• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.655-664
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• 2022

On the motivation by an integrative study of multi-omics data, we are interested in estimating the structure of the sparse cross correlation matrix of two high-dimensional random vectors. We rewrite the problem as a multiple testing problem and propose a new method to estimate the sparse structure of the cross correlation matrix. To do so, we test the correlation coefficients simultaneously and threshold the correlation coefficients by controlling FRD at a predetermined level α. Further, we apply the proposed method and an alternative adaptive thresholding procedure by Cai and Liu (2016) to the integrative analysis of the protein expression data (X) and the mRNA expression data (Y) in TCGA breast cancer cohort. By varying the FDR level α, we show that the new procedure is consistently more efficient in estimating the sparse structure of cross correlation matrix than the alternative one.

• Journal of Preventive Medicine and Public Health
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• v.54 no.4
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• pp.259-264
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• 2021

Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.

• Genomics & Informatics
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• v.10 no.2
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• pp.69-73
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• 2012

The explosive development of genomics technologies including microarrays and next generation sequencing (NGS) has provided comprehensive maps of cancer genomes, including the expression of mRNAs and microRNAs, DNA copy numbers, sequence variations, and epigenetic changes. These genome-wide profiles of the genetic aberrations could reveal the candidates for diagnostic and/or prognostic biomarkers as well as mechanistic insights into tumor development and progression. Recent efforts to establish the huge cancer genome compendium and integrative omics analyses, so-called "integromics", have extended our understanding on the cancer genome, showing its daunting complexity and heterogeneity. However, the challenges of the structured integration, sharing, and interpretation of the big omics data still remain to be resolved. Here, we review several issues raised in cancer omics data analysis, including NGS, focusing particularly on the study design and analysis strategies. This might be helpful to understand the current trends and strategies of the rapidly evolving cancer genomics research.

• BMB Reports
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• v.52 no.1
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• pp.56-63
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• 2019

Aging is a complex and progressive process characterized by physiological and functional decline with time that increases susceptibility to diseases. Aged-related functional change is accompanied by a low-grade, unresolved chronic inflammation as a major underlying mechanism. In order to explain aging in the context of chronic inflammation, a new integrative concept on age-related chronic inflammation is necessary that encompasses much broader and wider characteristics of cells, tissues, organs, systems, and interactions between immune and non-immune cells, metabolic and non-metabolic organs. We have previously proposed a novel concept of senescent (seno)-inflammation and provided its frameworks. This review summarizes senoinflammation concept and additionally elaborates modulation of senoinflammation by calorie restriction (CR). Based on aging and CR studies and systems-biological analysis of Omics big data, we observed that senescence associated secretory phenotype (SASP) primarily composed of cytokines and chemokines was notably upregulated during aging whereas CR suppressed them. This result further strengthens the novel concept of senoinflammation in aging process. Collectively, such evidence of senoinflammation and modulatory role of CR provide insights into aging mechanism and potential interventions, thereby promoting healthy longevity.

• BMB Reports
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• v.47 no.1
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• pp.15-20
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• 2014

Intracellular lipid-binding proteins (LBPs) impact fatty acid homeostasis in various ways, including fatty acid transport into mitochondria. However, the physiological consequences caused by mutations in genes encoding LBPs remain largely uncharacterized. Here, we explore the metabolic consequences of lbp-5 gene deficiency in terms of energy homeostasis in Caenorhabditis elegans. In addition to increased fat storage, which has previously been reported, deletion of lbp-5 attenuated mitochondrial membrane potential and increased reactive oxygen species levels. Biochemical measurement coupled to proteomic analysis of the lbp-5(tm1618) mutant revealed highly increased rates of glycolysis in this mutant. These differential expression profile data support a novel metabolic adaptation of C. elegans, in which glycolysis is activated to compensate for the energy shortage due to the insufficient mitochondrial ${\beta}$-oxidation of fatty acids in lbp-5 mutant worms. This report marks the first demonstration of a unique metabolic adaptation that is a consequence of LBP-5 deficiency in C. elegans.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.157-176
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• 2024

Integrative analysis for better understanding of complex biological systems gains more attention. Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject. In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA). Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD). To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data. It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data. Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method. Additionally, CIA exhibits variations dependent on the weight matrices employed.

