development of computational methods or analytic pipelines to facilitate large amounts of single-cell RNA-Seq data analysis. Here, we present a detailed protocol for SINCERA (SINgle CEll RNA-Seq profiling Analysis), a generally applicable analytic pipeline for processing single-cell data from a whole organ or sorted cells. The pipeline supports the analysis for the identification of major cell types, cell type-specific gene signatures, and driving forces of given cell types. In this chapter, we provide step-by-step instructions for the functions and features of SINCERA together with application examples to provide a practical guide for the research community. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html .

Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis

BackgroundMonoallelic expression of autosomal genes has been implicated in human psychiatric disorders. However, there is a paucity of allelic expression studies in human brain cells at the single cell and genome wide levels.ResultsIn this report, we reanalyzed a previously published single-cell RNA-seq dataset from several postmortem human brains and observed pervasive monoallelic expression in individual cells, largely in a random manner. Examining single nucleotide variants with a predicted functional disruption, we found that the “damaged” alleles were overall expressed in fewer brain cells than their counterparts, and at a lower level in cells where their expression was detected. We also identified many brain cell type-specific monoallelically expressed genes. Interestingly, many of these cell type-specific monoallelically expressed genes were enriched for functions important for those brain cell types. In addition, function analysis showed that genes displaying monoallelic expression and correlated expression across neuronal cells from different individual brains were implicated in the regulation of synaptic function.ConclusionsOur findings suggest that monoallelic gene expression is prevalent in human brain cells, which may play a role in generating cellular identity and neuronal diversity and thus increasing the complexity and diversity of brain cell functions.

Whole transcriptome profiling of taste bud cells

Analysis of single-cell RNA-Seq data can provide insights into the specific functions of individual cell types that compose complex tissues. Here, we examined gene expression in two distinct subpopulations of mouse taste cells: Tas1r3-expressing type II cells and physiologically identified type III cells. Our RNA-Seq libraries met high quality control standards and accurately captured differential expression of marker genes for type II (e.g. the Tas1r genes, Plcb2, Trpm5) and type III (e.g. Pkd2l1, Ncam, Snap25) taste cells. Bioinformatics analysis showed that genes regulating responses to stimuli were up-regulated in type II cells, while pathways related to neuronal function were up-regulated in type III cells. We also identified highly expressed genes and pathways associated with chemotaxis and axon guidance, providing new insights into the mechanisms underlying integration of new taste cells into the taste bud. We validated our results by immunohistochemically confirming expression of selected genes encoding synaptic (Cplx2 and Pclo) and semaphorin signalling pathway (Crmp2, PlexinB1, Fes and Sema4a) components. The approach described here could provide a comprehensive map of gene expression for all taste cell subpopulations and will be particularly relevant for cell types in taste buds and other tissues that can be identified only by physiological methods.

Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning.

We present single-cell interpretation via multikernel learning (SIMLR), an analytic framework and software which learns a similarity measure from single-cell RNA-seq data in order to perform dimension reduction, clustering and visualization. On seven published data sets, we benchmark SIMLR against state-of-the-art methods. We show that SIMLR is scalable and greatly enhances clustering performance while improving the visualization and interpretability of single-cell sequencing data.

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.

BackgroundThe ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)).ResultsThirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases.ConclusionThis novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available data sets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data

BackgroundA key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates.ResultsTo aid in the analysis and interpretation of single-cell RNA-Seq data, we have developed FastProject, a software tool which analyzes a gene expression matrix and produces a dynamic output report in which two-dimensional projections of the data can be explored. Annotated gene sets (referred to as gene ‘signatures’) are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms.ConclusionsHere we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1176-5) contains supplementary material, which is available to authorized users.

Comparison of methods to detect differentially expressed genes between single-cell populations

We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.