Targeted projection pursuit for visualizing gene expression data classifications

Faith, Joe; Mintram, Robert; Angelova, Maia

doi:10.1093/bioinformatics/btl463

Cited by 20 publications

(21 citation statements)

References 11 publications

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“…Here we apply the LiRaM LVQ algorithm to two gene expression data sets which were recently analyzed by Faith, Mintram, and Angelova (2006).…”

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

“…The first set concerns small round blue cell childhood tumors, and we refer to it as SRBCT (Faith et al, 2006). It comprises cDNA microarray expression levels of 50 pre-selected genes in 83 different samples (Khan et al, 2001).…”

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

“…For details of the data sets we refer to Faith et al (2006) and references therein. The authors present a method termed Targeted Projection Pursuit (TPP) and compare it with several existing techniques, including Multi-dimensional Scaling (MDS) (Ewing & Cherry, 2001), VizStruct (Zhang, Zhang, & Ramanathan, 2004), a dendrogram based method (Eisen, Spellman, Brown, & Botstein, 1998), and Projection Pursuit (Lee, Cook, Klinke, & Lumley, 2005).…”

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

“…TPP is demonstrated to outperform most of these methods or to achieve at least comparable performance on the above data sets. The employed data sets as well as source codes of TPP implementations are publicly available (Faith et al, 2006). First, we apply LiRaM LVQ with one prototype per class to the SRBCT data set.…”

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

“…In Faith et al (2006), error free visualizations of the NCI data are obtained by means of TPP in combination with PCA, Projection Pursuit and subsequent LDA or KNN classification. For a visual inspection of the achieved separation we refer to Figs.…”

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

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Limited Rank Matrix Learning, discriminative dimension reduction and visualization

et al. 2012

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a b s t r a c tWe present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently.In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two-or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method.

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“…Here we apply the LiRaM LVQ algorithm to two gene expression data sets which were recently analyzed by Faith, Mintram, and Angelova (2006).…”

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

Section: High Dimensional Gene Expression Datamentioning

confidence: 99%

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Limited Rank Matrix Learning, discriminative dimension reduction and visualization

et al. 2012

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Challenges in model‐based clustering

Melnykov

2013

WIREs Computational Stats

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Model‐based clustering is an increasingly popular area of cluster analysis that relies on probabilistic description of data by means of finite mixture models. Mixture distributions prove to be a powerful technique for modeling heterogeneity in data. In model‐based clustering, each data group is seen as a sample from one or several mixture components. Despite attractive interpretation, model‐based clustering poses many challenges. This paper discusses some of the most important problems a researcher might encounter while applying the model‐based cluster analysis. WIREs Comput Stat 2013, 5:135–148. doi: 10.1002/wics.1248This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical and Graphical Methods of Data Analysis > Density Estimation

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Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases

et al. 2019

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The problem of classifying subjects into risk categories is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of these algorithms is to predict dichotomous responses (e.g. healthy/at risk) based on several features. Similarly to statistical inference models, also ML models are subject to the common problem of class imbalance. Therefore, they are affected by the majority class increasing the false negative rate. In this paper, we built and evaluated eighteen ML models classifying approximately 4300 female participants from the UK Biobank into three categorical risk statuses based on responses for the discretised visceral adipose tissue values from magnetic resonance imaging. We also examined the effect of sampling techniques on classification modelling when dealing with class imbalance. Results showed that the use of sampling techniques had a significant impact. They not only drove an improvement in predicting patients risk status, but also facilitated an increase in the information contained within each variable. Based on domain experts criteria, the three best models for classification were finally identified. These encouraging results will guide further developments of classification models for predicting visceral adipose tissue without the need for a costly scan.

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Targeted projection pursuit for visualizing gene expression data classifications

Abstract: source code, additional diagrams, and original data are available from http://computing.unn.ac.uk/staff/CGJF1/tpp/bioinf.html

Cited by 20 publications

References 11 publications

Limited Rank Matrix Learning, discriminative dimension reduction and visualization

Limited Rank Matrix Learning, discriminative dimension reduction and visualization

Challenges in model‐based clustering

Machine Learning Classification of Females Susceptibility to Visceral Fat Associated Diseases

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