Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review

Liu, Xueyan; Li, Nan; Liu, Sheng; Wang, Jun; Zhang, Ning; Zheng, Xubin; Leung, Kwong‐Sak; Cheng, Lixin

doi:10.3389/fbioe.2019.00358

Cited by 95 publications

(69 citation statements)

References 56 publications

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“…The R library "affy" was employed for expression data preprocessing with the Robust Multichip Average (RMA) in the R 3.3.1 software. Following the correction of background effect, quantile normalization and log2transformation, the datasets were used for subsequent analyses [28].…”

Section: Data Sources and Data Preprocessingmentioning

confidence: 99%

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

et al. 2020

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Background: Left-sided heart failure (HF) is documented as a key prognostic factor in HF. However, the relative molecular mechanisms underlying left-sided HF is unknown. The purpose of this study is to unearth significant modules, pivotal genes and candidate regulatory components governing the progression of left-sided HF by bioinformatical analysis. Methods: A total of 319 samples in GSE57345 dataset were used for weighted gene correlation network analysis (WGCNA). ClusterProfiler package in R was used to conduct functional enrichment for genes uncovered from the modules of interest. Regulatory networks of genes were built using Cytoscape while Enrichr database was used for identification of transcription factors (TFs). The MCODE plugin was used for identifying hub genes in the modules of interest and their validation was performed based on GSE1869 dataset. Results: A total of six significant modules were identified. Notably, the blue module was confirmed as the most crucially associated with left-sided HF, ischemic heart disease (ISCH) and dilated cardiomyopathy (CMP). Functional enrichment conveyed that genes belonging to this module were mainly those driving the extracellular matrixassociated processes such as extracellular matrix structural constituent and collagen binding. A total of seven transcriptional factors, including Suppressor of Zeste 12 Protein Homolog (SUZ12) and nuclear factor erythroid 2 like 2 (NFE2L2), adrenergic receptor (AR), were identified as possible regulators of coexpression genes identified in the blue module. A total of three key genes (OGN, HTRA1 and MXRA5) were retained after validation of their prognostic value in left-sided HF. The results of functional enrichment confirmed that these key genes were primarily involved in response to transforming growth factor beta and extracellular matrix.

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Section: Data Sources and Data Preprocessingmentioning

confidence: 99%

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

et al. 2020

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“…Raw array data preprocessing was performed using the affy package in the R environment [25]. The raw gene expression matrixes were normalized by the RMA method [26][27][28]. We correct for batch effects of the datasets by ComBat [29].…”

Section: Materials and Methods 1 Gene Expression Datasets And Data Pmentioning

confidence: 99%

Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Cheng

Nan

Kang

et al. 2020

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Background: Sepsis is a fatal disease referring to the presence of a known or strongly suspected infection coupled with systemic and uncontrolled immune activation causing multiple organ failure. However, neither pathogenic long non-coding RNAs (lncRNAs) nor biological network analysis in sepsis draws enough attention to the society of sepsis studies. Methods: We performed an in-silico investigation of the gene coexpression pattern for the patients response to all-cause sepsis in consecutive intensive care unit (ICU) admissions. Sepsis coexpression gene modules were identified using WGCNA and enrichment analysis. lncRNAs were determined as sepsis biomarkers based on the interactions among lncRNAs and the identified modules. Results: Twenty-three sepsis modules, including both differentially expressed modules and prognostic modules, were identified from the whole blood RNA expression profilings of sepsis patients. Five lncRNAs, FENDRR, MALAT1, TUG1, CRNDE, and ANCR, were detected as sepsis regulators based on the interactions among lncRNAs and the identified coexpression modules. Furthermore, we found that CRNDE and MALAT1 may act as miRNA sponges of sepsis related miRNAs to regulate the expression of sepsis modules. Ultimately, FENDRR, MALAT1, TUG1, and CRNDE were reannotated using three independent lncRNA expression datasets and validated as differentially expressed lncRNAs. Conclusion: The procedure facilitates the identification of prognostic biomarkers and novel therapeutic strategies of sepsis. Our findings highlight the importance of transcriptome modularity and regulatory lncRNAs in the progress of sepsis.

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“…Raw array data preprocessing was performed using the affy package in the R environment [26]. The raw gene expression matrixes were normalized by the RMA method [27][28][29]. Illumina chip dataset GSE69528 was adopted as validation dataset.…”

Section: Gene Expression Datasets and Data Preprocessingmentioning

confidence: 99%

Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

et al. 2020

Self Cite

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Background: Sepsis is a fatal disease referring to the presence of a known or strongly suspected infection coupled with systemic and uncontrolled immune activation causing multiple organ failure. However, current knowledge of the role of lncRNAs in sepsis is still extremely limited. Methods: We performed an in silico investigation of the gene coexpression pattern for the patients response to all-cause sepsis in consecutive intensive care unit (ICU) admissions. Sepsis coexpression gene modules were identified using WGCNA and enrichment analysis. lncRNAs were determined as sepsis biomarkers based on the interactions among lncRNAs and the identified modules. Results: Twenty-three sepsis modules, including both differentially expressed modules and prognostic modules, were identified from the whole blood RNA expression profiling of sepsis patients. Five lncRNAs, FENDRR, MALAT1, TUG1, CRNDE, and ANCR, were detected as sepsis regulators based on the interactions among lncRNAs and the identified coexpression modules. Furthermore, we found that CRNDE and MALAT1 may act as miRNA sponges of sepsis related miRNAs to regulate the expression of sepsis modules. Ultimately, FENDRR, MALAT1, TUG1, and CRNDE were reannotated using three independent lncRNA expression datasets and validated as differentially expressed lncRNAs. Conclusion: The procedure facilitates the identification of prognostic biomarkers and novel therapeutic strategies of sepsis. Our findings highlight the importance of transcriptome modularity and regulatory lncRNAs in the progress of sepsis.

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Normalization Methods for the Analysis of Unbalanced Transcriptome Data: A Review

Cited by 95 publications

References 56 publications

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

Weighted correlation network bioinformatics uncovers a key molecular biosignature driving the left-sided heart failure

Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

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