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JCD-DEA: a joint covariate detection tool for differential expression analysis on tumor expression profiles.

ABSTRACT: BACKGROUND:Differential expression analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is important how to select features which best discriminate between different groups of patients. Despite the emergence of multivariate analysis approaches, prevailing feature selection methods primarily focus on multiple hypothesis testing on individual variables, and then combine them for an explanatory result. Besides, these methods, which are commonly based on hypothesis testing, view classification as a posterior validation of the selected variables. RESULTS:Based on previously provided A5 feature selection strategy, we develop a joint covariate detection tool for differential expression analysis on tumor expression profiles. This software combines hypothesis testing with testing according to classification results. A model selection approach based on Gaussian mixture model is introduced in for automatic selection of features. Besides, a projection heatmap is proposed for the first time. CONCLUSIONS:Joint covariate detection strengthens the viewpoint for selecting variables which are not only individually but also jointly significant. Experiments on simulation and realistic data show the effectiveness of the developed software, which enhances the reliability of joint covariate detection for differential expression analysis on tumor expression profiles. The software is available at http://bio-nefu.com/resource/jcd-dea .

SUBMITTER: Li Y

PROVIDER: S-EPMC6599234 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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JCD-DEA: a joint covariate detection tool for differential expression analysis on tumor expression profiles.

Li Yi Y Liu Yanan Y Wu Yiming Y Zhao Xudong X

BMC bioinformatics 20190628 1

<h4>Background</h4>Differential expression analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is important how to select features which best discriminate between different groups of patients. Despite the emergence of multivariate analysis approaches, prevailing feature selection methods primarily focus on multiple hypothesis testing on individual variables, and then combine them for an explanatory result. Besides, these methods ...[more]

PMID: 31253075

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Project description:BackgroundSequencing data has become a standard measure of diverse cellular activities. For example, gene expression is accurately measured by RNA sequencing (RNA-Seq) libraries, protein-DNA interactions are captured by chromatin immunoprecipitation sequencing (ChIP-Seq), protein-RNA interactions by crosslinking immunoprecipitation sequencing (CLIP-Seq) or RNA immunoprecipitation (RIP-Seq) sequencing, DNA accessibility by assay for transposase-accessible chromatin (ATAC-Seq), DNase or MNase sequencing libraries. The processing of these sequencing techniques involves library-specific approaches. However, in all cases, once the sequencing libraries are processed, the result is a count table specifying the estimated number of reads originating from each genomic locus. Differential analysis to determine which loci have different cellular activity under different conditions starts with the count table and iterates through a cycle of data assessment, preparation and analysis. Such complex analysis often relies on multiple programs and is therefore a challenge for those without programming skills.ResultsWe developed DEBrowser as an R bioconductor project to interactively visualize every step of the differential analysis, without programming. The application provides a rich and interactive web based graphical user interface built on R's shiny infrastructure. DEBrowser allows users to visualize data with various types of graphs that can be explored further by selecting and re-plotting any desired subset of data. Using the visualization approaches provided, users can determine and correct technical variations such as batch effects and sequencing depth that affect differential analysis. We show DEBrowser's ease of use by reproducing the analysis of two previously published data sets.ConclusionsDEBrowser is a flexible, intuitive, web-based analysis platform that enables an iterative and interactive analysis of count data without any requirement of programming knowledge.

Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, irreversible lung disorder of unknown cause. This disease is characterized by profibrotic activation of resident pulmonary fibroblasts resulting in aberrant deposition of extracellular matrix (ECM) proteins. However, although much is known about the pathophysiology of IPF, the cellular and molecular processes that occur and allow aberrant fibroblast activation remain an unmet need. To explore the differentially expressed proteins (DEPs) associated with aberrant activation of these fibroblasts, we used the IPF lung fibroblast cell lines LL97A (IPF-1) and LL29 (IPF-2), compared to the normal lung fibroblast cell line CCD19Lu (NL-1). Protein samples were quantified and identified using a label-free quantitative proteomic analysis approach by liquid chromatography-tandem mass spectrometry (LC-MS/MS). DEPs were identified after pairwise comparison, including all experimental groups. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) network construction were used to interpret the proteomic data. Eighty proteins expressed exclusively in the IPF-1 and IPF-2 clusters were identified. In addition, 19 proteins were identified up-regulated in IPF-1 and 10 in IPF-2; 10 proteins were down-regulated in IPF-1 and 2 in IPF-2 when compared to the NL-1 proteome. Using the search tool for retrieval of interacting genes/proteins (STRING) software, a PPI network was constructed between the DEPs and the 80 proteins expressed exclusively in the IPF-2 and IPF-1 clusters, containing 115 nodes and 136 edges. The 10 hub proteins present in the IPP network were identified using the CytoHubba plugin of the Cytoscape software. GO and KEGG pathway analyses showed that the hub proteins were mainly related to cell adhesion, integrin binding, and hematopoietic cell lineage. Our results provide relevant information on DEPs present in IPF lung fibroblast cell lines when compared to the normal lung fibroblast cell line that could play a key role during IPF pathogenesis.

Dataset Information

JCD-DEA: a joint covariate detection tool for differential expression analysis on tumor expression profiles.

Publications

JCD-DEA: a joint covariate detection tool for differential expression analysis on tumor expression profiles.

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