Niche construction in evolutionary theory the construction of an academic niche?, bioRxiv, 2017-02-20

AbstractIn recent years, fairly far-reaching claims have been repeatedly made about how niche construction, the modification by organisms of their environment, and that of other organisms, represents a vastly neglected phenomenon in ecological and evolutionary thought. The proponents of this view claim that the niche construction perspective greatly expands the scope of standard evolutionary theory and that niche construction deserves to be treated as a significant evolutionary process in its own right, almost at par with natural selection. Claims have also been advanced about how niche construction theory represents a substantial extension to, and re-orientation of, standard evolutionary theory, which is criticized as being narrowly gene-centric and ignoring the rich complexity and reciprocity of organism-environment interactions. We examine these claims in some detail and show that they do not stand up to scrutiny. We suggest that the manner in which niche construction theory is sought to be pushed in the literature is better viewed as an exercise in academic niche construction whereby, through incessant repetition of largely untenable claims, and the deployment of rhetorically appealing but logically dubious analogies, a receptive climate for a certain sub-discipline is sought to be manufactured within the scientific community. We see this as an unfortunate, but perhaps inevitable, nascent post-truth tendency within science.

biorxiv evolutionary-biology 100-200-users 2017

mixOmics an R package for ‘omics feature selection and multiple data integration, bioRxiv, 2017-02-15

AbstractThe advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently.We introducemixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a system biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latestmixOmicsintegrative frameworks for the multivariate analyses of ‘omics data available from the package.

biorxiv bioinformatics 100-200-users 2017

 

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