DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments

AbstractDifferential gene expression (DGE) is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we present an R package, ViDGER (Visualization of Differential Gene Expression Results using R), which contains nine functions that generate information-rich visualizations for the interpretation of DGE results from three widely-used tools, Cuffdiff, DESeq2, and edgeR.

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DEGseq: an R package for identifying differentially expressed genes from RNA-seq data

Bioinformatics ◽

10.1093/bioinformatics/btp612 ◽

2009 ◽

Vol 26 (1) ◽

pp. 136-138 ◽

Cited By ~ 1945

Author(s):

Likun Wang ◽

Zhixing Feng ◽

Xi Wang ◽

Xiaowo Wang ◽

Xuegong Zhang

Keyword(s):

Differentially Expressed Genes ◽

R Package ◽

Differentially Expressed ◽

Rna Seq

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Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

10.1101/2020.08.24.265298 ◽

2020 ◽

Author(s):

Dustin J. Sokolowski ◽

Mariela Faykoo-Martinez ◽

Lauren Erdman ◽

Huayun Hou ◽

Cadia Chan ◽

...

Keyword(s):

Single Cell ◽

Differential Expression ◽

Differentially Expressed Genes ◽

Cell Types ◽

R Package ◽

Differentially Expressed ◽

Rna Seq ◽

Kidney Regeneration ◽

Cell Type ◽

User Friendly

AbstractRNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.HighlightsscMappR integrates scRNA-seq and bulk RNA-seq to re-calibrate bulk differentially expressed genes (DEGs).scMappR correctly identified immune-cell expressed DEGs from a bulk RNA-seq analysis of mouse kidney regeneration.scMappR is deployed as a user-friendly R package available at CRAN.

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Identification of differentially expressed genes and the role of PDK4 in CD14+ monocytes of coronary artery disease

Bioscience Reports ◽

10.1042/bsr20204124 ◽

2021 ◽

Author(s):

Pei Du ◽

Ren Guo ◽

Keqin Gao ◽

Shuang Yang ◽

Baige Yao ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Signaling Pathway ◽

Differentially Expressed Genes ◽

R Package ◽

Differentially Expressed ◽

Receptor Interaction ◽

Cell Periphery ◽

Rna Seq ◽

Artery Disease

Background. Coronary artery disease (CAD) is a chronic inflammatory disease caused by development of atherosclerosis, which is the leading cause of mortality and disability. Our study aimed to identify the differentially expressed genes (DEGs) in CD14+ monocytes from CAD patients compared with those from non-CAD controls, which might pave the way to diagnosis and treatment for CAD. Methods. The RNA-seq was performed by BGISEQ-500, followed by analyzing with R package to screening differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by R package. In addition, we validated the results of RNA-seq using RT-qPCR. Furthermore, we explored the function of selected ten genes in LDL-treated CD14+ monocytes by RT-qPCR. Results. a total of 2897 DEGs were identified, including 753 up- and 2144 down-regulated genes in CD14+ monocytes from CAD patients. These DEGs were mainly enriched in plasma membrane and cell periphery of cell component, immune system process of biological process, NF-kappa B signaling pathway, cell adhesion molecules signaling pathway and cytokine-cytokine receptor interaction signaling pathway. In LDL-treated CD14+ monocytes, the mRNA expression of PDK4 was significantly up-regulated. Conclusion. In this study, we suggested that PDK4 might play a role in progression of CAD. The study will provide some pieces of evidence to investigate the role and mechanism of key genes in the pathogenesis of CAD.

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bcbioRNASeq: R package for bcbio RNA-seq analysis

F1000Research ◽

10.12688/f1000research.12093.1 ◽

2017 ◽

Vol 6 ◽

pp. 1976 ◽

Cited By ~ 3

Author(s):

Michael J. Steinbaugh ◽

Lorena Pantano ◽

Rory D. Kirchner ◽

Victor Barrera ◽

Brad A. Chapman ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

R Package ◽

Functional Enrichment ◽

Differentially Expressed ◽

Control Analysis ◽

Rna Seq ◽

Sequencing Data ◽

Post Process

RNA-seq analysis involves multiple steps from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates and wrapper functions to post-process bcbio output data. bcbioRNASeq automates the generation of high-level RNA-seq reports, including identification of differentially expressed genes, functional enrichment analysis and quality control analysis.

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bcbioRNASeq: R package for bcbio RNA-seq analysis

F1000Research ◽

10.12688/f1000research.12093.2 ◽

2018 ◽

Vol 6 ◽

pp. 1976 ◽

Cited By ~ 8

Author(s):

Michael J. Steinbaugh ◽

Lorena Pantano ◽

Rory D. Kirchner ◽

Victor Barrera ◽

Brad A. Chapman ◽

...

