Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms

Qiu, Chuan; Yu, Fangtang; Su, Kuan-Jui; Zhao, Qi; Zhang, Lan; Xu, Chao; Hu, Wei; Wang, Zun; Zhao, Lan‐Juan; Tian, Qing; Wang, Yuping; Deng, Hong‐Wen; Shen, Hui

doi:10.1016/j.isci.2020.100847

Cited by 41 publications

(37 citation statements)

References 113 publications

(106 reference statements)

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“…In the early-integration approach, also known as juxtaposition-based, the multi-omics datasets are first concatenated into one matrix. To deal with the highdimensionality of the joint dataset, these methods generally adopt matrix factorization (55,56,58,71), statistical (47,49,58,60,62,(72)(73)(74)(75)(76), and machine learning tools (58,76,77). Alternatively, data models relying on polyglot approaches can be used especially in (bio)informatics applications (78,79).…”

Section: Background and Related Workmentioning

confidence: 99%

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

et al. 2020

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Section: Background and Related Workmentioning

confidence: 99%

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

et al. 2020

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“…Combined of RNA-Seq, ChIP-Seq, and Hi-C or TCC data enables to explain that chromatin structural modifications and enhancer activities are components for the alterations of gene expression levels ( 11 , 12 , 98 ). RNA-Seq and BS related techniques, like WGBS and RRBS, can be used to find the inverse correlation between gene expression levels and methylation patterns in CpG and/or promoter regions ( 15 , 82 ).…”

Section: Integrative Analysis With Transcriptome and Other Sequencingmentioning

confidence: 99%

“…Methylome: DNA methylation has been expected to play a central role for epigenetic changes and reported to have inverse relation with gene expression levels ( Fig. 2C ) ( 15 , 82 , 104 , 105 ). There were tries to manifest the relation between the pattern of methylated genome regions and gene regulation, independent on the observed somatic mutations in HCC.…”

Section: Integrative Analysis With Transcriptome and Other Sequencingmentioning

confidence: 99%

“…Recently, integrative analysis of transcriptome and methylome was carried out to identify biomarkers in osteoporosis ( 82 ). Multi-omics data, including transcriptome, methylome, and metabolome, was integrated by sparse multiple discriminative canonical correlation analysis (SMDCCA), a multivariate integrating method that is used for searching optimal linear combination of features, and the integrated data was combined with genome ( 82 ). SMDCCA is a valid method of finding potential biomarkers.…”

Section: Integrative Analysis With Transcriptome and Other Sequencingmentioning

confidence: 99%

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From genome sequencing to the discovery of potential biomarkers in liver disease

Jung

et al. 2020

BMB Rep.

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Chronic liver disease progresses through several stages, fatty liver, steatohepatitis, cirrhosis, and eventually, it leads to hepatocellular carcinoma (HCC) over a long period of time. Since a large proportion of patients with HCC are accompanied by cirrhosis, it is considered to be an important factor in the diagnosis of liver cancer. This is because cirrhosis leads to an irreversible harmful effect, but the early stages of chronic liver disease could be reversed to a healthy state. Therefore, the discovery of biomarkers that could identify the early stages of chronic liver disease is important to prevent serious liver damage. Biomarker discovery at liver cancer and cirrhosis has enhanced the development of sequencing technology. Next generation sequencing (NGS) is one of the representative technical innovations in the biological field in the recent decades and it is the most important thing to design for research on what type of sequencing methods are suitable and how to handle the analysis steps for data integration. In this review, we comprehensively summarized NGS techniques for identifying genome, transcriptome, DNA methylome and 3D/4D chromatin structure, and introduced framework of processing data set and integrating multi-omics data for uncovering biomarkers.

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“…In the early-integration approach, also known as juxtaposition-based, the multi-omics datasets are first concatenated into one matrix. To deal with the high-dimensionality of the joint dataset, these methods generally adopt matrix factorization (68,53,55,52), statistical (46,69,70,59,57,44,71,72,73,55), and machine learning tools (74,73,55). Although the dimensionality reduction procedure is necessary and may improve the predictive performance, it can also cause the loss of key information (66).…”

Section: Background and Related Workmentioning

confidence: 99%

Integrative Network Fusion: a multi-omics approach in molecular profiling

Chierici

Bussola

Marcolini

et al. 2020

Preprint

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Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold crossvalidation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein abundances and copy number variants are used to predict estrogen receptor status (BRCA-ER, N=381) and breast invasive carcinoma subtypes (BRCA-subtypes, N=305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N=157; KIRC-OS, N=181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs 0.80; FS: 56 vs 1801) and BRCA-subtypes 1 Chierici et al. INF(0.84 vs 0.80; 302 vs 1801), improving KIRC-OS performance (0.38 vs 0.31; 111 vs 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes 1 .

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Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms

Cited by 41 publications

References 113 publications

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

From genome sequencing to the discovery of potential biomarkers in liver disease

Integrative Network Fusion: a multi-omics approach in molecular profiling

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