Visualization and Outlier Detection for Multivariate Elastic Curve Data.
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ABSTRACT:
SUBMITTER: Xie W
PROVIDER: S-EPMC7589786 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationIt has become routine in neuroscience studies to measure brain networks for different individuals using neuroimaging. These networks are typically expressed as adjacency matrices, with each cell containing a summary of connectivity between a pair of brain regions. There is an emerging statistical literature describing methods for the analysis of such multi-network data in which nodes are common across networks but the edges vary. However, there has been essentially no consideration of the important problem of outlier detection. In particular, for certain subjects, the neuroimaging data are so poor quality that the network cannot be reliably reconstructed. For such subjects, the resulting adjacency matrix may be mostly zero or exhibit a bizarre pattern not consistent with a functioning brain. These outlying networks may serve as influential points, contaminating subsequent statistical analyses. We propose a simple Outlier DetectIon for Networks (ODIN) method relying on an influence measure under a hierarchical generalized linear model for the adjacency matrices. An efficient computational algorithm is described, and ODIN is illustrated through simulations and an application to data from the UK Biobank.ResultsODIN was successful in identifying moderate to extreme outliers. Removing such outliers can significantly change inferences in downstream applications.Availability and implementationODIN has been implemented in both Python and R and these implementations along with other code are publicly available at github.com/pritamdey/ODIN-python and github.com/pritamdey/ODIN-r, respectively.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9890313 | biostudies-literature
Project description:BackgroundModeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data.ResultsIn this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data.ConclusionProfiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.
| S-EPMC1539028 | biostudies-literature
Project description:BackgroundPatterns of multi-locus differentiation (i.e., genomic clines) often extend broadly across hybrid zones and their quantification can help diagnose how species boundaries are shaped by adaptive processes, both intrinsic and extrinsic. In this sense, the transitioning of loci across admixed individuals can be contrasted as a function of the genome-wide trend, in turn allowing an expansion of clinal theory across a much wider array of biodiversity. However, computational tools that serve to interpret and consequently visualize 'genomic clines' are limited, and users must often write custom, relatively complex code to do so.ResultsHere, we introduce the ClineHelpR R-package for visualizing genomic clines and detecting outlier loci using output generated by two popular software packages, bgc and Introgress. ClineHelpR bundles both input generation (i.e., filtering datasets and creating specialized file formats) and output processing (e.g., MCMC thinning and burn-in) with functions that directly facilitate interpretation and hypothesis testing. Tools are also provided for post-hoc analyses that interface with external packages such as ENMeval and RIdeogram.ConclusionsOur package increases the reproducibility and accessibility of genomic cline methods, thus allowing an expanded user base and promoting these methods as mechanisms to address diverse evolutionary questions in both model and non-model organisms. Furthermore, the ClineHelpR extended functionality can evaluate genomic clines in the context of spatial and environmental features, allowing users to explore underlying processes potentially contributing to the observed patterns and helping facilitate effective conservation management strategies.
| S-EPMC8520269 | biostudies-literature
Project description:UnlabelledIn many genomic studies, one works with genome-position-dependent data, e.g. ChIP-chip or ChIP-Seq scores. Using conventional tools, it can be difficult to get a good feel for the data, especially the distribution of features. This article argues that the so-called Hilbert curve visualization can complement genome browsers and help to get further insights into the structure of one's data. This is demonstrated with examples from different use cases. An open-source application, called HilbertVis, is presented that allows the user to produce and interactively explore such plots.Availabilityhttp://www.ebi.ac.uk/huber-srv/hilbert/.
| S-EPMC2677744 | biostudies-literature
Project description:BackgroundLarge and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alone are insufficient. Further, we establish an iterative process that uses multiple multivariate approaches, communication between teams, and visualization for other large-scale projects to follow.MethodsWe illustrate this process with preliminary neuropsychology and gait data for the vascular cognitive impairment cohort from the Ontario Neurodegenerative Disease Research Initiative, a multi-cohort observational study that aims to characterize biomarkers within and between five neurodegenerative diseases. Each dataset was evaluated four times: with and without covariate adjustment using two validated multivariate methods - Minimum Covariance Determinant (MCD) and Candès' Robust Principal Component Analysis (RPCA) - and results were assessed in relation to two univariate methods. Outlying participants identified by multiple multivariate analyses were compiled and communicated to the data teams for verification.ResultsOf 161 and 148 participants in the neuropsychology and gait data, 44 and 43 were flagged by one or both multivariate methods and errors were identified for 8 and 5 participants, respectively. MCD identified all participants with errors, while RPCA identified 6/8 and 3/5 for the neuropsychology and gait data, respectively. Both outperformed univariate approaches. Adjusting for covariates had a minor effect on the participants identified as outliers, though did affect error detection.ConclusionsManual QC procedures are insufficient for large studies as many errors remain undetected. In these data, the MCD outperforms the RPCA for identifying errors, and both are more successful than univariate approaches. Therefore, data-driven multivariate outlier techniques are essential tools for QC as data become more complex.
| S-EPMC6521365 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2013-09-05 | GSE22850 | GEO
Project description:The use of multivariate analysis techniques, such as principal component analysis-inverse least-squares (PCA-ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA-ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA-ILS, which relies on explicit user definition of component number and profiles, MCR-ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR-ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR-ALS models are characterized. It is shown, through use of previously reported techniques, that MCR-ALS can produce similar results to PCA-ILS and may serve as a useful supplement or replacement to PCA-ILS for signal isolation from FSCV data.
| S-EPMC6470876 | biostudies-literature
Project description:Background and objectiveElectronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible observations are frequent in records of laboratory test results and vital signs. Outlier detection methods can offer low-cost solutions to flagging implausible EHR observations. This article evaluates the utility of a semi-supervised encoding approach (super-encoding) for constructing non-linear exemplar data distributions from EHR observation data and detecting non-conforming observations as outliers.MethodsTwo hypotheses are tested using experimental design and non-parametric hypothesis testing procedures: (1) adding demographic features (e.g., age, gender, race/ethnicity) can increase precision in outlier detection, (2) sampling small subsets of the large EHR data can increase outlier detection by reducing noise-to-signal ratio. The experiments involved applying 492 encoder configurations (involving different input features, architectures, sampling ratios, and error margins) to a set of 30 datasets EHR observations including laboratory tests and vital sign records extracted from the Research Patient Data Registry (RPDR) from Partners HealthCare.ResultsResults are obtained from (30 × 492) 14,760 encoders. The semi-supervised encoding approach (super-encoding) outperformed conventional autoencoders in outlier detection. Adding age of the patient at the observation (encounter) to the baseline encoder that only included observation value as the input feature slightly improved outlier detection. Top-nine performing encoders are introduced. The best outlier detection performance was from a semi-supervised encoder, with observation value as the single feature and a single hidden layer, built on one percent of the data and one percent reconstruction error. At least one encoder configurations had a Youden's J index higher than 0.9999 for all 30 observation types.ConclusionGiven the multiplicity of distributions for a single observation in EHR data (i.e., same observation represented with different names or units), as well as non-linearity of human observations, encoding offers huge promises for outlier detection in large-scale data repositories. https://github.com/hestiri/superencoder.
| S-EPMC10111961 | biostudies-literature
Project description:A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.Supplementary informationThe online version contains supplementary material available at 10.1007/s00477-021-02021-0.
| S-EPMC8053745 | biostudies-literature
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.
| S-EPMC5936001 | biostudies-literature