Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs.

ABSTRACT:

Background

Directed acyclic graphs (DAGs) are popular tools for identifying appropriate adjustment strategies for epidemiological analysis. However, a lack of direction on how to build them is problematic. As a solution, we propose using a combination of evidence synthesis strategies and causal inference principles to integrate the DAG-building exercise within the review stages of research projects. We demonstrate this idea by introducing a novel protocol: 'Evidence Synthesis for Constructing Directed Acyclic Graphs' (ESC-DAGs)'.

Methods

ESC-DAGs operates on empirical studies identified by a literature search, ideally a novel systematic review or review of systematic reviews. It involves three key stages: (i) the conclusions of each study are 'mapped' into a DAG; (ii) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly; (iii) the resulting DAGs are then synthesised into one or more 'integrated DAGs'. This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence.

Conclusions

ESC-DAGs is a practical, systematic and transparent approach for developing DAGs from background knowledge. These DAGs can then direct primary data analysis and DAG-based sensitivity analysis. ESC-DAGs has a modular design to allow researchers who are experienced DAG users to both use and improve upon the approach. It is also accessible to researchers with limited experience of DAGs or evidence synthesis.

SUBMITTER: Ferguson KD

PROVIDER: S-EPMC7124493 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Analyzing microarray data with transitive directed acyclic graphs

Project description:Test compound one, 5,6-benzoflavone (BNF), was known to act through both the Ah receptor and Nrf2 receptor pathways, while test compounds two and three, 3H-1,2-dithiole-3-thione (D3T) and 4-methyl-5-pyrazinyl-3H-1,2-dithiole-3-thione (OLT), were known to act through the Nrf2 receptor pathway. Furthermore, D3T is known to be more potent and efficacious than OLT for Nrf2 activation. OLT has been shown to exhibit 20-50% of the efficacy of D3T for inhibition of alfatoxin-induced heptic foci. Nonetheless, because OLT is an approved drug, it is currently being evaluated in human phase II intervention trials of biomarkers of alfatoxin-related hepatocellular carcinoma. More recently, BNF was shown to be an effective chemopreventive agent in the rat mammary carcinogen model, inhibiting 7,12-dimethylbenz(a)anthracene DNA adduct formation in liver and mammary cells by 96 and 83% respectively. We used microarrays to study the structure activities that lie within the test compounds. Keywords: treatment effect study

2008-12-30 | GSE8880 | GEO

Phasertng: directed acyclic graphs for crystallographic phasing.

Project description:Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.

| S-EPMC7787104 | biostudies-literature

Directed Acyclic Graphs for Oral Disease Research.

Project description:Directed acyclic graphs (DAGs) are nonparametric graphical tools used to depict causal relations in the epidemiologic assessment of exposure-outcome associations. Although their use in dental research was first advocated in 2002, DAGs have yet to be widely adopted in this field. DAGs help identify threats to causal inference such as confounders, bias due to subject selection, and inappropriate handling of missing data. DAGs can also inform the data analysis strategy based on relations among variables depicted on it. This article uses the example of a study of temporomandibular disorders (TMDs), investigating causal effects of facial injury on subsequent risk of TMD. We illustrate how DAGs can be used to identify 1) potential confounders, 2) mediators and the consequences of attempt to estimate direct causal effects, 3) colliders and the consequences of conditioning on colliders, and 4) variables that are simultaneously mediators and confounders and the consequences of adjustment for such variables. For example, one DAG shows that statistical adjustment for the pressure pain threshold would necessarily bias the causal relation between facial injury and TMD. Finally, we discuss the usefulness of DAGs during study design, subject selection, and choosing variables to be measured in a study.

| S-EPMC4935832 | biostudies-literature

Analyzing microarray data with transitive directed acyclic graphs.

Project description:Post hoc assignment of patterns determined by all pairwise comparisons in microarray experiments with multiple treatments has been proven to be useful in assessing treatment effects. We propose the usage of transitive directed acyclic graphs (tDAG) as the representation of these patterns and show that such representation can be useful in clustering treatment effects, annotating existing clustering methods, and analyzing sample sizes. Advantages of this approach include: (1) unique and descriptive meaning of each cluster in terms of how genes respond to all pairs of treatments; (2) insensitivity of the observed patterns to the number of genes analyzed; and (3) a combinatorial perspective to address the sample size problem by observing the rate of contractible tDAG as the number of replicates increases. The advantages and overall utility of the method in elaborating drug structure activity relationships are exemplified in a controlled study with real and simulated data.

| S-EPMC2743182 | biostudies-literature

Statistical tests for associations between two directed acyclic graphs.

