150 research outputs found
Ethics Reporting in Biospecimen and Genetic Research: Current Practice and Suggestions for Changes
<div><p>Modern approaches for research with human biospecimens employ a variety of substantially different types of ethics approval and informed consent. In most cases, standard ethics reporting such as “consent and approval was obtained” is no longer meaningful. A structured analysis of 120 biospecimen studies recently published in top journals revealed that more than 85% reported on consent and approval, but in more than 90% of cases, this reporting was insufficient and thus potentially misleading. Editorial policies, reporting guidelines, and material transfer agreements should include recommendations for meaningful ethics reporting in biospecimen research. Meaningful ethics reporting is possible without higher word counts and could support public trust as well as networked research.</p></div
Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data
Interpreting the complex interplay of metabolites in
heterogeneous
biosamples still poses a challenging task. In this study, we propose
independent component analysis (ICA) as a multivariate analysis tool
for the interpretation of large-scale metabolomics data. In particular,
we employ a Bayesian ICA method based on a mean-field approach, which
allows us to statistically infer the number of independent components
to be reconstructed. The advantage of ICA over correlation-based methods
like principal component analysis (PCA) is the utilization of higher
order statistical dependencies, which not only yield additional information
but also allow a more meaningful representation of the data with fewer
components. We performed the described ICA approach on a large-scale
metabolomics data set of human serum samples, comprising a total of
1764 study probands with 218 measured metabolites. Inspecting the <i>source matrix</i> of statistically independent metabolite profiles
using a weighted enrichment algorithm, we observe strong enrichment
of specific metabolic pathways in all components. This includes signatures
from amino acid metabolism, energy-related processes, carbohydrate
metabolism, and lipid metabolism. Our results imply that the human
blood metabolome is composed of a distinct set of overlaying, statistically
independent signals. ICA furthermore produces a <i>mixing matrix</i>, describing the strength of each independent component for each
of the study probands. Correlating these values with plasma high-density
lipoprotein (HDL) levels, we establish a novel association between
HDL plasma levels and the branched-chain amino acid pathway. We conclude
that the Bayesian ICA methodology has the power and flexibility to
replace many of the nowadays common PCA and clustering-based analyses
common in the research field
Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data
Interpreting the complex interplay of metabolites in
heterogeneous
biosamples still poses a challenging task. In this study, we propose
independent component analysis (ICA) as a multivariate analysis tool
for the interpretation of large-scale metabolomics data. In particular,
we employ a Bayesian ICA method based on a mean-field approach, which
allows us to statistically infer the number of independent components
to be reconstructed. The advantage of ICA over correlation-based methods
like principal component analysis (PCA) is the utilization of higher
order statistical dependencies, which not only yield additional information
but also allow a more meaningful representation of the data with fewer
components. We performed the described ICA approach on a large-scale
metabolomics data set of human serum samples, comprising a total of
1764 study probands with 218 measured metabolites. Inspecting the <i>source matrix</i> of statistically independent metabolite profiles
using a weighted enrichment algorithm, we observe strong enrichment
of specific metabolic pathways in all components. This includes signatures
from amino acid metabolism, energy-related processes, carbohydrate
metabolism, and lipid metabolism. Our results imply that the human
blood metabolome is composed of a distinct set of overlaying, statistically
independent signals. ICA furthermore produces a <i>mixing matrix</i>, describing the strength of each independent component for each
of the study probands. Correlating these values with plasma high-density
lipoprotein (HDL) levels, we establish a novel association between
HDL plasma levels and the branched-chain amino acid pathway. We conclude
that the Bayesian ICA methodology has the power and flexibility to
replace many of the nowadays common PCA and clustering-based analyses
common in the research field
Selected ethics statement (see also S1 Table) with specifying reporting on informed consent or ethics approval or both.
<p>Selected ethics statement (see also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002521#pbio.1002521.s001" target="_blank">S1 Table</a>) with specifying reporting on informed consent or ethics approval or both.</p
Examples of additional details on consent and approval extracted from ethics statements.
<p>Examples of additional details on consent and approval extracted from ethics statements.</p
Association analysis results in female gout case-control sample.
<p>Numbers of genotypes (11, 12, 22) according to alleles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007729#pone-0007729-t003" target="_blank">Table 3</a>.</p>a<p>Model including medication with diuretics, lipid lowering and antihypertensive therapy, HDL-C, type 2 diabetes, smoking, and BMI.</p
Association analysis results in male gout case-control sample.
<p>Numbers of genotypes (11, 12, 22) according to alleles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007729#pone-0007729-t003" target="_blank">Table 3</a>.</p>a<p>Model including medication with diuretics, lipid lowering and antihypertensive therapy, HDL-C, type 2 diabetes, smoking, and BMI.</p
SNP marker used in analysis.
a<p>on human genome build 18.</p>b<p>in total sample (<i>n</i> = 4,960).</p
Characteristics of gout case and control study sample.
<p>Values denote means±standard deviations unless indicated otherwise. n. s., not significant; CAD, coronary artery disease; MI, myocardial infarction; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index.</p>a<p>At inclusion to study.</p>b<p>Defined as LDL-C ≥160 mg/dL or intake of lipid lowering medication.</p>c<p>Defined as blood pressure ≥140/90 mmHg or ongoing antihypertensive therapy.</p>d<p>Defined as history of diabetes mellitus or intake of antidiabetic medication.</p>e<p>Former or current smoking habit.</p
Association between variations in the gene and incident type 2 diabetes is modified by the ratio of total cholesterol to HDL-cholesterol-1
Xis indicate TC/HDL-C concentrations of patients with at least one copy of the minor allele.<p><b>Copyright information:</b></p><p>Taken from "Association between variations in the gene and incident type 2 diabetes is modified by the ratio of total cholesterol to HDL-cholesterol"</p><p>http://www.biomedcentral.com/1471-2350/9/9</p><p>BMC Medical Genetics 2008;9():9-9.</p><p>Published online 25 Feb 2008</p><p>PMCID:PMC2292153.</p><p></p
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