Keyword(s):

Differentially Expressed Genes ◽

R Package ◽

Functional Enrichment ◽

Differentially Expressed ◽

Bioconductor Package ◽

Rna Seq ◽

Sequencing Data ◽

Output Data ◽

Post Process ◽

High Level

RNA-seq analysis involves multiple steps, from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates, objects and wrapper functions to post-process bcbio RNA sequencing output data. bcbioRNASeq helps automate the generation of high-level RNA-seq reports, facilitating the quality control analyses, identification of differentially expressed genes and functional enrichment analyses.

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edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test

10.1101/008409 ◽

2014 ◽

Author(s):

Emmanuel Dimont ◽

Jiantao Shi ◽

Rory Kirchner ◽

Winston Hide

Keyword(s):

Differentially Expressed Genes ◽

New Technologies ◽

Dynamic Range ◽

R Package ◽

Differentially Expressed ◽

Rna Seq ◽

Exact Test ◽

Digital Expression ◽

Sequencing Platforms ◽

Generation Sequencing

Summary: Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far few, exploit the expanded dynamic range afforded by the new technologies. We present edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as 2 replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes. Availability: The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/edgeRun) Contact: [email protected]

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Comparative Analysis of the Intermolt and Postmolt Hepatopancreas Transcriptomes Provides Insight into the Mechanisms of Procambarus clarkii Molting Process

Life ◽

10.3390/life11060480 ◽

2021 ◽

Vol 11 (6) ◽

pp. 480

Author(s):

Shengyan Su ◽

Brian Pelekelo Munganga ◽

Can Tian ◽

JianLin Li ◽

Fan Yu ◽

...

Keyword(s):

Immune Response ◽

Differentially Expressed Genes ◽

Hormonal Regulation ◽

De Novo ◽

Procambarus Clarkii ◽

Expression Profiles ◽

R Package ◽

Differentially Expressed ◽

Rna Seq ◽

Molting Process

In the present study, we used RNA-Seq to investigate the expression changes in the transcriptomes of two molting stages (postmolt (M) and intermolt (NM)) of the red swamp crayfish and identified differentially expressed genes. The transcriptomes of the two molting stages were de novo assembled into 139,100 unigenes with a mean length of 675.59 bp. The results were searched against the NCBI, NR, KEGG, Swissprot, and KOG databases, to annotate gene descriptions, associate them with gene ontology terms, and assign them to pathways. Furthermore, using the DESeq R package, differentially expressed genes were evaluated. The analysis revealed that 2347 genes were significantly (p > 0.05) differentially expressed in the two molting stages. Several genes and other factors involved in several molecular events critical for the molting process, such as energy requirements, hormonal regulation, immune response, and exoskeleton formation were identified and evaluated by correlation and KEGG analysis. The expression profiles of transcripts detected via RNA-Seq were validated by real-time PCR assay of eight genes. The information presented here provides a transient view of the hepatopancreas transcripts available in the postmolt and intermolt stage of crayfish, hormonal regulation, immune response, and skeletal-related activities during the postmolt stage and the intermolt stage.

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scCODE: an R package for personalized differentially expressed gene detection on single-cell RNA-sequencing data

10.1101/2021.11.18.469072 ◽

2021 ◽

Author(s):

jiawei Zou ◽

miaochen Wang ◽

zhen Zhang ◽

zheqi Liu ◽

xiaobin Zhang ◽

...

Keyword(s):

Single Cell ◽

Differentially Expressed Gene ◽

R Package ◽

Differentially Expressed ◽

Rna Seq ◽

Sequencing Data ◽

Gene Detection ◽

Gene Filtering ◽

Consensus Optimization ◽

Detection Strategies

Differential expression (DE) gene detection in single-cell RNA-seq (scRNA-seq) data is a key step to understand the biological question investigated. We find that DE methods together with gene filtering have profound impact on DE gene identification, and different datasets will benefit from personalized DE gene detection strategies. Existing tools don't take gene filtering into consideration, and couldn't evaluate DE performance on real datasets without prior knowledge of true results. Based on two new metrics, we propose scCODE (single cell Consensus Optimization of Differentially Expressed gene detection), an R package to automatically optimize DE gene detection for each experimental scRNA-seq dataset.

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ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

Bioinformatics ◽

10.1093/bioinformatics/btab179 ◽

2021 ◽

Author(s):

Irzam Sarfraz ◽

Muhammad Asif ◽

Joshua D Campbell

Keyword(s):

Single Cell ◽

R Package ◽

Poor Quality ◽

Data Matrix ◽

Supplementary Information ◽

Data Provenance ◽

Rna Seq ◽

Efficient Management ◽

The Matrix ◽

The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

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