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Type:

Package

Title:

Hierarchical GxE Interactions in a Regularized Regression Model

Version:

1.0.2

Date:

2021-11-28

Author:

Natalia Zemlianskaia

Maintainer:

Natalia Zemlianskaia <natasha.zemlianskaia@gmail.com>

Description:

The method focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure for the joint selection of interaction terms in a regularized regression model. For details see Zemlianskaia et al. (2021) <doi:10.48550/arXiv.2103.13510>.

License:

MIT + file LICENSE

Imports:

Rcpp (≥ 1.0.3), Matrix, bigmemory, methods

Depends:

dplyr, R (≥ 3.5)

Suggests:

glmnet, testthat, knitr, rmarkdown, ggplot2

LinkingTo:

Rcpp, RcppEigen, RcppThread, BH, bigmemory

VignetteBuilder:

knitr

NeedsCompilation:

yes

Packaged:

2021-11-28 17:20:02 UTC; nataliazemlianskaia

Repository:

CRAN

Date/Publication:

2021-11-30 07:30:02 UTC

Hierarchical GxE Interactions in a Regularized Regression Model

Description

Author(s)

Natalia Zemlianskaia

Maintainer: Natalia Zemlianskaia <natasha.zemlianskaia@gmail.com>

References

"A Scalable Hierarchical Lasso for Gene-Environment Interactions", Natalia Zemlianskaia, W.James Gauderman, Juan Pablo Lewinger https://arxiv.org/abs/2103.13510

Data Generation

Description

Generates genotypes data matrix G (sample_size by p), vector of environmental measurments E, and an outcome vector Y of size sample_size. Simulates training, validation, and test datasets.

Usage

data.gen(sample_size = 100, p = 20, n_g_non_zero = 15, n_gxe_non_zero = 10, 
         family = "gaussian", mode = "strong_hierarchical", 
         normalize = FALSE, normalize_response = FALSE, 
         seed = 1, pG = 0.2, pE = 0.3,
         n_confounders = NULL)

Arguments

sample_size

sample size of the data

p

total number of main effects

n_g_non_zero

number of non-zero main effects to generate

n_gxe_non_zero

number of non-zero interaction effects to generate

family

"gaussian" for continous outcome Y and "binomial" for binary 0/1 outcome

mode

either "strong_hierarchical", "hierarchical", or "anti_hierarchical". In the strong hierarchical mode the hierarchical structure is maintained (beta_g = 0 then beta_gxe = 0) and also |beta_g| >= |beta_gxe|. In the hierarchical mode the hierarchical structure is maintained, but |beta_G| < |beta_gxe|. In the anti_hierarchical mode the hierarchical structure is violated (beta_g = 0 then beta_gxe != 0).

normalize

TRUE to normalize matrix G and vector E

normalize_response

TRUE to normalize vector Y

pG

genotypes prevalence, value from 0 to 1

pE

environment prevalence, value from 0 to 1

seed

random seed

n_confounders

number of confounders to generate, either NULL or >1

Value

A list of simulated datasets and generating coefficients

G_train, G_valid, G_test

generated genotypes matrices

E_train, E_valid, E_test

generated vectors of environmental values

Y_train, Y_valid, Y_test

generated outcome vectors

C_train, C_valid, C_test

generated confounders matrices

GxE_train, GxE_valid, GxE_test

generated GxE matrix

Beta_G

main effect coefficients vector

Beta_GxE

interaction coefficients vector

beta_0

intercept coefficient value

beta_E

environment coefficient value

Beta_C

confounders coefficient values

index_beta_non_zero, index_beta_gxe_non_zero, index_beta_zero, index_beta_gxe_zero

inner data generation variables

n_g_non_zero

number of non-zero main effects generated

n_gxe_non_zero

number of non-zero interactions generated

n_total_non_zero

total number of non-zero variables

SNR_g

signal-to-noise ratio for the main effects

SNR_gxe

signal-to-noise ratio for the interactions

family, p, sample_size, mode, seed

input simulation parameters

Examples

data = data.gen(sample_size=100, p=100)
G = data$G_train; GxE = data$GxE_train
E = data$E_train; Y = data$Y_train

Get model coefficients

Description

A function to obtain coefficients from the model fit object corresponding to the desired pair of tuning parameters lambda = (lambda_1, lambda_2).

