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A novel approach for biomarker selection and the integration of repeated measures experiments from two assays.

ABSTRACT: BACKGROUND: High throughput 'omics' experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects. RESULTS: We propose a two-step analysis combining a multilevel approach and a multivariate approach to reveal separately the effects of conditions within subjects from the biological variation between subjects. The approach is extended to two-factor designs and to the integration of two matched data sets. It allows internal variable selection to highlight genes able to discriminate the net condition effect within subjects. A simulation study was performed to demonstrate the good performance of the multilevel multivariate approach compared to a classical multivariate method. The multilevel multivariate approach outperformed the classical multivariate approach with respect to the classification error rate and the selection of relevant genes. The approach was applied to an HIV-vaccine trial evaluating the response with gene expression and cytokine secretion. The discriminant multilevel analysis selected a relevant subset of genes while the integrative multilevel analysis highlighted clusters of genes and cytokines that were highly correlated across the samples. CONCLUSIONS: Our combined multilevel multivariate approach may help in finding signatures of vaccine effect and allows for a better understanding of immunological mechanisms activated by the intervention. The integrative analysis revealed clusters of genes, that were associated with cytokine secretion. These clusters can be seen as gene signatures to predict future cytokine response. The approach is implemented in the R package mixOmics (http://cran.r-project.org/) with associated tutorials to perform the analysis(a).

SUBMITTER: Liquet B

PROVIDER: S-EPMC3627901 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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A novel approach for biomarker selection and the integration of repeated measures experiments from two assays.

Liquet Benoit B Lê Cao Kim-Anh KA Hocini Hakim H Thiébaut Rodolphe R

BMC bioinformatics 20121206

<h4>Background</h4>High throughput 'omics' experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects.<h4>Results</h4>We propose a two-step analysis combining a multilevel approach and a multivariate approach to reveal separately the effect ...[more]

PMID: 23216942

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Project description:Although numerous studies have examined sexual and substance use behaviors that put people at risk for sexually transmitted infections including HIV, most focus on an overall measure of aggregate risk or a few simple and particular subtypes of sexual acts assessed in separate analyses. In this article, we introduce a more sensitive approach to assess how the relative characteristics of sex acts may determine the level of risk in which an individual chooses to engage. Project AWARE, a randomized clinical trial conducted among 5012 patients in nine STD clinics across the U.S., is used to illustrate the approach. Our study was guided by two aims: (1) describe a new approach to examine the count of sexual acts using a disaggregated repeated measures design and (2) show how this new approach can be used to evaluate interactions among different categories of sexual risk behaviors and other predictors of interest (such as gender/sexual orientation). Profiles of different subtypes of sexual acts in the past 6 months were assessed. Potential interactions of the characteristics associated with each subtype which resulted in up to 48 distinct subtypes of sexual risk behaviors-sex with a primary/non-primary partner; partner's HIV status; vaginal/anal sex; condom use; and substance use before or during sex act-can be examined. Specifically, we chose condom use and primary and non-primary status of partner as an application in this paper to illustrate our method. There were significantly more condomless sex acts (M = 23, SE = 0.9) and sex acts with primary partners (M = 27.1, SE = 0.9) compared to sex acts with condoms (M = 10.9, SE = 0.4, IRR = 2.10, 95% CI 1.91-2.32, p < .001) and sex acts with non-primary partner (M = 10.9, SE = 0.5, IRR = 2.5, 95% CI 2.33-2.78, p < .001). In addition, there were significant differences for the count of sexual risk behaviors among women who have sex with men (WSM), men who have sex with women (MSW) and men who have sex with men (MSM) for sex acts with and without condom use, primary and non-primary partner, and their interaction (ps = .03, < .0001, and .001, respectively). This approach extends our understanding of how people make choices among sexual behaviors and may be useful in future research on disaggregated characteristics of sex acts.

Project description:BackgroundAs the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other correlated designs are becoming commonplace, and these studies offer immense potential for understanding how transcriptional changes within an individual over time differ depending on treatment or environmental conditions. While several methods have been proposed for dealing with repeated measures within RNA-Seq analyses, they are either restricted to handling only paired measurements, can only test for differences between two groups, and/or have issues with maintaining nominal false positive and false discovery rates. In this work, we propose a Bayesian hierarchical negative binomial generalized linear mixed model framework that can flexibly model RNA-Seq counts from studies with arbitrarily many repeated observations, can include covariates, and also maintains nominal false positive and false discovery rates in its posterior inference.ResultsIn simulation studies, we showed that our proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with maintenance of nominal false positive and false discovery rates compared the other available strategies, especially at the smaller sample sizes investigated. This behavior was then replicated in an application to real RNA-Seq data where MCMSeq was able to find previously reported genes associated with tuberculosis infection in a cohort with longitudinal measurements.ConclusionsFailing to account for repeated measurements when analyzing RNA-Seq experiments can result in significantly inflated false positive and false discovery rates. Of the methods we investigated, whether they model RNA-Seq counts directly or worked on transformed values, the Bayesian hierarchical model implemented in the mcmseq R package (available at https://github.com/stop-pre16/mcmseq ) best combined sensitivity and nominal error rate control.

Project description:BackgroundCYP26A1 has been reported in multiple cancers. However, the role of CYP26A1 in pancreatic cancer (PC) has not been explored.MethodThe public data used for this study was obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Cancer Cell Line Encyclopedia (CCLE) cell lines. CCK8, colony formation, and EdU assay were used to assess the proliferation ability of cancer cells. Transwell and wound healing assays were used to evaluate the invasion and migration ability of cancer cells. qRT-PCR and western blot assays were used to analyze the RNA and protein level of genes. Survival package was used for prognosis analysis. Gene Set Enrichment Analysis (GSEA) was used to identify biological pathway differences between two groups. ssGSEA analysis was used to quantify the immune microenvironment in PC tissue. GDSC and TIDE analyses were used for sensitivity analysis of chemotherapy and immunotherapy.ResultsOur results showed that CYP26A1 was overexpressed in PC tissue and cell lines. Meanwhile, metastatic PC cell lines tend to have a higher CYP26A1 level compared with the primary PC cell lines based on CCLE data. Moreover, CYP26A1 was associated with worse clinical features. Also, we found that CYP26A1 had a satisfactory efficiency in predicting overall survival, disease-specific survival, and progression-free interval of PC patients, independent of other clinical features. In vitro experiments indicated that CYP26A1 could significantly facilitate the proliferation, invasion, and migration ability of PC cells. GSEA showed that the pathways of angiogenesis, E2F target, MYC target, mTORC signaling, G2M checkpoint, and epithelial-mesenchymal transition were activated in high CYP26A1 patients. Immune infiltration analysis showed that CYP26A1 was positively correlated with macrophages, Th1 cells, and Treg cells, but negatively correlated with Th17 cells. TIDE analysis showed that non_responder patients had a higher CYP26A1 level compared with predicted responder patients of immunotherapy. Drug sensitivity analysis and assay showed that CYP26A1 could increase the chemotherapy sensitivity of gemcitabine.ConclusionsIn summary, CYP26A1 promotes PC progression and is a novel biomarker of PC, with potential for clinical application.

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A novel approach for biomarker selection and the integration of repeated measures experiments from two assays.

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A novel approach for biomarker selection and the integration of repeated measures experiments from two assays.

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