eclust (version 0.1.0)

s_pen_separate: Fit Penalized Regression Models on Simulated Data

Description

This function can run penalized regression models on the untransformed design matrix. To be used with simulated data where the 'truth' is known i.e., you know which features are associated with the response. This function was used to produce the simulation results in Bhatnagar et al. 2016. Can run lasso, elasticnet, SCAD or MCP models

Usage

s_pen_separate(x_train, x_test, y_train, y_test, s0, exp_family = c("gaussian", "binomial"), model = c("lasso", "elasticnet", "scad", "mcp"), topgenes = NULL, stability = F, filter = F, include_E = T, include_interaction = F)

Arguments

x_train

ntrain x p matrix of simulated training set where ntrain is the number of training observations and p is total number of predictors. This matrix needs to have named columns representing the feature names or the gene names

x_test

ntest x p matrix of simulated training set where ntest is the number of training observations and p is total number of predictors. This matrix needs to have named columns representing the feature names or the gene names

y_train

numeric vector of length ntrain representing the responses for the training subjects. If continuous then you must set exp_family = "gaussion". For exp_family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class)

y_test

numeric vector of length ntest representing the responses for the test subjects. If continuous then you must set exp_family = "gaussion". For exp_family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class).

s0

chracter vector of the active feature names, i.e., the features in x_train that are truly associated with the response.

exp_family

Response type. See details for y_train argument above.

model

Regression model to be fit on cluster summaries. Default is model="lasso" which corresponds to glmnet mixing parameter alpha=1. model="elasticnet" corresponds to glmnet mixing parameter alpha=0.5, model="mcp" and model="scad" are the non-convex models from the ncvreg package

topgenes

List of features to keep if filter=TRUE. Default is topgenes = NULL which means all features are kept for the analysis

stability

Should stability measures be calculated. Default is stability=FALSE. See details

filter

Should analysis be run on a subset of features. Default is filter = FALSE

include_E

Should the environment variable be included in the regression analysis. Default is include_E = TRUE

include_interaction

Should interaction effects between the features in x_train and the environment variable be fit. Default is include_interaction=TRUE

Value

This function has two different outputs depending on whether stability = TRUE or stability = FALSEIf stability = TRUE then this function returns a p x 2 data.frame or data.table of regression coefficients without the intercept. The output of this is used for subsequent calculations of stability.If stability = FALSE then returns a vector with the following elements (See Table 3: Measures of Performance in Bhatnagar et al (2016+) for definitions of each measure of performance): for definitions of each measure of performance):

Details

The stability of feature importance is defined as the variability of feature weights under perturbations of the training set, i.e., small modifications in the training set should not lead to considerable changes in the set of important covariates (Toloşi, L., & Lengauer, T. (2011)). A feature selection algorithm produces a weight, a ranking, and a subset of features. In the CLUST and ECLUST methods, we defined a predictor to be non-zero if its corresponding cluster representative weight was non-zero. Using 10-fold cross validation (CV), we evaluated the similarity between two features and their rankings using Pearson and Spearman correlation, respectively. For each CV fold we re-ran the models and took the average Pearson/Spearman correlation of the 10 choose 2 combinations of estimated coefficients vectors. To measure the similarity between two subsets of features we took the average of the Jaccard distance in each fold. A Jaccard distance of 1 indicates perfect agreement between two sets while no agreement will result in a distance of 0.

References

Toloşi, L., & Lengauer, T. (2011). Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics, 27(14), 1986-1994.

Bhatnagar, SR., Yang, Y., Blanchette, M., Bouchard, L., Khundrakpam, B., Evans, A., Greenwood, CMT. (2016+). An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures Preprint

Langfelder, P., Zhang, B., & Horvath, S. (2008). Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics, 24(5), 719-720.

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, http://www.stanford.edu/~hastie/Papers/glmnet.pdf

Breheny, P. and Huang, J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Statist., 5: 232-253.

Examples

## Not run: 
# library(magrittr)
# 
# # simulation parameters
# rho = 0.90; p = 500 ;SNR = 1 ; n = 200; n0 = n1 = 100 ; nActive = p*0.10 ; cluster_distance = "tom";
# Ecluster_distance = "difftom"; rhoOther = 0.6; betaMean = 2;
# alphaMean = 1; betaE = 3; distanceMethod = "euclidean"; clustMethod = "hclust";
# cutMethod = "dynamic"; agglomerationMethod = "average"
# 
# #in this simulation its blocks 3 and 4 that are important
# #leaveOut:  optional specification of modules that should be left out
# #of the simulation, that is their genes will be simulated as unrelated
# #("grey"). This can be useful when simulating several sets, in some which a module
# #is present while in others it is absent.
# d0 <- s_modules(n = n0, p = p, rho = 0, exposed = FALSE,
#                 modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
#                 minCor = 0.01,
#                 maxCor = 1,
#                 corPower = 1,
#                 propNegativeCor = 0.3,
#                 backgroundNoise = 0.5,
#                 signed = FALSE,
#                 leaveOut = 1:4)
# 
# d1 <- s_modules(n = n1, p = p, rho = rho, exposed = TRUE,
#                 modProportions = c(0.15,0.15,0.15,0.15,0.15,0.25),
#                 minCor = 0.4,
#                 maxCor = 1,
#                 corPower = 0.3,
#                 propNegativeCor = 0.3,
#                 backgroundNoise = 0.5,
#                 signed = FALSE)
# 
# truemodule1 <- d1$setLabels
# 
# X <- rbind(d0$datExpr, d1$datExpr) %>%
#   magrittr::set_colnames(paste0("Gene", 1:p)) %>%
#   magrittr::set_rownames(paste0("Subject",1:n))
# 
# betaMainEffect <- vector("double", length = p)
# betaMainInteractions <- vector("double", length = p)
# 
# # the first nActive/2 in the 3rd block are active
# betaMainEffect[which(truemodule1 %in% 3)[1:(nActive/2)]] <- runif(
#   nActive/2, betaMean - 0.1, betaMean + 0.1)
# 
# # the first nActive/2 in the 4th block are active
# betaMainEffect[which(truemodule1 %in% 4)[1:(nActive/2)]] <- runif(
#   nActive/2, betaMean+2 - 0.1, betaMean+2 + 0.1)
# betaMainInteractions[which(betaMainEffect!=0)] <- runif(nActive, alphaMean - 0.1, alphaMean + 0.1)
# beta <- c(betaMainEffect, betaE, betaMainInteractions)
# 
# result <- s_generate_data(p = p, X = X,
#                           beta = beta,
#                           include_interaction = TRUE,
#                           cluster_distance = cluster_distance,
#                           n = n, n0 = n0,
#                           eclust_distance = Ecluster_distance,
#                           signal_to_noise_ratio = SNR,
#                           distance_method = distanceMethod,
#                           cluster_method = clustMethod,
#                           cut_method = cutMethod,
#                           agglomeration_method = agglomerationMethod,
#                           nPC = 1)
# 
# pen_res <- s_pen_separate(x_train = result[["X_train"]],
#                           x_test = result[["X_test"]],
#                           y_train = result[["Y_train"]],
#                           y_test = result[["Y_test"]],
#                           s0 = result[["S0"]],
#                           model = "lasso",
#                           exp_family = "gaussian",
#                           include_interaction = TRUE)
# unlist(pen_res)
# ## End(Not run)