Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Transcriptomics]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA553574 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9661980.fastq.gz | Fastqsanger.gz | |||
| SRR9661990.fastq.gz | Fastqsanger.gz | |||
| SRR9662000.fastq.gz | Fastqsanger.gz | |||
| SRR9662010.fastq.gz | Fastqsanger.gz | |||
| SRR9661981.fastq.gz | Fastqsanger.gz |
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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:Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Methylomics]
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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:Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. 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). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. 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.
| S-EPMC6737999 | biostudies-literature
Project description:A discovery study using RNA-HighSeq from 37 endometrial biopsies was performed to build classifiers that differentiate between control patients and adenomyosis, ovarian endometriosis and deep infiltrating endometriosis. Total RNA was extracted from endometrial biopsies. We found an AUC of 0.97 where the classifier differentiated almost perfectly all three diseases together from the control group when including 11 phenotypic variables of the patients in the classifier.
2024-01-31 | GSE193928 | GEO
Project description:Transcriptomics and methylomics of human monocytes
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Project description:Endometriosis, an estrogen-dependent, progesterone-resistant, inflammatory disorder affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Since the uterine lining (endometrium) in women with endometriosis has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n=148 archived endometrial samples from women with endometriosis or without endometriosis (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with endometriosis or no endometriosis involved two binary decisions each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no endometriosis from endometriosis, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified endometriosis with 90-100% accuracy, were cycle phase-specific or independent, and utilized relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism and growth factor signaling in endometrium of women with endometriosis. Similar findings were observed with other disorders versus controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic endometriosis with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate-genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of endometriosis and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue. We analyzed endometrial samples from n=148 women without or with endometriosis and/or other uterine/pelvic pathologies, using whole genome microarrays.
2014-10-01 | E-GEOD-51981 | 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-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