Dataset Information

Capturing heterogeneity in gene expression studies by surrogate variable analysis.

ABSTRACT: It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce "surrogate variable analysis" (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies.

SUBMITTER: Leek JT

PROVIDER: S-EPMC1994707 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Capturing heterogeneity in gene expression studies by surrogate variable analysis.

Leek Jeffrey T JT Storey John D JD

PLoS genetics 20070801 9

It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can th ...[more]

PMID: 17907809

Similar Datasets

Project description:BackgroundThe choice of outcome measure is a critical decision in the design of any clinical trial, but many Phase III clinical trials in critical care fail to detect a difference between the interventions being compared. This may be because the surrogate outcomes used to show beneficial effects in early phase trials (which informed the design of the subsequent Phase III trials) are not valid guides to the differences between the interventions for the main outcomes of the Phase III trials. We undertook a systematic review (1) to generate a list of outcome measures used in critical care trials, (2) to determine the variability in the outcome reporting in the respiratory subgroup and (3) to create a smaller list of potential early phase endpoints in the respiratory subgroup.MethodsData related to outcomes were extracted from studies published in the six top-ranked critical care journals between 2010 and 2020. Outcomes were classified into subcategories and categories. A subset of early phase endpoints relevant to the respiratory subgroup was selected for further investigation. The variability of the outcomes and the variability in reporting was investigated.ResultsA total of 6905 references were retrieved and a total of 294 separate outcomes were identified from 58 studies. The outcomes were then classified into 11 categories and 66 subcategories. A subset of 22 outcomes relevant for the respiratory group were identified as potential early phase outcomes. The summary statistics, time points and definitions show the outcomes are analysed and reported in different ways.ConclusionThe outcome measures were defined, analysed and reported in a variety of ways. This creates difficulties for synthesising data in systematic reviews and planning definitive trials. This review once again highlights an urgent need for standardisation and validation of surrogate outcomes reported in critical care trials. Future work should aim to validate and develop a core outcome set for surrogate outcomes in critical care trials.

Dataset Information

Capturing heterogeneity in gene expression studies by surrogate variable analysis.

Publications

Capturing heterogeneity in gene expression studies by surrogate variable analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets