Multivariate pattern analysis - mvpa

Build validated Projection to Latent Structures regression (PLS-R) models through Monte Carlo resampling. Identify important explanatory variables / features, using the Target Projection on the validated PLS-R model. Perform covariate projection to interpret the influence of potential covariates on a response value.

An overview of included tools: - Principal component analysis - Univariate correlation - QQ-plot - Projection to latent structures regression(PLS-R) model generation (using Monte Carlo resampling) - Target Projection / calculation of Target Projection-derived values like selectivity ratio or selectivity fraction - Covariate projection

Installation

You can install mvpa from GitHub using the following lines of R code:

if (!require("devtools", quietly = TRUE)) {
  install.packages("devtools") 
} else {
  print("Devtools has been already installed.")
}
    
devtools::install_github("liningtonlab/mvpa")

How to use the package

Please look up the online documentation (link) that contains an annotated script of how the package might be used in a real-world example.

Quick start

Use a multivariate dataset, perform covariate projection in order to remove covariateinfluence and use the resulting dataframe for PLS-regression analysis and subsequent target projection.

library(mvpa)

# We load the in-built demo dataset
dataset <- HOMA_IR

# Perform confounder projection
result_list <- perform_covariate_projection(dataset,
                                            covariates = c('Age', 'Sex'),
                                            standardize = TRUE)
                                    
# Plot explained variance by confounders
plot_explained_var_by_covariates(result_list,
                                 rel_font_size = 1,
                                 rotate = FALSE)

# Use the covariate-freed dataset and perform PLS-R
new_data <- remove_variables(result_list$residual_variance_df,
                             vars_to_remove = c("Age", "Sex"))
# Enable reproducible Monte-Carlo resampling
set.seed(5)

pls_r_result <- perform_mc_pls_tp(data = new_data,
                                  response = "HOMA_IR",
                                  nr_components = 3,
                                  standardize = FALSE,
                                  mc_resampling = TRUE,
                                  nr_repetitions = 150,
                                  cal_ratio = 0.5,
                                  validation_threshold = 0.5)
                                 
# Free current R session from seed                                 
set.seed(NULL)

# We can plot the RMSEP bar plot to see which selected number of components performed the best
plot_cost_function(pls_r_result,
                   validation_threshold = 0.5)

# A different view that shall help to understand how the ideal number of components has been selected
plot_cost_function_values_distribution(pls_r_result)

# Now we can look at the explained (predictive) versus orthogonal variance per variable
plot_variable_variance_distribution(pls_r_result)

# Using the same data, we can plot the selectivity ratios and selectivity fractions
plot_tp_value(pls_r_result, tp_value_to_plot = "selectivity_ratio")
plot_tp_value(pls_r_result, tp_value_to_plot = "selectivity_fraction")

# In case we want to see the distributions of Selectivity Ratios derived from the repeated sampling,
# we can plot that as well
plot_tp_value_mc(pls_r_result,
               tp_value_to_plot = "selectivity_ratio",
               component = 1,
               confidence_limits = c(0.05, 0.95))

Contact

For feedback, questions or comments please contact Roger G. Linington or Olav M. Kvalheim