• Genomics & Informatics
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• v.14 no.1
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• pp.34-40
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• 2016

Due to advances in omics technologies, numerous genome-wide studies on human samples have been published, and most of the omics data with the associated clinical information are available in public repositories, such as Gene Expression Omnibus and ArrayExpress. While analyzing several public datasets, we observed that errors in gender information occur quite often in public datasets. When we analyzed the gender description and the methylation patterns of gender-specific probes (glucose-6-phosphate dehydrogenase [G6PD], ephrin-B1 [EFNB1], and testis specific protein, Y-linked 2 [TSPY2]) in 5,611 samples produced using Infinium 450K HumanMethylation arrays, we found that 19 samples from 7 datasets were erroneously described. We also analyzed 1,819 samples produced using the Affymetrix U133Plus2 array using several gender-specific genes (X (inactive)-specific transcript [XIST], eukaryotic translation initiation factor 1A, Y-linked [EIF1AY], and DEAD [Asp-Glu-Ala-Asp] box polypeptide 3, Y-linked [DDDX3Y]) and found that 40 samples from 3 datasets were erroneously described. We suggest that the users of public datasets should not expect that the data are error-free and, whenever possible, that they should check the consistency of the data.

• Molecules and Cells
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• v.44 no.3
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• pp.127-135
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• 2021

Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.

• Food Science and Industry
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• v.50 no.1
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• pp.16-25
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• 2017

There is no doubt that accumulation of big data using multi-omics technologies will be useful to solve human's long-standing problems such as development of personalized diet and medicine, overcoming diseases, and longevity. However, in the food industry, big data based on omics is scarcely accumulated. In particular, comprehensive analysis of molecular lipid metabolites directly associated with food quality, such as taste, flavor, and texture has been very limited. Moreover, most of food lipidomics studies are applied to analyze lipid components and discriminate authenticity and freshness of limited foods including vegetable and fish oil. However, if lipid big data through food lipidomics research of various foods and materials can be accumulated, lipidomics can be used in the optimization of food processing, production, delivery system, food safety, and storage as well as functional food.

• BMB Reports
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• v.52 no.2
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• pp.133-138
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• 2019

Upon viral infection, the 2', 5'-oligoadenylate synthetase (OAS)-ribonuclease L (RNaseL) system works to cleave viral RNA, thereby blocking viral replication. However, it is unclear whether OAS proteins have a role in regulating gene expression. Here, we show that OAS1 and OAS3 act as negative regulators of the expression of chemokines and interferon-responsive genes in human macrophages. Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein-9 nuclease (Cas9) technology was used to engineer human myeloid cell lines in which the OAS1 or OAS3 gene was deleted. Neither OAS1 nor OAS3 was exclusively responsible for the degradation of rRNA in macrophages stimulated with poly(I:C), a synthetic surrogate for viral double-stranded (ds)RNA. An mRNA sequencing analysis revealed that genes related to type I interferon signaling and chemokine activity were increased in $OAS1^{-/-}$ and $OAS3^{-/-}$ macrophages treated with intracellular poly(I:C). Indeed, retinoic-acid-inducible gene (RIG)-I- and interferon-induced helicase C domain-containing protein (IFIH1 or MDA5)-mediated induction of chemokines and interferon-stimulated genes was regulated by OAS3, but Toll-like receptor 3 (TLR3)- and TLR4-mediated induction of those genes was modulated by OAS1 in macrophages. However, stimulation of these cells with type I interferons had no effect on OAS1- or OAS3-mediated chemokine secretion. These data suggest that OAS1 and OAS3 negatively regulate the expression of chemokines and interferon-responsive genes in human macrophages.