Dataset Information

Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data.

ABSTRACT: Triplet ordering preferences are used to perform Monte Carlo sampling of the posterior causal orderings originating from the analysis of gene-expression experiments involving observation as well as, usually few, interventions, like knock-outs. The performance of this sampling approach is compared to a previously used sampling via pairwise ordering preference as well as to the sampling of the full posterior distribution. For a fair comparison, the latter approach is restricted to twice the numerical effort of the triplet-based approach. This is done for artificially generated causal, i.e., directed acyclic graphs (DAGs) and for actual experimental data taken from the ROSETTA challenge. The sampling using the triplets ordering turns out to be superior to both other approaches.

SUBMITTER: Hartmann AK

PROVIDER: S-EPMC5283676 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data.

Hartmann Alexander K AK Nuel Grégory G

PloS one 20170131 1

Triplet ordering preferences are used to perform Monte Carlo sampling of the posterior causal orderings originating from the analysis of gene-expression experiments involving observation as well as, usually few, interventions, like knock-outs. The performance of this sampling approach is compared to a previously used sampling via pairwise ordering preference as well as to the sampling of the full posterior distribution. For a fair comparison, the latter approach is restricted to twice the numeri ...[more]

PMID: 28141825

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Project description:BACKGROUND: In recent years, there has been great interest in using transcriptomic data to infer gene regulatory networks. For the time being, methodological development in this area has primarily made use of graphical Gaussian models for observational wild-type data, resulting in undirected graphs that are not able to accurately highlight causal relationships among genes. In the present work, we seek to improve the estimation of causal effects among genes by jointly modeling observational transcriptomic data with arbitrarily complex intervention data obtained by performing partial, single, or multiple gene knock-outs or knock-downs. RESULTS: Using the framework of causal Gaussian Bayesian networks, we propose a Markov chain Monte Carlo algorithm with a Mallows proposal model and analytical likelihood maximization to sample from the posterior distribution of causal node orderings, and in turn, to estimate causal effects. The main advantage of the proposed algorithm over previously proposed methods is its flexibility to accommodate any kind of intervention design, including partial or multiple knock-out experiments. Using simulated data as well as data from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 2007 challenge, the proposed method was compared to two alternative approaches: one requiring a complete, single knock-out design, and one able to model only observational data. CONCLUSIONS: The proposed algorithm was found to perform as well as, and in most cases better, than the alternative methods in terms of accuracy for the estimation of causal effects. In addition, multiple knock-outs proved to contribute valuable additional information compared to single knock-outs. Finally, the simulation study confirmed that it is not possible to estimate the causal ordering of genes from observational data alone. In all cases, we found that the inclusion of intervention experiments enabled more accurate estimation of causal regulatory relationships than the use of wild-type data alone.

Project description:BackgroundMicroarray technology is a high-throughput method for measuring the expression levels of thousand of genes simultaneously. The observed intensities combine a non-specific binding, which is a major disadvantage with microarray data. The Affymetrix GeneChip assigned a mismatch (MM) probe with the intention of measuring non-specific binding, but various opinions exist regarding usefulness of MM measures. It should be noted that not all observed intensities are associated with expressed genes and many of those are associated with unexpressed genes, of which measured values express mere noise due to non-specific binding, cross-hybridization, or stray signals. The implicit assumption that all genes are expressed leads to poor performance of microarray data analyses. We assume two functional states of a gene - expressed or unexpressed - and propose a robust method to estimate gene expression states using an order relationship between PM and MM measures.ResultsAn indicator 'probability of a gene being expressed' was obtained using the number of probe pairs within a probe set where the PM measure exceeds the MM measure. We examined the validity of the proposed indicator using Human Genome U95 data sets provided by Affymetrix. The usefulness of 'probability of a gene being expressed' is illustrated through an exploration of candidate genes involved in neuroblastoma prognosis. We identified the candidate genes for which expression states differed (un-expressed or expressed) when compared between two outcomes. The validity of this result was subsequently confirmed by quantitative RT-PCR.ConclusionThe proposed qualitative evaluation, 'probability of a gene being expressed', is a useful indicator for improving microarray data analysis. It is useful to reduce the number of false discoveries. Expression states - expressed or unexpressed - correspond to the most fundamental gene function 'On' and 'Off', which can lead to biologically meaningful results.

Project description:BackgroundA well-known challenge in estimating the mortality risks of obesity is reverse causality attributable to illness-associated and smoking-associated weight loss. Given that the likelihood of chronic and acute illnesses rises with age, reverse causality is most threatening to estimates derived from elderly populations.MethodsI analyzed data from 12,523 respondents over 50 years old from a nationally representative longitudinal dataset, the Health and Retirement Study (HRS). The effects of both baseline body weight and time-varying weight change on mortality are estimated, adjusting for demographic and socio-economic variables, as well as time-varying confounders including illness and smoking. Body weight is measured by body mass index (BMI). In survival models for mortality, illness and smoking were lagged to minimize bias from reverse causality in estimates of the effect of weight change. Furthermore, because illness both causes and is caused by changes in BMI, I used a marginal structural model (MSM) rather than standard adjustment to control confounding by this and other time-dependent factors.ResultsOverall, relative to normal weight, underweight and Class II/III at baseline are associated with hazard ratios that are 2.07 (95% confidence interval (CI): 1.28-3.37) and 1.82 (1.54-2.16) respectively, whereas overweight and Class I obesity do not significantly lower or raise the mortality risks. Furthermore, relative to stable weight change, all types of weight change lead to significantly increased risk of mortality. Specifically, large weight loss results in a mortality risk that is nearly 3.86 (3.26-4.58) times of staying in the stable weight range and small weight loss is about 1.81 (1.55-2.11 ) times riskier. In contrast, large weight gain and small weight gain are associated with hazard ratios that are 1.98 (1.67-2.35) and 1.20 (1.02-1.41) respectively.ConclusionsBeing underweight or severe obese at baseline is associated with excess mortality risk, and weight change tend to raise mortality risk. Both the confounding by illness and by smoking lead to overestimates of the effects of being underweight at baseline and of weight loss, but underestimates the effect of being obese at baseline.

Dataset Information

Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data.

Publications

Using Triplet Ordering Preferences for Estimating Causal Effects in the Analysis of Gene Expression Data.

Similar Datasets

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