Transcriptome profiling and differential expression analysis of the immune-related genes during the early phase of acute infection with Aeromonas hydrophila in the Chinese sucker (Myxocyprinus asiaticus)

Aquaculture ◽  
2021 ◽  
pp. 737258
Author(s):  
Fang Li ◽  
Xiaohong Liu ◽  
Hailong Ge ◽  
Jing Huang ◽  
Yaoguang Zhang ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Aeromonas Hydrophila ◽  
Early Phase ◽  
Acute Infection ◽  
Differential Expression Analysis ◽  
Transcriptome Profiling ◽  
Myxocyprinus Asiaticus ◽  
Chinese Sucker ◽  
Immune Related Genes

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

2020 ◽  
Author(s):  
Federico Marini ◽  
Jan Linke ◽  
Harald Binder
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Web Application ◽  
Differential Expression Analysis ◽  
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.

scholarly journals scRMD: imputation for single cell RNA-seq data via robust matrix decomposition

2020 ◽  
Vol 36 (10) ◽  
pp. 3156-3161 ◽  
Author(s):  
Chong Chen ◽  
Changjing Wu ◽  
Linjie Wu ◽  
Xiaochen Wang ◽  
Minghua Deng ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Matrix Decomposition ◽  
Transcriptome Profiling ◽  
R Package ◽  
Supplementary Information ◽  
Downstream Analysis

Abstract Motivation Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant increase of noises, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values can be beneficial in scRNA-seq data analysis. Results In this article, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method called scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis. Availability and implementation The R package scRMD is available at https://github.com/XiDsLab/scRMD. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals scRMD: Imputation for single cell RNA-seq data via robust matrix decomposition

2018 ◽  
Author(s):  
Chong Chen ◽  
Changjing Wu ◽  
Linjie Wu ◽  
Yishu Wang ◽  
Minghua Deng ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Matrix Decomposition ◽  
Transcriptome Profiling ◽  
Downstream Analysis ◽  
Whole Transcriptome ◽  
Biological And Medical Applications

AbstractMotivationSingle cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant noise increase, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values thus becomes an essential step in scRNA-seq data analysis.ResultsIn this paper, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering [email protected]

scholarly journals ideal: an R/Bioconductor package for interactive differential expression analysis

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Federico Marini ◽  
Jan Linke ◽  
Harald Binder
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Web Application ◽  
Differential Expression Analysis ◽  
Transcriptome Profiling ◽  
Data Interpretation ◽  
R Package ◽  
Rna Seq ◽  
Bioconductor Project ◽  
Analysis Workflow

Abstract Background RNA 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. Results We developed the 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. 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 workflow 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. also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility. Conclusion 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.

scholarly journals Potential of Immune-Related Genes as Biomarkers for Diagnosis and Subtype Classification of Preeclampsia

2020 ◽  
Vol 11 ◽  
Author(s):  
Ying Wang ◽  
Zhen Li ◽  
Guiyu Song ◽  
Jun Wang
Keyword(s):  
Differential Expression ◽  
Clinical Outcomes ◽  
Expression Analysis ◽  
Immune Cell ◽  
Differential Expression Analysis ◽  
Cell Infiltration ◽  
Support Vector ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Immune Related Genes

ObjectivePreeclampsia is the main cause of maternal mortality due to a lack of diagnostic biomarkers and effective prevention and treatment. The immune system plays an important role in the occurrence and development of preeclampsia. This research aimed to identify significant immune-related genes to predict preeclampsia and possible prevention and control methods.MethodsDifferential expression analysis between normotensive and PE pregnancies was performed to identify significantly changed immune-related genes. Generalized linear model (GLM), random forest (RF), and support vector machine (SVM) models were established separately to screen the most suitable biomarkers for the diagnosis of PE among these significantly changed immune-related genes. The consensus clustering method was used to divide the PE cases into several subgroups to explore the function of the significantly changed immune-related genes in PE.ResultsThirteen significantly changed immune-related genes were obtained by the differential expression analysis. RF was the best model and was used to select the four most important explanatory variables (CRH, PI3, CCL18, and CCL2) to diagnose PE. A nomogram model was constructed to predict PE based on these four variables. The decision curve analysis (DCA) and clinical impact curves revealed that PE patients could significantly benefit from this nomogram. Consensus clustering analysis of the 13 differentially expressed immune-related genes (DIRGs) was used to identify 3 subgroups of PE pregnancies with different clinical outcomes and immune cell infiltration.ConclusionOur study identified four immune-related genes to predict PE and three subgroups of PE with different clinical outcomes and immune cell infiltration. Future studies on the three subgroups may provide direction for individualized treatment of PE patients.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

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