Transcriptomics and methylomics of human monocytes
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA242316 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)). The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009. The current study is particularly focused on the relationships between transcriptomics and methylomics with age. Data includes transcriptomic and methylomic data from CD14+ samples, collected from 1,202 individuals ranging 44 - 83 years of age (Exam 1). Peripheral monocytes were isolated from blood (Exam 5) with anti-CD14 coated magnetic beads, and the Illumina HumanHT-12 v4 Expression BeadChip and the Illumina HumanMethylation450 BeadChip were used to provide genome-wide coverage of mRNA expression and DNA methylation, respectively. This data enabled us to identify CpG loci whose degree of methylation was associated with age (age-DMR),and identify age- and expression-associated methylation sites (age-eMS), whose degree of methylation was associated with age and cis-gene expression (+/- 1Mb), after adjusting for other covariates such as race, gender, study site, and sample contaimination with B-cells, T-cells, natural killer cells, and neutrophils. To estimate residual sample contamination for monocyte data analysis, separate enrichment scores for neutrophils, B cells, T cells, and natural killer cells were calculated and provided as sample characteristics. The participant race, gender, and study site were provided as 'raceGenderSite variable' which represents a combination of those factors. A detailed description of each sample characteristics is included in the 'README.txt'.
2014-11-24 | E-GEOD-56046 | biostudies-arrayexpress
Project description:The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)). The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009. The current study is particularly focused on the relationships between transcriptomics and methylomics with age. Data includes transcriptomic and methylomic data from CD14+ samples, collected from 1,202 individuals ranging 44 - 83 years of age (Exam 1). Peripheral monocytes were isolated from blood (Exam 5) with anti-CD14 coated magnetic beads, and the Illumina HumanHT-12 v4 Expression BeadChip and the Illumina HumanMethylation450 BeadChip were used to provide genome-wide coverage of mRNA expression and DNA methylation, respectively. This data enabled us to identify CpG loci whose degree of methylation was associated with age (age-DMR),and identify age- and expression-associated methylation sites (age-eMS), whose degree of methylation was associated with age and cis-gene expression (+/- 1Mb), after adjusting for other covariates such as race, gender, study site, and sample contaimination with B-cells, T-cells, natural killer cells, and neutrophils. To estimate residual sample contamination for monocyte data analysis, separate enrichment scores for neutrophils, B cells, T cells, and natural killer cells were calculated and provided as sample characteristics. The participant race, gender, and study site were provided as 'raceGenderSite variable' which represents a combination of those factors. A detailed description of each sample characteristics is included in the 'README.txt'.
2014-11-24 | E-GEOD-56045 | biostudies-arrayexpress
Project description:The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)). The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009.
2014-11-24 | GSE56045 | GEO
Project description:The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)). The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009.
2014-11-24 | GSE56046 | GEO
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
2019-07-18 | GSE134052 | GEO
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
2019-07-18 | GSE134056 | GEO
Project description:Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Methylomics]
| PRJNA553570 | ENA
Project description:The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes and T cells from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)). The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009.
2014-11-24 | GSE56581 | GEO
Project description:The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes and T cells from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)). The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009.
2014-11-24 | GSE56580 | GEO
Project description:The MESA Epigenomics and Transcriptomics Study has been launched to investigate potential gene expression regulatory methylation sites in humans by examining the association between CpG methylation and gene expression in purified human monocytes and T cells from a large study population (community-dwelling participants in the Multi-Ethnic Study of Atherosclerosis (MESA)). The MESA Epigenomics and Transcriptomics Study was funded by a National Heart, Lung and Blood Institute grant (R01HL101250) through the NIH Roadmap Epigenomics Program in 2009. Data includes transcriptomic and methylomic data from CD4+ samples, collected from 214 individuals. Peripheral T cells were isolated from blood (Exam 5) with anti-CD4 coated magnetic beads, and the Illumina HumanHT-12 v4 Expression BeadChip and the Illumina HumanMethylation450 BeadChip were used to provide genome-wide coverage of mRNA expression and DNA methylation, respectively. This data enabled us to identifyCpG loci whose degree of methylation was associated with age (age-DMR),and identify age- and expression-associated methylation sites (age-eMS), whose degree of methylation was associated with age and cis-gene expression (+/- 1Mb), after adjusting for other covariates such as race, gender, study site, and sample contaimination with B-cells, monocytes, natural killer cells, and neutrophils. To estimate residual sample contamination for monocyte data analysis, separate enrichment scores for neutrophils, B cells, T cells, and natural killer cells were calculated and provided as sample characteristics. The participant race, gender, and study site were provided as 'raceGenderSite variable' which represents a combination of those factors. A detailed description of each sample characteristics is included in the 'README.txt'.
2014-11-24 | E-GEOD-56581 | biostudies-arrayexpress