scholarly journals Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information

2018 ◽  
Author(s):  
Xinghao Yu ◽  
Lishun Xiao ◽  
Ping Zeng ◽  
Shuiping Huang
Keyword(s):  
Predictive Accuracy ◽  
Disease Risk ◽  
Model Fitting ◽  
Model Averaging ◽  
Real Data ◽  
Genetic Data ◽  
Model Specification ◽  
High Dimensional ◽  
Data Sets ◽  
Pathway Information

AbstractMotivationIn the past few years many novel prediction approaches have been proposed and widely employed in high dimensional genetic data for disease risk evaluation. However, those approaches typically ignore in model fitting the important group structures or functional classifications that naturally exists in genetic data.MethodsIn the present study, we applied a novel model averaging approach, called Jackknife Model Averaging Prediction (JMAP), for high dimensional genetic risk prediction while incorporating KEGG pathway information into the model specification. JMAP selects the optimal weights across candidate models by minimizing a cross-validation criterion in a jackknife way. Compared with previous approaches, one of the primary features of JMAP is to allow model weights to vary from 0 to 1 but without the limitation that the summation of weights is equal to one. We evaluated the performance of JMAP using extensive simulation studies and compared it with existing methods. We finally applied JMAP to five real cancer datasets that are publicly available from TCGA.ResultsThe simulations showed that, compared with other existing approaches, JMAP performed best or are among the best methods across a range of scenarios. For example, among 14 out of 16 simulation settings with PVE=0.3, JMAP has an average of 0.075 higher prediction accuracy compared with gsslasso. We further found that in the simulation the model weights for the true candidate models have much smaller chances to be zero compared with those for the null candidate models and are substantially greater in magnitude. In the real data application, JMAP also behaves comparably or better compared with the other methods for both continuous and binary phenotypes. For example, for the COAD, CRC and PAAD data sets, the average gains of predictive accuracy of JMAP are 0.019, 0.064 and 0.052 compared with gsslasso.ConclusionThe proposed method JMAP is a novel method that can provide more accurate phenotypic prediction while incorporating external useful group information.

scholarly journals Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Xinghao Yu ◽  
Lishun Xiao ◽  
Ping Zeng ◽  
Shuiping Huang
Keyword(s):  
Risk Prediction ◽  
Genetic Risk ◽  
Model Averaging ◽  
Genetic Data ◽  
Model Specification ◽  
High Dimensional ◽  
Pathway Information ◽  
Genetic Risk Prediction ◽  
Group Structures ◽  
Novel Model

Motivation. In the past few years many prediction approaches have been proposed and widely employed in high dimensional genetic data for disease risk evaluation. However, those approaches typically ignore in model fitting the important group structures that naturally exists in genetic data. Methods. In the present study, we applied a novel model-averaging approach, called jackknife model averaging prediction (JMAP), for high dimensional genetic risk prediction while incorporating pathway information into the model specification. JMAP selects the optimal weights across candidate models by minimizing a cross validation criterion in a jackknife way. Compared with previous approaches, one of the primary features of JMAP is to allow model weights to vary from 0 to 1 but without the limitation that the summation of weights is equal to one. We evaluated the performance of JMAP using extensive simulation studies and compared it with existing methods. We finally applied JMAP to four real cancer datasets that are publicly available from TCGA. Results. The simulations showed that compared with other existing approaches (e.g., gsslasso), JMAP performed best or is among the best methods across a range of scenarios. For example, among 14 out of 16 simulation settings with PVE = 0.3, JMAP has an average of 0.075 higher prediction accuracy compared with gsslasso. We further found that in the simulation, the model weights for the true candidate models have much smaller chances to be zero compared with those for the null candidate models and are substantially greater in magnitude. In the real data application, JMAP also behaves comparably or better compared with the other methods for continuous phenotypes. For example, for the COAD, CRC, and PAAD datasets, the average gains of predictive accuracy of JMAP are 0.019, 0.064, and 0.052 compared with gsslasso. Conclusion. The proposed method JMAP is a novel model-averaging approach for high dimensional genetic risk prediction while incorporating external useful group structures into the model specification.

Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality

2018 ◽  
Vol 30 (12) ◽  
pp. 3281-3308
Author(s):  
Hong Zhu ◽  
Li-Zhi Liao ◽  
Michael K. Ng
Keyword(s):  
Dimensionality Reduction ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Main Idea ◽  
Real Data ◽  
Learning Performance ◽  
High Dimensional ◽  
Data Sets ◽  
Outer Loop ◽  
Orthogonality Constraints

We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and inertial proximal alternating linearized minimization (iPALM) iterations as the inner loop. The main advantage of this method is that both sparsity and orthogonality can be satisfied in the proposed algorithm. We show the global convergence of the proposed iterative algorithm. We also demonstrate that the proposed algorithm can achieve high sparsity and orthogonality requirements, which are very important for dimensionality reduction. Experimental results on both synthetic and real data sets show that the proposed algorithm can obtain learning performance comparable to that of other tested MI learning algorithms.

Mahalanobis distance informed by clustering

2018 ◽  
Vol 8 (2) ◽  
pp. 377-406
Author(s):  
Almog Lahav ◽  
Ronen Talmon ◽  
Yuval Kluger
Keyword(s):  
Mahalanobis Distance ◽  
High Dimensional Data ◽  
Hidden Variables ◽  
Real Data ◽  
Risk Groups ◽  
High Dimensional ◽  
Data Sets ◽  
Kaplan Meier ◽  
Data Points ◽  
Survival Plot

Abstract A fundamental question in data analysis, machine learning and signal processing is how to compare between data points. The choice of the distance metric is specifically challenging for high-dimensional data sets, where the problem of meaningfulness is more prominent (e.g. the Euclidean distance between images). In this paper, we propose to exploit a property of high-dimensional data that is usually ignored, which is the structure stemming from the relationships between the coordinates. Specifically, we show that organizing similar coordinates in clusters can be exploited for the construction of the Mahalanobis distance between samples. When the observable samples are generated by a nonlinear transformation of hidden variables, the Mahalanobis distance allows the recovery of the Euclidean distances in the hidden space. We illustrate the advantage of our approach on a synthetic example where the discovery of clusters of correlated coordinates improves the estimation of the principal directions of the samples. Our method was applied to real data of gene expression for lung adenocarcinomas (lung cancer). By using the proposed metric we found a partition of subjects to risk groups with a good separation between their Kaplan–Meier survival plot.

The Gibbs and split–merge sampler for population mixture analysis from genetic data with incomplete baselines

2006 ◽  
Vol 63 (3) ◽  
pp. 576-596 ◽  
Author(s):  
Jerome Pella ◽  
Michele Masuda
Keyword(s):  
Monte Carlo ◽  
Binary Tree ◽  
Quantitative Measure ◽  
Real Data ◽  
Genetic Data ◽  
Superior Performance ◽  
Data Sets ◽  
Linkage Equilibrium ◽  
Equilibrium Conditions ◽  
Mixture Sample

Although population mixtures often include contributions from novel populations as well as from baseline populations previously sampled, unlabeled mixture individuals can be separated to their sources from genetic data. A Gibbs and split–merge Markov chain Monte Carlo sampler is described for successively partitioning a genetic mixture sample into plausible subsets of individuals from each of the baseline and extra-baseline populations present. The subsets are selected to satisfy the Hardy–Weinberg and linkage equilibrium conditions expected for large, panmictic populations. The number of populations present can be inferred from the distribution for counts of subsets per partition drawn by the sampler. To further summarize the sampler's output, co-assignment probabilities of mixture individuals to the same subsets are computed from the partitions and are used to construct a binary tree of their relatedness. The tree graphically displays the clusters of mixture individuals together with a quantitative measure of the evidence supporting their various separate and common sources. The methodology is applied to several simulated and real data sets to illustrate its use and demonstrate the sampler's superior performance.

