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- Factor loading (2)
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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Relating Nanoparticle Properties To Biological Outcomes In Exposure Escalation Experiments, Trina Patel, Cecile Low-Kam, Zhaoxia Ji, Haiyuan Zhang, Tian Xia, Andre E. Nel, Jeffrey I. Zinc, Donatello Telesca
Relating Nanoparticle Properties To Biological Outcomes In Exposure Escalation Experiments, Trina Patel, Cecile Low-Kam, Zhaoxia Ji, Haiyuan Zhang, Tian Xia, Andre E. Nel, Jeffrey I. Zinc, Donatello Telesca
COBRA Preprint Series
A fundamental goal in nano-toxicology is that of identifying particle physical and chemical properties, which are likely to explain biological hazard. The first line of screening for potentially adverse outcomes often consists of exposure escalation experiments, involving the exposure of micro-organisms or cell lines to a battery of nanomaterials. We discuss a modeling strategy, that relates the outcome of an exposure escalation experiment to nanoparticle properties. Our approach makes use of a hierarchical decision process, where we jointly identify particles that initiate adverse biological outcomes and explain the probability of this event in terms of the particle physico-chemical descriptors. The …
Pls-Rog: Partial Least Squares With Rank Order Of Groups, Hiroyuki Yamamoto
Pls-Rog: Partial Least Squares With Rank Order Of Groups, Hiroyuki Yamamoto
COBRA Preprint Series
Partial least squares (PLS), which is an unsupervised dimensionality reduction method, has been widely used in metabolomics. PLS can separate score depend on groups in a low dimensional subspace. However, this cannot use the information about rank order of groups. This information is often provided in which concentration of administered drugs to animals is gradually varies. In this study, we proposed partial least squares for rank order of groups (PLS-ROG). PLS-ROG can consider both separation and rank order of groups.
Statistical Hypothesis Test Of Factor Loading In Principal Component Analysis And Its Application To Metabolite Set Enrichment Analysis, Hiroyuki Yamamoto, Tamaki Fujimori, Hajime Sato, Gen Ishikawa, Kenjiro Kami, Yoshiaki Ohashi
Statistical Hypothesis Test Of Factor Loading In Principal Component Analysis And Its Application To Metabolite Set Enrichment Analysis, Hiroyuki Yamamoto, Tamaki Fujimori, Hajime Sato, Gen Ishikawa, Kenjiro Kami, Yoshiaki Ohashi
COBRA Preprint Series
Principal component analysis (PCA) has been widely used to visualize high-dimensional metabolomic data in a two- or three-dimensional subspace. In metabolomics, some metabolites (e.g. top 10 metabolites) have been subjectively selected when using factor loading in PCA, and biological inferences for these metabolites are made. However, this approach is possible to lead biased biological inferences because these metabolites are not objectively selected by statistical criterion. We proposed a statistical procedure to pick up metabolites by statistical hypothesis test of factor loading in PCA and make biological inferences by metabolite set enrichment analysis (MSEA) for these significant metabolites. This procedure depends …
Quantifying Alternative Splicing From Paired-End Rna-Sequencing Data, David Rossell, Camille Stephan-Otto Attolini, Manuel Kroiss, Almond Stöcker
Quantifying Alternative Splicing From Paired-End Rna-Sequencing Data, David Rossell, Camille Stephan-Otto Attolini, Manuel Kroiss, Almond Stöcker
COBRA Preprint Series
RNA-sequencing has revolutionized biomedical research and, in particular, our ability to study gene alternative splicing. The problem has important implications for human health, as alternative splicing is involved in malfunctions at the cellular level and multiple diseases. However, the high-dimensional nature of the data and the existence of experimental biases pose serious data analysis challenges. We find that the standard data summaries used to study alternative splicing are severely limited, as they ignore a substantial amount of valuable information. Current data analysis methods are based on such summaries and are hence sub-optimal. Further, they have limited flexibility in accounting for …
Robust Estimation Of Pure/Natural Direct Effects With Mediator Measurement Error, Eric J. Tchetgen Tchetgen, Sheng Hsuan Lin
Robust Estimation Of Pure/Natural Direct Effects With Mediator Measurement Error, Eric J. Tchetgen Tchetgen, Sheng Hsuan Lin
COBRA Preprint Series
Recent developments in causal mediation analysis have offered new notions of direct and indirect effects, that formalize more traditional and informal notions of mediation analysis emanating primarily from the social sciences. The pure or natural direct effect of Robins-Greenland-Pearl quantifies the causal effect of an exposure that is not mediated by a variable on the causal pathway to the outcome, and combines with the natural indirect effect to produce the total causal effect of the exposure. Sufficient conditions for identification of natural direct effects were previously given, that assume certain independencies about potential outcomes, and a rich literature on estimation …