Project description:Biological data, and particularly annotation data, are increasingly being represented in directed acyclic graphs (DAGs). However, while relevant biological information is implicit in the links between multiple domains, annotations from these different domains are usually represented in distinct, unconnected DAGs, making links between the domains represented difficult to determine. We develop a novel family of general statistical tests for the discovery of strong associations between two directed acyclic graphs. Our method takes the topology of the input graphs and the specificity and relevance of associations between nodes into consideration. We apply our method to the extraction of associations between biomedical ontologies in an extensive use-case. Through a manual and an automatic evaluation, we show that our tests discover biologically relevant relations. The suite of statistical tests we develop for this purpose is implemented and freely available for download.

| S-EPMC2886832 | biostudies-literature

Enumerating consistent sub-graphs of directed acyclic graphs: an insight into biomedical ontologies.

Project description:MotivationModern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics), often organized in concept hierarchies or ontologies. Most ontology can be seen as directed acyclic graphs (DAGs), where nodes represent concepts and edges represent relational ties between these concepts. Given an ontology graph, each object can only be annotated by a consistent sub-graph; that is, a sub-graph such that if an object is annotated by a particular concept, it must also be annotated by all other concepts that generalize it. Ontologies therefore provide a compact representation of a large space of possible consistent sub-graphs; however, until now we have not been aware of a practical algorithm that can enumerate such annotation spaces for a given ontology.ResultsWe propose an algorithm for enumerating consistent sub-graphs of DAGs. The algorithm recursively partitions the graph into strictly smaller graphs until the resulting graph becomes a rooted tree (forest), for which a linear-time solution is computed. It then combines the tallies from graphs created in the recursion to obtain the final count. We prove the correctness of this algorithm, propose several practical accelerations, evaluate it on random graphs and then apply it to characterize four major biomedical ontologies. We believe this work provides valuable insights into the complexity of concept annotation spaces and its potential influence on the predictability of ontological annotation.Availability and implementationhttps://github.com/shawn-peng/counting-consistent-sub-DAG.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6022688 | biostudies-literature

Identification In Missing Data Models Represented By Directed Acyclic Graphs.

Project description:Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target distribution using censored observed data, rely on missing data models represented as a factorization with respect to a directed acyclic graph. In this paper we consider the identifiability of the target distribution within this class of models, and show that the most general identification strategies proposed so far retain a significant gap in that they fail to identify a wide class of identifiable distributions. To address this gap, we propose a new algorithm that significantly generalizes the types of manipulations used in the ID algorithm [14, 16], developed in the context of causal inference, in order to obtain identification.

| S-EPMC6935350 | biostudies-literature

Estimation of high-dimensional directed acyclic graphs with surrogate intervention.

Project description:Directed acyclic graphs (DAGs) have been used to describe causal relationships between variables. The standard method for determining such relations uses interventional data. For complex systems with high-dimensional data, however, such interventional data are often not available. Therefore, it is desirable to estimate causal structure from observational data without subjecting variables to interventions. Observational data can be used to estimate the skeleton of a DAG and the directions of a limited number of edges. We develop a Bayesian framework to estimate a DAG using surrogate interventional data, where the interventions are applied to a set of external variables, and thus such interventions are considered to be surrogate interventions on the variables of interest. Our work is motivated by expression quantitative trait locus (eQTL) studies, where the variables of interest are the expression of genes, the external variables are DNA variations, and interventions are applied to DNA variants during the process of a randomly selected DNA allele being passed to a child from either parent. Our method, surrogate intervention recovery of a DAG ($\texttt{sirDAG}$), first constructs a DAG skeleton using penalized regressions and the subsequent partial correlation tests, and then estimates the posterior probabilities of all the edge directions after incorporating DNA variant data. We demonstrate the utilities of $\texttt{sirDAG}$ by simulation and an application to an eQTL study for 550 breast cancer patients.

| S-EPMC7776804 | biostudies-literature

Estimation of sparse directed acyclic graphs for multivariate counts data.

Project description:The next-generation sequencing data, called high-throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this article provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study.

| S-EPMC4975686 | biostudies-literature

Directed acyclic graphs: a tool for causal studies in paediatrics.

Project description:Many paediatric clinical research studies, whether observational or interventional, have as an eventual aim the identification or quantification of causal relationships. One might ask: does screen time influence childhood obesity? Could overuse of paracetamol in infancy cause wheeze? How does breastfeeding affect later cognitive outcomes? In this review, we present causal directed acyclic graphs (DAGs) to a paediatric audience. DAGs are a graphical tool which provide a way to visually represent and better understand the key concepts of exposure, outcome, causation, confounding, and bias. We use clinical examples, including those outlined above, framed in the language of DAGs, to demonstrate their potential applications. We show how DAGs can be most useful in identifying confounding and sources of bias, demonstrating inappropriate statistical adjustments for presumed biases, and understanding threats to validity in randomised controlled trials. We believe that a familiarity with DAGs, and the concepts underlying them, will be of benefit both to the researchers planning studies, and practising clinicians interpreting them.

| S-EPMC6215481 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data