scholarly journals Assessing Treatment Effects with Pharmacometric Models: A New Method that Addresses Problems with Standard Assessments

2021 ◽  
Vol 23 (3) ◽  
Author(s):  
Estelle Chasseloup ◽  
Adrien Tessier ◽  
Mats O. Karlsson
Keyword(s):  
Multiple Testing ◽  
Type I Error ◽  
Drug Effect ◽  
Model Averaging ◽  
Clinical Trial Data ◽  
Real Data ◽  
Data Sets ◽  
Type I ◽  
Data Types ◽  
Inflated Type

AbstractLongitudinal pharmacometric models offer many advantages in the analysis of clinical trial data, but potentially inflated type I error and biased drug effect estimates, as a consequence of model misspecifications and multiple testing, are main drawbacks. In this work, we used real data to compare these aspects for a standard approach (STD) and a new one using mixture models, called individual model averaging (IMA). Placebo arm data sets were obtained from three clinical studies assessing ADAS-Cog scores, Likert pain scores, and seizure frequency. By randomly (1:1) assigning patients in the above data sets to “treatment” or “placebo,” we created data sets where any significant drug effect was known to be a false positive. Repeating the process of random assignment and analysis for significant drug effect many times (N = 1000) for each of the 40 to 66 placebo-drug model combinations, statistics of the type I error and drug effect bias were obtained. Across all models and the three data types, the type I error was (5th, 25th, 50th, 75th, 95th percentiles) 4.1, 11.4, 40.6, 100.0, 100.0 for STD, and 1.6, 3.5, 4.3, 5.0, 6.0 for IMA. IMA showed no bias in the drug effect estimates, whereas in STD bias was frequently present. In conclusion, STD is associated with inflated type I error and risk of biased drug effect estimates. IMA demonstrated controlled type I error and no bias.

LASSO-Type Variable Selection Methods for High-Dimensional Data

2013 ◽  
Vol 444-445 ◽  
pp. 604-609
Author(s):  
Guang Hui Fu ◽  
Pan Wang
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Real Data ◽  
Oracle Property ◽  
High Dimensional ◽  
Data Sets ◽  
Group Effect ◽  
Selection Methods ◽  
Variable Selection Method ◽  
Type Variable

LASSO is a very useful variable selection method for high-dimensional data , But it does not possess oracle property [Fan and Li, 200 and group effect [Zou and Hastie, 200. In this paper, we firstly review four improved LASSO-type methods which satisfy oracle property and (or) group effect, and then give another two new ones called WFEN and WFAEN. The performance on both the simulation and real data sets shows that WFEN and WFAEN are competitive with other LASSO-type methods.

scholarly journals Algorithmic Jingle Jungle: A Comparison of Implementations of Principal Axis Factoring and Promax Rotation in R and SPSS

2020 ◽  
Author(s):  
Silvia Grieder ◽  
Markus D. Steiner
Keyword(s):  
Data Structures ◽  
Principal Axis ◽  
Model Averaging ◽  
Simulated Data ◽  
Real Data ◽  
R Package ◽  
Data Sets ◽  
Potential Alternative ◽  
Principal Axis Factoring ◽  
Promax Rotation

A statistical procedure is assumed to produce comparable results across programs. Using the case of an exploratory factor analysis procedure—principal axis factoring (PAF) and promax rotation—we show that this assumption is not always justified. Procedures with equal names are sometimes implemented differently across programs: a jingle fallacy. Focusing on two popular statistical analysis programs, we indeed discovered a jingle jungle for the above procedure: Both PAF and promax rotation are implemented differently in the psych R package and in SPSS. Based on analyses with 230 real and 216,000 simulated data sets implementing 108 different data structures, we show that these differences in implementations can result in fairly different factor solutions for a variety of different data structures. Differences in the solutions for real data sets ranged from negligible to very large, with 38% displaying at least one different indicator-to-factor correspondence. A simulation study revealed systematic differences in accuracies between different implementations, and large variation between data structures, with small numbers of indicators per factor, high factor intercorrelations, and weak factors resulting in the lowest accuracies. Moreover, although there was no single combination of settings that was superior for all data structures, we identified implementations of PAF and promax that maximize performance on average. We recommend researchers to use these implementations as best way through the jungle, discuss model averaging as a potential alternative, and highlight the importance of adhering to best practices of scale construction.

