In sahirbhatnagar/eclust: Environment Based Clustering for Interpretable Predictive Models in High Dimensional Data

knitr::opts_chunk$set(echo = TRUE)
pacman::p_load(eclust)

Real data analysis functions (r_)

We will use the data(tcgaov) dataset included in this package, which contains a subset of the TCGA mRNA Ovarian serous cystadenocarcinoma data generated using Affymetrix HTHGU133a arrays. See ?tcgaov for details about the data.

In the example below we use the r_cluster_data to create the environment based clusters, and their summaries. We then use the r_prepare_data function to get it into proper form for regression routines such as earth::earth, glmnet::cv.glmnet, and ncvreg::ncvreg.

Extract the relevant data

# load the data
data("tcgaov")
tcgaov[1:5,1:6, with = FALSE]

# use log survival as the response
Y <- log(tcgaov[["OS"]])

# specify the environment variable
E <- tcgaov[["E"]]

# specify the matrix of genes only
genes <- as.matrix(tcgaov[,-c("OS","rn","subtype","E","status"),with = FALSE])

# for this example the training set will be all subjects.
# change `p` argument to create a train and test set.
trainIndex <- drop(caret::createDataPartition(Y, p = 1, list = FALSE, times = 1))
testIndex <- trainIndex

Cluster the data and calculate cluster representations

We cluster the genes using the correlation matrix (specified by cluster_distance = "corr") and the difference of the exposure dependent correlation matrices (specified by eclust_distance = "diffcorr")

cluster_res <- r_cluster_data(data = genes,
                              response = Y,
                              exposure = E,
                              train_index = trainIndex,
                              test_index = testIndex,
                              cluster_distance = "corr",
                              eclust_distance = "diffcorr",
                              measure_distance = "euclidean",
                              clustMethod = "hclust",
                              cutMethod = "dynamic",
                              method = "average",
                              nPC = 1,
                              minimum_cluster_size = 30)

# the number of clusters determined by the similarity matrices specified
# in the cluster_distance and eclust_distance arguments. This will always be larger
# than cluster_res$clustersAll$nclusters which is based on the similarity matrix
# specified in the cluster_distance argument
cluster_res$clustersAddon$nclusters

# the number of clusters determined by the similarity matrices specified
# in the cluster_distance argument only
cluster_res$clustersAll$nclusters

# what's in the cluster_res object
names(cluster_res)

Prepare data for input in any regression routine

Now we use the r_prepare_data function, where we are using the average expression from each cluster as feaand their interaction with E as features in the regression model:

# prepare data for use with earth function
avg_eclust_interaction <- r_prepare_data(
  data = cbind(cluster_res$clustersAddon$averageExpr, 
               Y = Y[trainIndex],
               E = E[trainIndex]),
  response = "Y", exposure = "E")

head(avg_eclust_interaction[["X"]])

Fit a regression model

At this stage, you can decide which regression model to use. Here we choose the MARS model from the earth package, but you may choose regression models from any number of packages (e.g. see the extensive list of models of models available in the caret package).

fit_earth <- earth::earth(x = avg_eclust_interaction[["X"]], 
                          y = avg_eclust_interaction[["Y"]], 
                          pmethod = "backward", 
                          keepxy = TRUE, 
                          degree = 2, 
                          trace = 1, 
                          nk = 1000)

coef(fit_earth)

You can also install the plotmo package to visualise the relationships between the hinge functions and the response using plotmo::plotmo(fit_earth).

Determine the features that have been selected

The u_extract_selected_earth is a utility function in this package to extract the selected predictors from the MARS model:

u_extract_selected_earth(fit_earth)

We that genes in clusters 1, 7 and 9 were selected. We also see that the interaction between the genes in cluster 5 and the environment was selected and has the highest variable importance. We can see the genes involved using the cluster_res$clustersAddonMembership object:

# Genes in cluster 5
cluster_res$clustersAddonMembership[cluster %in% 5]

# variable importance
earth::evimp(fit_earth)

sahirbhatnagar/eclust documentation built on May 29, 2019, 12:58 p.m.