visualneurosciencelab/PlasticityPhenotypes: Creates a plasticity phenotype using features that correlate with PCA basis vectors

Package contains six functions which work towards determining a plasticity phenotype. The first (cum_var) identifies basis vectors that contain a specified amount of cumulative variance in PCA (e.g. PCA function in FactoMineR package). The second (amplitude_plots) plots the amplitudes of each variable about the PCA basis vectors identified using cum_var. The third (feature_matrix) allows the user to create new features, determined by the user based on the output of amplitude_plots. The function then tests whether they are correlated with the basis vectors obtained from the PCA (e.g. PCA function in FactoMineR package). The fourth (plasticity_phenotype) creates a colour-coded matrix of stacked horizontal bars representing the group averages for each feature identified in feature_matrix. The fifth (phenotype_boxplots) creates a series of boxplots to demonstrate expression levels of each feature, color of boxplots taken from plasticity_pheotype. The sixth (bootstrap_phenotype) performs a bootstrap resampling -- with replacement -- for each experimental group across all plasticity features examined in phenotype_boxplots and plasticity_phenotype. This is done to examine which plasticity features across each experimental group are significantly greater than, less than, or non-significantly different from a specified reference group. The results are visualized on a custom colour-coded phenotype. The seventh (ora_phenotype) performs an overrepresentation analysis for each experimental group across all plasticity features examined in phenotype_boxplots and plasticity_phenotype. This is done to examine which plasticity features across each experimental group are overrepresented, underrepresented, or indifferent greater from a specified reference group.

install.packages("remotes")
remotes::install_github("visualneurosciencelab/PlasticityPhenotypes")