Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information

Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author’s webpage.

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Estimating Group Differences in Network Models using Moderation Analysis

10.31234/osf.io/926pv ◽

2020 ◽

Author(s):

Jonas M B Haslbeck

Keyword(s):

Ising Model ◽

Graphical Model ◽

Network Models ◽

R Package ◽

Group Differences ◽

Data Sets ◽

Gaussian Graphical Model ◽

Single Model ◽

Cross Sectional ◽

Moderation Analysis

Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological data sets. In many applications the goal is to compare such network models across groups. In this paper I introduce a method to estimate differences in network models across groups that is based on moderation analysis. This method is attractive because it allows to make comparisons across more than two groups within a single model, and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the moderation method to compare a network model across three groups using the R-package mgm.

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A modified expectation‐maximization algorithm for latent Gaussian graphical model

Canadian Journal of Statistics ◽

10.1002/cjs.11643 ◽

2021 ◽

Author(s):

Chaowen Zheng ◽

Jingfang Huang ◽

Ian A. Wood ◽

Yichao Wu

Keyword(s):

Expectation Maximization ◽

Graphical Model ◽

Expectation Maximization Algorithm ◽

Gaussian Graphical Model

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MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

BMC Bioinformatics ◽

10.1186/s12859-021-04288-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yance Feng ◽

Lei M. Li

Keyword(s):

Biological Significance ◽

Housekeeping Genes ◽

R Package ◽

Data Sets ◽

Statistical Regression ◽

Rna Seq ◽

Least Trimmed Squares ◽

Standard Data ◽

Wide Range ◽

Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

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Screening Breast MRI and Gadolinium Deposition: Cause for Concern?

Journal of Breast Imaging ◽

10.1093/jbi/wbab074 ◽

2021 ◽

Author(s):

Colleen H Neal

Keyword(s):

Breast Cancer ◽

Renal Function ◽

Contrast Agents ◽

Biological Significance ◽

Breast Mri ◽

Reliable Data ◽

Breast Cancer Data ◽

Cancer Data ◽

Contrast Enhanced ◽

Gadolinium Retention

Abstract Gadolinium-based contrast agents (GBCAs) have been used worldwide for over 30 years and have enabled lifesaving diagnoses. Contrast-enhanced breast MRI is frequently used as supplemental screening for women with an elevated lifetime risk of breast cancer. Data have emerged that indicate a fractional amount of administered gadolinium is retained in the bone, skin, solid organs, and brain tissues of patients with normal renal function, although there are currently no reliable data regarding the clinical or biological significance of this retention. Linear GBCAs are associated with a higher risk of gadolinium retention than macrocyclic agents. Over the course of their lives, screened women may receive high cumulative doses of GBCA. Therefore, as breast MRI screening utilization increases, thoughtful use of GBCA is indicated in this patient population.

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Investigation of the international comparability of population-based routine hospital data set derived comorbidity scores for patients with lung cancer

Thorax ◽

10.1136/thoraxjnl-2017-210362 ◽

2017 ◽

Vol 73 (4) ◽

pp. 339-349 ◽

Cited By ~ 4

Author(s):

Margreet Lüchtenborg ◽

Eva J A Morris ◽

Daniela Tataru ◽

Victoria H Coupland ◽

Andrew Smith ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Survival ◽

Population Based ◽

Data Sets ◽

Hospital Data ◽

Data Set ◽

Discharge Data ◽

International Differences ◽

Cancer Data ◽

Comorbidity Scores

IntroductionThe International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome.MethodsLinked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons.ResultsIt was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable.ConclusionThe results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required.

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Sparse Gaussian graphical model estimation via alternating minimization

Biometrika ◽

10.1093/biomet/asx003 ◽

2017 ◽

Vol 104 (2) ◽

pp. 379-395 ◽

Cited By ~ 4

Author(s):

Onkar Dalal ◽

Bala Rajaratnam

Keyword(s):

Graphical Model ◽

Model Estimation ◽

Alternating Minimization ◽

Gaussian Graphical Model

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Gaussian Message Propagation in d-order Neighborhood for Gaussian Graphical Model

Advances in Neural Networks – ISNN 2013 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-39065-4_65 ◽

2013 ◽

pp. 539-546

Author(s):

Yarui Chen ◽

Congcong Xiong ◽

Hailin Xie

Keyword(s):

Graphical Model ◽

Gaussian Graphical Model ◽

Message Propagation

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Analysis and Integration of Biological Data

Knowledge Discovery Practices and Emerging Applications of Data Mining - Advances in Data Mining and Database Management ◽

10.4018/978-1-60960-067-9.ch014 ◽

2010 ◽

pp. 287-314

Author(s):

Diego Milone ◽

Georgina Stegmayer ◽

Matías Gerard ◽

Laura Kamenetzky ◽

Mariana López ◽

...

Keyword(s):

Data Integration ◽

New Technologies ◽

Biological Significance ◽

Neural Model ◽

Biological Data ◽

Data Sets ◽

Clustering Methods ◽

Gene Expressions ◽

Internal Coherence ◽

Biological Data Integration

The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.

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