The Generalized Pairs Plot

Emerson, John W.; Green, Walton A.; Schloerke, Barret; Crowley, Jason; Cook, Dianne; Hofmann, Heike; Wickham, Hadley

doi:10.1080/10618600.2012.694762

Cited by 109 publications

(74 citation statements)

References 17 publications

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“…In addition, the physiological states of the individuals varied with each cohort. Pairwise relationships between the variables age, BMI, gender, and batch can be visualized using a generalised pairs plot 37 which allows for the simultaneous inspection of both categorical and quantitative information in the data. Figure 4 (b) shows that the individuals whose serum samples were run in batches 3, 6, and 7 have non-overlapping age ranges, and that the BMI is higher in the individuals whose serum samples were run in batch 2.…”

Section: Applicationsmentioning

confidence: 99%

“…Plots showing (a) the first three principal components and (b) generalised pairs plot 37 of the variables age, batch, gender and BMI (The diagonal panels show the marginal distribution of each variable, and off-diagonal panels display pairwise relationships between the quantitative (age, BMI) and categorical (gender, batch). Scattter plots, boxplots, and mosaic plots are used to represent respectively, the relationship between two quantitative variables, between a categorical and a quantitative variable, and between two categorical variables.…”

Section: Figurementioning

confidence: 99%

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Statistical Methods for Handling Unwanted Variation in Metabolomics Data

et al. 2015

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Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognised need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely-used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available online.

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Section: Applicationsmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Statistical Methods for Handling Unwanted Variation in Metabolomics Data

et al. 2015

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“…Generalized pairs plots (GPLOM'S) extend the concept of scatter plot matrices by the pairwise depiction of heterogeneous data using type-combination-dependent visualizations [10,19]. Dai et al [9] incorporate a GPLOM-like visualization using choropleth maps mapping spatial data, such as mortality rates together with scatter plots augmented with Pearsson's r values.…”

Section: Prior and Related Workmentioning

confidence: 99%

3D Regression Heat Map Analysis of Population Study Data

Klemm

Lawonn

Glaser

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.

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“…email is a binary factor with values yes and no, and a scatterplot is not ideal to visualize a discrete variable. For such variables, the gpairs, or Generalized Pair Plots, package [41] provides a function called gpairs() that produces a scatterplot matrix that includes better visualizations for both discrete and continuous variables. For example, if we want to look more closely at the relationship between email and online.visits, online.trans and online.spend, we can use gpairs() as follows: Unfortunately gpairs() does not accept formula input, so we select the columns to include by number.…”

Section: Scatterplotmatrix()mentioning

confidence: 99%