Usage

gesso.coef(fit, lambda)

Arguments

fit

model fit object obtained either by using function gesso.fit or gesso.cv

lambda

a pair of tuning parameters organized in a tibble (ex: lambda = tibble(lambda_1=grid[1], lambda_2=grid[1]))

Value

A list of model coefficients corresponding to lambda values of tuning parameters

beta_0

estimated intercept value

beta_e

estimated environmental coefficient value

beta_g

a vector of estimated main effect coefficients

beta_c

a vector of estimated confounders coefficients

beta_gxe

a vector of estimated interaction coefficients

Examples

data = data.gen()
model = gesso.cv(data$G_train, data$E_train, data$Y_train, grid_size=20, 
        parallel=TRUE, nfolds=3)
gxe_coefficients = gesso.coef(model$fit, model$lambda_min)$beta_gxe                
g_coefficients = gesso.coef(model$fit, model$lambda_min)$beta_g

Get model coefficients with specified number of non-zero interactions

Description

A function to obtain coefficients with target_b_gxe_non_zero specified to control the desired sparsity of interactions in the model.

Usage

gesso.coefnum(cv_model, target_b_gxe_non_zero, less_than = TRUE)

Arguments

cv_model

cross-validated model fit object obtained by using function gesso.cv

target_b_gxe_non_zero

number of non-zero interactions we want to inlcude in the model

less_than

TRUE if we want to control a number of at most non-zero interactions, FALSE if we want to control a number of at least non-zero interactions

Value

A list of model coefficients corresponding to the best model that contains at most or at least target_b_gxe_non_zero non-zero interaction terms.

The target model is selected based on the averaged cross-validation (cv) results: for each pair of parameters lambda=(lambda_1, lambda_2) in the grid and each cv fold we obtain a number of non-zero estimated interaction terms, then average cv results by lambda and choose the tuning parameters corresponding to the minimum average cv loss that have at most or at least target_b_gxe_non_zero non-zero interaction terms. Returned coefficients are obtained by fitting the model on the full data with the selected tuning parameters.

Note that the number of estimated non-zero interactions will only approximately reflect the numbers obtained on cv datasets.

beta_0

estimated intercept value

beta_e

estimated environmental coefficient value

beta_g

a vector of estimated main effect coefficients

beta_gxe

a vector of estimated interaction coefficients

beta_c

a vector of estimated confounders coefficients

Examples

data = data.gen()
model = gesso.cv(data$G_train, data$E_train, data$Y_train)
model_coefficients = gesso.coefnum(model, 5)
gxe_coefficients = model_coefficients$beta_gxe; sum(gxe_coefficients!=0)

Cross-Validation

Description

Performs nfolds-fold cross-validation to tune hyperparmeters lambda_1 and lambda_2 for the gesso model.

Usage

gesso.cv(G, E, Y, C = NULL, normalize = TRUE, normalize_response = FALSE, grid = NULL,
         grid_size = 20, grid_min_ratio = NULL, alpha = NULL, family = "gaussian", 
         type_measure = "loss", fold_ids = NULL, nfolds = 4, 
         parallel = TRUE, seed = 42, tolerance = 1e-3, max_iterations = 5000, 
         min_working_set_size = 100, verbose = TRUE)

Arguments

G

matrix of main effects of size n x p, variables organized by columns

E

vector of environmental measurments

Y

outcome vector. Set family="gaussian" for the continuous outcome and family="binomial" for the binary outcome with 0/1 levels