scholarly journals Cluster Weighted Model Based on TSNE Algorithm for High-Dimensional Data

2021 ◽  
Author(s):  
Kehinde Olobatuyi
Keyword(s):  
Mixture Models ◽  
Dimensional Space ◽  
High Dimensional Data ◽  
Expectation Maximization Algorithm ◽  
Real Data ◽  
R Package ◽  
High Dimensional ◽  
Data Sets ◽  
Dimensionality Reduction Technique ◽  
Weighted Model

Abstract Similar to many Machine Learning models, both accuracy and speed of the Cluster weighted models (CWMs) can be hampered by high-dimensional data, leading to previous works on a parsimonious technique to reduce the effect of ”Curse of dimensionality” on mixture models. In this work, we review the background study of the cluster weighted models (CWMs). We further show that parsimonious technique is not sufficient for mixture models to thrive in the presence of huge high-dimensional data. We discuss a heuristic for detecting the hidden components by choosing the initial values of location parameters using the default values in the ”FlexCWM” R package. We introduce a dimensionality reduction technique called T-distributed stochastic neighbor embedding (TSNE) to enhance the parsimonious CWMs in high-dimensional space. Originally, CWMs are suited for regression but for classification purposes, all multi-class variables are transformed logarithmically with some noise. The parameters of the model are obtained via expectation maximization algorithm. The effectiveness of the discussed technique is demonstrated using real data sets from different fields.

scholarly journals Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets

2019 ◽  
Author(s):  
Homayun Afrabandpey ◽  
Tomi Peltola ◽  
Samuel Kaski
Keyword(s):  
Expert Knowledge ◽  
Covariance Structure ◽  
Predictive Performance ◽  
Real Data ◽  
High Dimensional ◽  
Strong Line ◽  
Small Data ◽  
Data Sets ◽  
Sequential Decision ◽  
Human In The Loop

Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative prior distributions for parameters. This either requires considerable statistical expertise or is laborious, as the emphasis has been on accuracy and not on efficiency of the process. Another line of work queries about importance of features one at a time, assuming them to be independent and hence missing covariance information. In contrast, we propose eliciting expert knowledge about pairwise feature similarities, to borrow statistical strength in the predictions, and using sequential decision making techniques to minimize the effort of the expert. Empirical results demonstrate improvement in predictive performance on both simulated and real data, in high-dimensional linear regression tasks, where we learn the covariance structure with a Gaussian process, based on sequential elicitation.

scholarly journals Small sample sizes: A big data problem in high-dimensional data analysis

2020 ◽  
pp. 096228022097022
Author(s):  
Frank Konietschke ◽  
Karima Schwab ◽  
Markus Pauly
Keyword(s):  
Repeated Measures ◽  
Real Data ◽  
Small Sample ◽  
High Dimensional ◽  
Data Sets ◽  
Sample Sizes ◽  
Preclinical Research ◽  
Data Set ◽  
Data Problem ◽  
Small Sample Sizes

In many experiments and especially in translational and preclinical research, sample sizes are (very) small. In addition, data designs are often high dimensional, i.e. more dependent than independent replications of the trial are observed. The present paper discusses the applicability of max t-test-type statistics (multiple contrast tests) in high-dimensional designs (repeated measures or multivariate) with small sample sizes. A randomization-based approach is developed to approximate the distribution of the maximum statistic. Extensive simulation studies confirm that the new method is particularly suitable for analyzing data sets with small sample sizes. A real data set illustrates the application of the methods.

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