A Prior-Free Framework Of Coherent Inference And Its Derivation Of Simple Shrinkage Estimators, David R. Bickel
A Prior-Free Framework Of Coherent Inference And Its Derivation Of Simple Shrinkage Estimators, David R. Bickel
COBRA Preprint Series
The reasoning behind uses of confidence intervals and p-values in scientific practice may be made coherent by modeling the inferring statistician or scientist as an idealized intelligent agent. With other things equal, such an agent regards a hypothesis coinciding with a confidence interval of a higher confidence level as more certain than a hypothesis coinciding with a confidence interval of a lower confidence level. The agent uses different methods of confidence intervals conditional on what information is available. The coherence requirement means all levels of certainty of hypotheses about the parameter agree with the same distribution of certainty over parameter …
On Identification Of Natural Direct Effects When A Confounder Of The Mediator Is Directly Affected By Exposure, Eric J. Tchetgen Tchetgen, Tyler J. Vanderweele
On Identification Of Natural Direct Effects When A Confounder Of The Mediator Is Directly Affected By Exposure, Eric J. Tchetgen Tchetgen, Tyler J. Vanderweele
COBRA Preprint Series
Natural direct and indirect effects formalize traditional notions of mediation analysis into a rigorous causal framework and have recently received considerable attention in epidemiology and in the social sciences. Sufficient conditions for identification of natural direct effects were formulated by Judea Pearl under a nonparametric structural equations model, which assumes certain independencies between potential outcomes. A common situation in epidemiology is that a confounder of the mediator is affected by the exposure, in which case, natural direct effects fail to be nonparametrically identified without additional assumptions, even under Pearl's nonparametric structural equations model. In this paper, the authors show that …
Why Odds Ratio Estimates Of Gwas Are Almost Always Close To 1.0, Yutaka Yasui
Why Odds Ratio Estimates Of Gwas Are Almost Always Close To 1.0, Yutaka Yasui
COBRA Preprint Series
“Missing heritability” in genome-wide association studies (GWAS) refers to the seeming inability for GWAS data to capture the great majority of genetic causes of a disease in comparison to the known degree of heritability for the disease, in spite of GWAS’ genome-wide measures of genetic variations. This paper presents a simple mathematical explanation for this phenomenon, assuming that the heritability information exists in GWAS data. Specifically, it focuses on the fact that the great majority of association measures (in the form of odds ratios) from GWAS are consistently close to the value that indicates no association, explains why this occurs, …
Differential Patterns Of Interaction And Gaussian Graphical Models, Masanao Yajima, Donatello Telesca, Yuan Ji, Peter Muller
Differential Patterns Of Interaction And Gaussian Graphical Models, Masanao Yajima, Donatello Telesca, Yuan Ji, Peter Muller
COBRA Preprint Series
We propose a methodological framework to assess heterogeneous patterns of association amongst components of a random vector expressed as a Gaussian directed acyclic graph. The proposed framework is likely to be useful when primary interest focuses on potential contrasts characterizing the association structure between known subgroups of a given sample. We provide inferential frameworks as well as an efficient computational algorithm to fit such a model and illustrate its validity through a simulation. We apply the model to Reverse Phase Protein Array data on Acute Myeloid Leukemia patients to show the contrast of association structure between refractory patients and relapsed …
Hierarchical Rank Aggregation With Applications To Nanotoxicology, Trina Patel, Donatello Telesca, Robert Rallo, Saji George, Xia Tian, Nel Andre
Hierarchical Rank Aggregation With Applications To Nanotoxicology, Trina Patel, Donatello Telesca, Robert Rallo, Saji George, Xia Tian, Nel Andre
COBRA Preprint Series
The development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENM). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian models coupled with the optimization of loss functions have been shown to provide an effective framework for conducting inference on ranks. In this article we present various loss function based ranking approaches for comparing ENM within experiments and toxicity parameters. Additionally, we propose …
Robustness Of Measures Of Interaction To Unmeasured Confounding, Eric J. Tchetgen Tchetgen, Tyler J. Vanderweele
Robustness Of Measures Of Interaction To Unmeasured Confounding, Eric J. Tchetgen Tchetgen, Tyler J. Vanderweele
COBRA Preprint Series
In this paper, we study the impact of unmeasured confounding on inference about a two-way interaction in a mean regression model with identity, log or logit link function. Necessary and sufficient conditions are established for a two-way interaction to be nonparametrically identified from the observed data, despite unmeasured confounding for the factors defining the interaction. A lung cancer data application illustrates the results.