BingleSeq: A user-friendly R package for Bulk and Single-cell RNA-Seq Data Analysis

Gene Annotation ◽

Transcriptome Profiling ◽

R Package ◽

Rna Seq ◽

The Individual ◽

User Friendly

AbstractRNA sequencing is a high-throughput sequencing technique considered as an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is Differential Expression analysis and it is used to determine genetic loci with distinct expression across different conditions. On the other hand, an emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both these types of analyses include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that both require programming expertise.BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface and incorporating three state-of-the-art software packages for each type of the aforementioned analyses, alongside additional features such as key visualisation techniques, functional gene annotation analysis and rank-based consensus for differential gene analysis results, among others. As a result, BingleSeq puts the best and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programming experience.

BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis

PeerJ ◽

10.7717/peerj.10469 ◽

2020 ◽

Vol 8 ◽

pp. e10469

Author(s):

Daniel Dimitrov ◽

Quan Gu

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Transcriptome Profiling ◽

R Package ◽

Rna Seq ◽

Single Cell Rna Sequencing ◽

The Individual ◽

Visualization Techniques ◽

User Friendly

Background RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both of these approaches include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that they require programing expertise. Although some effort has been directed toward the development of user-friendly RNA-Seq analysis analysis tools, few have the flexibility to explore both Bulk and single-cell RNA sequencing. Implementation BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface which incorporates three state-of-the-art software packages for each type of the aforementioned analyses. Furthermore, BingleSeq includes additional features such as visualization techniques, extensive functional annotation analysis and rank-based consensus for differential gene analysis results. As a result, BingleSeq puts some of the best reviewed and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programing experience. Availability BingleSeq is as an easy-to-install R package available on GitHub at https://github.com/dbdimitrov/BingleSeq/.

A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Genes ◽

10.3390/genes12121947 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1947

Author(s):

Samarendra Das ◽

Anil Rai ◽

Michael L. Merchant ◽

Matthew C. Cave ◽

Shesh N. Rai

Keyword(s):

Single Cell ◽

Differential Expression ◽

Rna Sequencing ◽

Performance Metrics ◽

Individual Performance ◽

Rna Seq ◽

Gene Expressions ◽

Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling

Nature Communications ◽

10.1038/s41467-019-12917-9 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 16

Author(s):

Qingnan Liang ◽

Rachayata Dharmat ◽

Leah Owen ◽

Akbar Shakoor ◽

Yumei Li ◽

...

Keyword(s):

Single Cell ◽

Transcriptome Profiling ◽

Cell Types ◽

Retinal Cell ◽

Peripheral Retina ◽

Marker Genes ◽

Rna Seq ◽

Cell Type ◽

Retinal Tissue ◽

The Individual

AbstractSingle-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an improved power for prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrate that obtaining single cell transcriptomes from human frozen tissues can provide insight missed by either human bulk RNA-seq or animal models.

Impact of human gene annotations on RNA-seq differential expression analysis

10.21203/rs.3.rs-301856/v1 ◽

2021 ◽

Author(s):

Yu Hamaguchi ◽

Chao Zeng ◽

Michiaki Hamada

Keyword(s):

Differential Expression ◽

High Throughput ◽

Human Gene ◽

Gene Annotation ◽

Rna Seq ◽

Gene Annotations ◽

Sequencing Technologies ◽

The Impact

Abstract Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data

BMC Bioinformatics ◽

10.1186/s12859-021-04461-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Federico Marini ◽

Annekathrin Ludt ◽

Jan Linke ◽

Konstantin Strauch

Keyword(s):

Differential Expression ◽

Transcriptome Profiling ◽

R Package ◽

Functional Enrichment ◽

Rna Seq ◽

Essential Information ◽

Wide Range ◽

Functional Patterns ◽

Interactive Visualizations

Abstract Background The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats—normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. Results We developed the software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. Conclusion is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both bird’s-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

Bioinformatics ◽

10.1093/bioinformatics/btz435 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4827-4829 ◽

Cited By ~ 6

Author(s):

Xiao-Fei Zhang ◽

Le Ou-Yang ◽

Shuo Yang ◽

Xing-Ming Zhao ◽

Xiaohua Hu ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Ensemble Learning ◽

R Package ◽

Supplementary Information ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

The Individual ◽

Downstream Analysis ◽

Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment

Bioinformatics ◽

10.1093/bioinformatics/btaa097 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3115-3123 ◽

Cited By ~ 3

Author(s):

Teng Fei ◽

Tianwei Yu

Keyword(s):

Single Cell ◽

Distance Matrix ◽

Real Data ◽

R Package ◽

Batch Effect ◽

Supplementary Information ◽

Rna Seq ◽

Sequencing Data ◽

Gene Differential Expression

Abstract Motivation Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data. Results We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. Availability and implementation The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. Supplementary information Supplementary data are available at Bioinformatics online.

intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data

BMC Genomics ◽

10.1186/s12864-020-07205-6 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Yingying Cao ◽

Simo Kitanovski ◽

Daniel Hoffmann

Keyword(s):

Gene Expression ◽

Expression Analysis ◽

Cell Types ◽

R Package ◽

Integrative Approach ◽

Integrated Analysis ◽

Data Sets ◽

Rna Seq

Abstract Background RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy. Results Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization. Conclusion intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset.

scCancer: a package for automated processing of single cell RNA-seq data in cancer

10.1101/800490 ◽

2019 ◽

Author(s):

Wenbo Guo ◽

Dongfang Wang ◽

Shicheng Wang ◽

Yiran Shan ◽

Jin Gu

Keyword(s):

Single Cell ◽

Learning Algorithm ◽

R Package ◽

Rna Seq ◽

Cell Level ◽

Quality Control Metrics ◽

Automated Processing ◽

Cellular Phenotypes ◽

User Friendly ◽

Processing Steps

AbstractSummaryMolecular heterogeneities bring great challenges for cancer diagnosis and treatment. Recent advance in single cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer transcriptomic heterogeneities at single cell level. Here, we develop an R package named scCancer which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness) and gene signatures. Finally, a user-friendly graphic report was generated for all the analyses.Availabilityhttp://lifeome.net/software/sccancer/[email protected]

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

10.1101/2020.01.10.901652 ◽

2020 ◽

Cited By ~ 4

Author(s):

Federico Marini ◽

Jan Linke ◽

Harald Binder

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Web Application ◽

Transcriptome Profiling ◽

Data Interpretation ◽

R Package ◽

Rna Seq ◽

Fully Integrated ◽

Bioconductor Project

AbstractBackgroundRNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.ResultsWe developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis work-flow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.Conclusionideal is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.