Covariance-based sample selection for heterogeneous data: Applications to gene expression and autism risk gene detection.
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ABSTRACT:
SUBMITTER: Lin KZ
PROVIDER: S-EPMC7958652 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo assess the issue of nonrepresentative sampling in Medicare Advantage (MA) risk adjustment.Data sourcesMedicare enrollment and claims data from 2008 to 2011.Data extractionRisk adjustment predictor variables were created from 2008 to 2010 Part A and B claims and the Medicare Beneficiary Summary File. Spending is based on 2009-2011 Part A and B, Durable Medical Equipment, and Home Health Agency claims files.Study designA propensity-score matched sample of Traditional Medicare (TM) beneficiaries who resembled MA enrollees was created. Risk adjustment formulas were estimated using multiple techniques, and performance was evaluated based on R2 , predictive ratios, and formula coefficients in the matched sample and a random sample of TM beneficiaries.Principal findingsMatching improved balance on observables, but performance metrics were similar when comparing risk adjustment formula results fit on and evaluated in the matched sample versus fit on the random sample and evaluated in the matched sample.ConclusionsFitting MA risk adjustment formulas on a random sample versus a matched sample yields little difference in MA plan payments. This does not rule out potential improvements via the matching method should reliable MA encounter data and additional variables become available for risk adjustment.
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Project description:MotivationVery little attention has been given to gene selection procedures based on intergene correlation structure, which is often neglected in the context of differential gene expression analysis. We propose a statistical procedure to select genes that have different associations with others across different phenotypes. This procedure is based on a new gene association score, called the covariance distance.ResultsWe apply the proposed method, along with two alternative methods, to several simulated datasets and find out that our method is much more powerful than the other two. For biological data, we demonstrate that the analysis of differentially associated genes complements the analysis of differentially expressed genes. Combining both procedures provides a more comprehensive functional interpretation of the experimental results.AvailabilityThe code is downloadable from http://www.urmc.rochester.edu/biostat/people/faculty/hu.cfm.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Batch bias has been found in many microarray gene expression studies that involve multiple batches of samples. A serious batch effect can alter not only the distribution of individual genes but also the inter-gene relationships. Even though some efforts have been made to remove such bias, there has been relatively less development on a multivariate approach, mainly because of the analytical difficulty due to the high-dimensional nature of gene expression data. We propose a multivariate batch adjustment method that effectively eliminates inter-gene batch effects. The proposed method utilizes high-dimensional sparse covariance estimation based on a factor model and a hard thresholding. Another important aspect of the proposed method is that if it is known that one of the batches is produced in a superior condition, the other batches can be adjusted so that they resemble the target batch. We study high-dimensional asymptotic properties of the proposed estimator and compare the performance of the proposed method with some popular existing methods with simulated data and gene expression data sets.
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Project description:BACKGROUND:Learning accurate models from 'omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals' outlierness based on the Cook's distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level. RESULTS:We applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified. CONCLUSIONS:The proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.
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Project description:BACKGROUND: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses. RESULTS: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system. CONCLUSION: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.
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Project description:Structural brain alterations in autism spectrum disorder (ASD) are heterogeneous, with limited effect sizes overall. In this study, we aimed to identify subgroups in ASD, based on neuroanatomical profiles; we hypothesized that the effect sizes for case/control differences would be increased in the newly defined subgroups. Analyzing a large data set from the ENIGMA-ASD working group (n = 2661), we applied exploratory factor analysis (EFA) to seven subcortical volumes of individuals with and without ASD to uncover the underlying organization of subcortical structures. Based on earlier findings and data availability, we focused on three age groups: boys (<=14 years), male adolescents (15-22 years), and adult men (> = 22 years). The resulting factor scores were used in a community detection (CD) analysis to cluster participants into subgroups. Three factors were found in each subsample; the factor structure in adult men differed from that in boys and male adolescents. From these factors, CD uncovered four distinct communities in boys and three communities in adolescents and adult men, irrespective of ASD diagnosis. The effect sizes for case/control comparisons were more pronounced than in the combined sample, for some communities. A significant group difference in ADOS scores between communities was observed in boys and male adolescents with ASD. We succeeded in stratifying participants into more homogeneous subgroups based on subcortical brain volumes. This stratification enhanced our ability to observe case/control differences in subcortical brain volumes in ASD, and may help to explain the heterogeneity of previous findings in ASD. LAY SUMMARY: Structural brain alterations in ASD are heterogeneous, with overall limited effect sizes. Here we aimed to identify subgroups in ASD based on neuroimaging measures. We tested whether the effect sizes for case/control differences would be increased in the newly defined subgroups. Based on neuroanatomical profiles, we succeeded in stratifying our participants into more homogeneous subgroups. The effect sizes of case/control differences were more pronounced in some subgroups than those in the whole sample.
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