C

matrix of confounders of size n x m, variables organized by columns

normalize

TRUE to normalize matrix G and vector E

normalize_response

TRUE to normalize vector Y (for family="gaussian")

grid

grid sequence for tuning hyperparameters, we use the same grid for lambda_1 and lambda_2

grid_size

specify grid_size to generate grid automatically. Grid is generated by calculating max_lambda from the data (smallest lambda such that all the coefficients are zero). min_lambda is calculated as a product of max_lambda and grid_min_ratio. The program then generates grid_size values equidistant on the log10 scale from min_lambda to max_lambda

grid_min_ratio

parameter to determine min_lambda (smallest value for the grid of lambdas), default is 0.1 for p > n, 0.01 otherwise

alpha

if NULL independent 2D grid is used for (lambda_1, lambda_2), else 1D grid is used where lambda_2 = alpha * lambda_1, i.e. (lambda_1, alpha * lambda_1)

family

"gaussian" for continuous outcome and "binomial" for binary

type_measure

loss to use for cross-validation. Specity type_measure="loss" for neative log likelihood or type_measure="auc" for AUC (for family="binomial" only)

fold_ids

option to input custom folds assignments

tolerance

tolerance for the dual gap convergence criterion

max_iterations

maximum number of iterations

min_working_set_size

minimum size of the working set

nfolds

number of cross-validation splits

parallel

TRUE to enable parallel cross-validation

seed

set random seed to control random folds assignments

verbose

TRUE to print messages

Value

A list of objects

cv_result

a tibble with cross-validation results: averaged across folds loss and the number of non-zero coefficients for each value of (lambda_1, lambda_2) path. Could be used for custom parameters tuning (ex: select (lambda_1, lambda_2) with a sertain number of non-zero main effects and/or a sertain number of interactions).

mean_loss averaged across folds loss value, vector of size lambda_1*lambda_2
mean_beta_g_nonzero averaged across folds number of non-zero main effects, vector of size lambda_1*lambda_2
mean_beta_gxe_nonzero averaged across folds number of non-zero interactions, vector of size lambda_1*lambda_2
lambda_1 lambda_1 pass, decreasing
lambda_2 lambda_2 pass, oscillating

lambda_min

a tibble of optimal (lambda_1, lambda_2) values, tuning parameter values that give minimum cross-validation loss (mean_loss)

fit

list, return of the function gesso.fit on the full data

grid

vector of values used for hyperparameters tuning

full_cv_result

inner variables

Examples

data = data.gen()
tune_model = gesso.cv(data$G_train, data$E_train, data$Y_train, 
                      grid_size=20, parallel=TRUE, nfolds=3)
gxe_coefficients = gesso.coef(tune_model$fit, tune_model$lambda_min)$beta_gxe        
g_coefficients = gesso.coef(tune_model$fit, tune_model$lambda_min)$beta_g

gesso fit

Description

Fits gesso model over the two dimentional grid of hyperparmeters lambda_1 and lambda_2, returns estimated coefficients for each pair of hyperparameters.

Usage

gesso.fit(G, E, Y, C = NULL, normalize = TRUE, normalize_response = FALSE,
          grid = NULL, grid_size = 20, grid_min_ratio = NULL, 
          alpha = NULL, family = "gaussian", weights = NULL,
          tolerance = 1e-3, max_iterations = 5000, 
          min_working_set_size = 100,
          verbose = FALSE)

Arguments

G

matrix of main effects of size n x p, variables organized by columns

E

vector of environmental measurments

Y

outcome vector. Set family="gaussian" for the continuous outcome and family="binomial" for the binary outcome with 0/1 levels

C

matrix of confounders of size n x m, variables organized by columns

normalize

TRUE to normalize matrix G and vector E

normalize_response

TRUE to normalize vector Y

grid

grid sequence for tuning hyperparameters, we use the same grid for lambda_1 and lambda_2

grid_size

grid_min_ratio

parameter to determine min_lambda (smallest value for the grid of lambdas), default is 0.1 for p > n, 0.01 otherwise

alpha

if NULL independent 2D grid is used for (lambda_1, lambda_2), else 1D grid is used where lambda_2 = alpha * lambda_1, i.e. (lambda_1, alpha * lambda_1)

family

"gaussian" for continuous outcome and "binomial" for binary

tolerance

tolerance for the dual gap convergence criterion

max_iterations

maximum number of iterations

min_working_set_size

minimum size of the working set

weights

inner fitting parameter

verbose

TRUE to print messages

Value

A list of estimated coefficients and other model fit metrics for each pair of hyperparameters (lambda_1, lambda_2)

beta_0

vector of estimated intercept values of size lambda_1*lambda_2

beta_e

vector of estimated environment coefficients of size lambda_1*lambda_2

beta_g

matrix of estimated main effects coefficients organized by rows, size (lambda_1*lambda_2) by p

