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1.
Methods Mol Biol ; 2117: 109-131, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31960375

RESUMO

Identifying epigenetic field defects, notably early DNA methylation alterations, is important for early cancer detection. Research has suggested these early methylation alterations are infrequent across samples and identifiable as outlier samples. Here we developed a weighted epigenetic distance-based method characterizing (dis)similarity in methylation measures at multiple CpGs in a gene or a genetic region between pairwise samples, with weights to up-weight signal CpGs and down-weight noise CpGs. Using distance-based approaches, weak signals that might be filtered out in a CpG site-level analysis could be accumulated and therefore boost the overall study power. In constructing epigenetic distances, we considered both differential methylation (DM) and differential variability (DV) signals. We demonstrated the superior performance of the proposed weighted epigenetic distance-based method over nonweighted versions and site-level EWAS (epigenome-wide association studies) methods in simulation studies. Application to breast cancer methylation data from Gene Expression Omnibus (GEO) comparing normal-adjacent tissue to tumor of breast cancer patients and normal tissue of independent age-matched cancer-free women identified novel epigenetic field defects that were missed by EWAS methods, when majority were previously reported to be associated with breast cancer and were confirmed the progression to breast cancer. We further replicated some of the identified epigenetic field defects.

2.
Stat Appl Genet Mol Biol ; 18(5)2019 09 17.
Artigo em Inglês | MEDLINE | ID: mdl-31525158

RESUMO

We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.

3.
Bioinformatics ; 35(19): 3718-3726, 2019 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-30863842

RESUMO

MOTIVATION: Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data. RESULTS: In this article, we proposed an association-signal-annotation boosted similarity network fusion (ab-SNF) method, adding feature-level association signal annotations as weights aiming to up-weight signal features and down-weight noise features when constructing subject similarity networks to boost the performance in disease subtyping. In various simulation studies, the proposed ab-SNF outperforms the original SNF approach without weights. Most importantly, the improvement in the subtyping performance due to association-signal-annotation weights is amplified in the integration process. Applications to somatic mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Genome Atlas project suggest that the proposed ab-SNF method consistently identifies new subtypes in each cancer that more accurately predict patient survival and are more biologically meaningful. AVAILABILITY AND IMPLEMENTATION: The R package abSNF is freely available for downloading from https://github.com/pfruan/abSNF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

4.
Nucleic Acids Res ; 47(1): e6, 2019 01 10.
Artigo em Inglês | MEDLINE | ID: mdl-30304472

RESUMO

Identifying epigenetic field defects, notably early DNA methylation alterations, is important for early cancer detection. Research has suggested these early methylation alterations are infrequent across samples and identifiable as outlier samples. Here we developed a weighted epigenetic distance-based method characterizing (dis)similarity in methylation measures at multiple CpGs in a gene or a genetic region between pairwise samples, with weights to up-weight signal CpGs and down-weight noise CpGs. Using distance-based approaches, weak signals that might be filtered out in a CpG site-level analysis could be accumulated and therefore boost the overall study power. In constructing epigenetic distances, we considered both differential methylation (DM) and differential variability (DV) signals. We demonstrated the superior performance of the proposed weighted epigenetic distance-based method over non-weighted versions and site-level EWAS (epigenome-wide association studies) methods in simulation studies. Application to breast cancer methylation data from Gene Expression Omnibus (GEO) comparing normal-adjacent tissue to tumor of breast cancer patients and normal tissue of independent age-matched cancer-free women identified novel epigenetic field defects that were missed by EWAS methods, when majority were previously reported to be associated with breast cancer and were confirmed the progression to breast cancer. We further replicated some of the identified epigenetic field defects.


Assuntos
Neoplasias da Mama/genética , Metilação de DNA/genética , Epigenômica/métodos , Modelos Teóricos , Neoplasias da Mama/patologia , Ilhas de CpG/genética , Progressão da Doença , Detecção Precoce de Câncer/métodos , Feminino , Regulação Neoplásica da Expressão Gênica/genética , Estudo de Associação Genômica Ampla , Humanos
5.
Nucleic Acids Res ; 44(16): e134, 2016 09 19.
Artigo em Inglês | MEDLINE | ID: mdl-27302130

RESUMO

DNA methylation plays an important role in many biological processes. Existing epigenome-wide association studies (EWAS) have successfully identified aberrantly methylated genes in many diseases and disorders with most studies focusing on analysing methylation sites one at a time. Incorporating prior biological information such as biological networks has been proven to be powerful in identifying disease-associated genes in both gene expression studies and genome-wide association studies (GWAS) but has been under studied in EWAS. Although recent studies have noticed that there are differences in methylation variation in different groups, only a few existing methods consider variance signals in DNA methylation studies. Here, we present a network-assisted algorithm, NEpiC, that combines both mean and variance signals in searching for differentially methylated sub-networks using the protein-protein interaction (PPI) network. In simulation studies, we demonstrate the power gain from using both the prior biological information and variance signals compared to using either of the two or neither information. Applications to several DNA methylation datasets from the Cancer Genome Atlas (TCGA) project and DNA methylation data on hepatocellular carcinoma (HCC) from the Columbia University Medical Center (CUMC) suggest that the proposed NEpiC algorithm identifies more cancer-related genes and generates better replication results.


Assuntos
Algoritmos , Epigênese Genética , Neoplasias da Mama/genética , Simulação por Computador , Metilação de DNA/genética , Epigenômica , Feminino , Estudos de Associação Genética , Estudo de Associação Genômica Ampla , Humanos , Invasividade Neoplásica
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