John T. Ormerod
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View article: The Lasso Distribution: Properties, Sampling Methods, and Applications in Bayesian Lasso Regression
The Lasso Distribution: Properties, Sampling Methods, and Applications in Bayesian Lasso Regression Open
In this paper, we introduce a new probability distribution, the Lasso distribution. We derive several fundamental properties of the distribution, including closed-form expressions for its moments and moment-generating function. Additionall…
View article: Scalable Expectation Propagation for Mixed-Effects Regression
Scalable Expectation Propagation for Mixed-Effects Regression Open
Mixed-effects regression models represent a useful subclass of regression models for grouped data; the introduction of random effects allows for the correlation between observations within each group to be conveniently captured when inferr…
View article: Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too?
Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too? Open
Summary We propose a new class of priors for Bayesian hypothesis testing, which we name ‘cake priors’. These priors circumvent the Jeffreys–Lindley paradox (also called Bartlett's paradox) a problem associated with the use of diffuse prior…
View article: Tractable skew-normal approximations via matching
Tractable skew-normal approximations via matching Open
Many approximate Bayesian inference methods assume a particular parametric form for approximating the posterior distribution. A Gaussian distribution provides a convenient density for such approaches; examples include the Laplace, penalize…
View article: Maternal diabetes independent of BMI is associated with altered accretion of adipose tissue in large for gestational age fetuses
Maternal diabetes independent of BMI is associated with altered accretion of adipose tissue in large for gestational age fetuses Open
Aim To analyse the effects of maternal diabetes mellitus (DM) and body mass Index (BMI) on central and peripheral fat accretion of large for gestational age (LGA) offspring. Methods This retrospective study included LGA fetuses (n = 595) w…
View article: spicyR: spatial analysis of <i>in situ</i> cytometry data in R
spicyR: spatial analysis of <i>in situ</i> cytometry data in R Open
Motivation High parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular archi…
View article: Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era
Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era Open
Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain i…
View article: scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model
scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model Open
Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished t…
View article: An Approximated Collapsed Variational Bayes Approach to Variable Selection in Linear Regression
An Approximated Collapsed Variational Bayes Approach to Variable Selection in Linear Regression Open
In this work, we propose a novel approximated collapsed variational Bayes approach to model selection in linear regression. The approximated collapsed variational Bayes algorithm offers improvements over mean field variational Bayes by mar…
View article: scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model
scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model Open
Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished t…
View article: spicyR: Spatial analysis of <i>in situ</i> cytometry data in R
spicyR: Spatial analysis of <i>in situ</i> cytometry data in R Open
Motivation High parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular archi…
View article: Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation
Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation Open
Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to …
View article: scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets Open
Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multip…
View article: Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data Open
We introduce a new method of performing high dimensional discriminant analysis, which we call multiDA. Starting from multiclass diagonal discriminant analysis classifiers which avoid the problem of high dimensional covariance estimation we…
View article: Diagonal Discriminant Analysis With Feature Selection for High-Dimensional Data
Diagonal Discriminant Analysis With Feature Selection for High-Dimensional Data Open
We introduce a new method of performing high-dimensional discriminant analysis (DA), which we call multiDA. Starting from multiclass diagonal DA classifiers which avoid the problem of high-dimensional covariance estimation we construct a h…
View article: Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation
Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation Open
Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to …
View article: Variational Discriminant Analysis with Variable Selection
Variational Discriminant Analysis with Variable Selection Open
A fast Bayesian method that seamlessly fuses classification and hypothesis testing via discriminant analysis is developed. Building upon the original discriminant analysis classifier, modelling components are added to identify discriminati…
View article: Variational Nonparametric Discriminant Analysis
Variational Nonparametric Discriminant Analysis Open
Variable selection and classification are common objectives in the analysis of high-dimensional data. Most such methods make distributional assumptions that may not be compatible with the diverse families of distributions data can take. A …
View article: scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication
scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication Open
Concerted examination of multiple collections of single cell RNA-Seq (scRNA-Seq) data promises further biological insights that cannot be uncovered with individual datasets. However, such integrative analyses are challenging and require so…
View article: Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data Open
We introduce a new method of performing high dimensional discriminant analysis, which we call multiDA. We achieve this by constructing a hybrid model that seamlessly integrates a multiclass diagonal discriminant analysis model and feature …
View article: DCARS: Differential correlation across ranked samples
DCARS: Differential correlation across ranked samples Open
Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an app…
View article: Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too?
Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too? Open
We introduce a new class of priors for Bayesian hypothesis testing, which we name "cake priors". These priors circumvent Bartlett's paradox (also called the Jeffreys-Lindley paradox); the problem associated with the use of diffuse priors l…
View article: Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models, second edition by Julian J. Faraway, Chapman and Hall/CRC, Boca Raton, 2016. No. of pages: 399. Price: £63.99 (book + eBook); £44.79 (eBook). ISBN 9781498720960
Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models, second edition by Julian J. Faraway, Chapman and Hall/CRC, Boca Raton, 2016. No. of pages: 399. Price: £63.99 (book + eBook); £44.79 (eBook). ISBN 9781498720960 Open
The second edition of book ‘Extending the linear model with R’ by Julian Faraway is an easily readable and relatively thorough (without being theory heavy) sequel of the earlier ‘Linear Models with R’ by the same author. The book itself is…
View article: A variational Bayes approach to variable selection
A variational Bayes approach to variable selection Open
We develop methodology and theory for a mean field variational Bayes approximation to a linear model with a spike and slab prior on the regression coefficients. In particular we show how our method forces a subset of regression coefficient…
View article: A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types
A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types Open
Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalizat…
View article: Differential distribution improves gene selection stability and has competitive classification performance for patient survival
Differential distribution improves gene selection stability and has competitive classification performance for patient survival Open
A consistent difference in average expression level, often referred to as differential expression (DE), has long been used to identify genes useful for classification. However, recent cancer studies have shown that when transcription facto…