beta_gxe

matrix of estimated interactions coefficients organized by rows, size (lambda_1*lambda_2) by p

beta_c

matrix of estimated confounders coefficients organized by rows, size (lambda_1*lambda_2) by m, where m is the number of confounders

num_iterations

number of iterations until convergence for each fit

working_set_size

maximum number of variables in the working set for each fit

has_converged

1 if the model converged within given max_iterations, 0 otherwise

objective_value

objective function (loss) value for each fit

beta_g_nonzero

number of estimated non-zero main effects for each fit

beta_gxe_nonzero

number of estimated non-zero interactions for each fit

lambda_1

lambda_1 path values, decreasing

lambda_2

lambda_2 path values, oscillating

grid

vector of values used for hyperparameters tuning

Examples

data = data.gen()
fit = gesso.fit(G=data$G_train, E=data$E_train, Y=data$Y_train, normalize=TRUE)
plot(fit$beta_g_nonzero, pch=19, cex=0.4, 
     ylab="num of non-zero features", xlab="lambdas path")
points(fit$beta_gxe_nonzero, pch=19, cex=0.4, col="red")

Predict new outcome vector

Description

Predict new outcome vector based on the new data and estimated model coefficients.

Usage

gesso.predict(beta_0, beta_e, beta_g, beta_gxe, new_G, new_E, 
                   beta_c=NULL, new_C=NULL, family = "gaussian")

Arguments

beta_0

estimated intercept value

beta_e

estimated environmental coefficient value

beta_g

a vector of estimated main effect coefficients

beta_gxe

a vector of estimated interaction coefficients

new_G

matrix of main effects, variables organized by columns

new_E

vector of environmental measurments

beta_c

a vector of estimated confounders coefficients

new_C

matrix of confounders, variables organized by columns

family

set family="gaussian" for the continuous outcome and family="binomial" for the binary outcome with 0/1 levels

Value

Returns a vector of predicted values

Examples

data = data.gen()
tune_model = gesso.cv(data$G_train, data$E_train, data$Y_train)
coefficients = gesso.coef(tune_model$fit, tune_model$lambda_min)
beta_0 = coefficients$beta_0; beta_e = coefficients$beta_e                   
beta_g = coefficients$beta_g; beta_gxe = coefficients$beta_gxe     

new_G = data$G_test; new_E = data$E_test
new_Y = gesso.predict(beta_0, beta_e, beta_g, beta_gxe, new_G, new_E)
cor(new_Y, data$Y_test)^2

Selection metrics

Description

Calculates principal selection metrics for the binary zero/non-zero classification problem (sensitivity, specificity, precision, auc).

Usage

selection.metrics(true_b_g, true_b_gxe, estimated_b_g, estimated_b_gxe)

Arguments

true_b_g

vector of true main effect coefficients

true_b_gxe

vector of true interaction coefficients

estimated_b_g

vector of estimated main effect coefficients

estimated_b_gxe

vector of estimated interaction coefficients

Value

A list of principal selection metrics

b_g_non_zero

number of non-zero main effects

b_gxe_non_zero

number of non-zero interactions

mse_b_g

mean squared error for estimation of main effects effect sizes

mse_b_gxe

mean squared error for estimation of interactions effect sizes

sensitivity_g

recall of the non-zero main effects

specificity_g

recall of the zero main effects

precision_g

precision with respect to non-zero main effects

sensitivity_gxe

recall of the non-zero interactions

specificity_gxe

recall of the zero interactions

precision_gxe

precision with respect to non-zero interactions

auc_g

area under the curve for zero/non-zero binary classification problem for main effects

auc_gxe

area under the curve for zero/non-zero binary classification problem for interactions

Examples

data = data.gen()
model = gesso.cv(data$G_train, data$E_train, data$Y_train)
gxe_coefficients = gesso.coef(model$fit, model$lambda_min)$beta_gxe                
g_coefficients = gesso.coef(model$fit, model$lambda_min)$beta_g  
selection.metrics(data$Beta_G, data$Beta_GxE, g_coefficients, gxe_coefficients)