Package {bravo}

FDR using window size

Description

Computes False Discovery Rate using base-pair window as proximity measure.

Usage

FDR_WS(model, truth, mapmat, winsize = 1000)

Arguments

FDR using correlation threshold

Description

Computes False Discovery Rate using SNP correlation as proximity measure.

Usage

FDR_corrected(model, truth, x, threshold = 0.9)

Arguments

FPR using window size

Description

Computes False Positive Rate using base-pair window as proximity measure.

Usage

FPR_WS(model, truth, mapmat, winsize = 1000)

Arguments

FPR using correlation threshold

Description

Computes False Positive Rate using SNP correlation as proximity measure.

Usage

FPR_corrected(model, truth, x, threshold = 0.9)

Arguments

TPR using window size

Description

Computes True Positive Rate using base-pair window as proximity measure.

Usage

TPR_WS(model, truth, mapmat, winsize = 1000)

Arguments

TPR using correlation threshold

Description

Computes True Positive Rate using SNP correlation as proximity measure.

Usage

TPR_corrected(model, truth, x, threshold = 0.9)

Arguments

Run SVEN with Optimal Parameters

Description

Fits SVEN on (x, y) using the supplied tuned parameters.

Usage

basic.sven.model(x, y, params, seed = 2441139)

Arguments

Bayesian Iterated Screening (ultra-high, high or low dimensional).

Description

Perform Bayesian iterated screening in Gaussian regression models

Usage

bits(X, y, lam = 1, w = 0.5, pp = FALSE, max.var = nrow(X), verbose = TRUE)

Arguments

Value

A list with components

References

Wang, R., Dutta, S., Roy, V. (2021) Bayesian iterative screening in ultra-high dimensional settings. https://arxiv.org/abs/2107.10175

Examples

n=50; p=100;
TrueBeta <- c(rep(5,3),rep(0,p-3))

rho <- 0.6
x1 <- matrix(rnorm(n*p), n, p)
X <- sqrt(1-rho)*x1 + sqrt(rho)*rnorm(n)
y <- 0.5 + X %*% TrueBeta + rnorm(n)
res<-bits(X,y, pp=TRUE)
res$model.pp # the vector of screened model
res$postprobs # the log (unnormalized) posterior probabilities corresponding to the model.pp.

BWAS: Bayesian GWAS for a Single Trait

Description

Runs SVEN and UNITE for one trait.

Usage

bwas(x, y, params, bigx, ehits = 20)

Arguments

Estimate SVEN Runtime

Description

Times a single SVEN run on the given SNP matrix using a random phenotype.

Usage

calc.runtime(X)

Arguments

Value

Time taken to run sven() once.

Clean SNP Matrix

Description

Removes duplicate SNPs and filters low MAF variants.

Usage

clean(SNPmat, MAF_threshold = 0.05)

Arguments

Create Parameter Grid

Description

Builds a grid of lambda and w tuning parameters for SVEN.

Usage

create_param_mat(x)

Arguments

Convert numeric matrix to sparse matrix

Description

Reads a numeric genotype file and converts it to a sparse matrix format.

Usage

dense2sparse(file.name, num.genotypes, separator, progress = TRUE)

Arguments

Value

A sparse matrix of class dgCMatix.

Launch the Sparse Converter Shiny App

Description

Opens the Sparse Matrix Converter GUI in your browser.

Usage

dense_to_sparse_converter()

Jaccard Index

Description

Computes Jaccard index between selected and true causal SNPs.

Usage

jcidx(vars, truth)

Arguments

Compute marginal inclusion probabilities from a fitted "sven" object.

Description

This function computes the marginal inclusion probabilities of all variables from a fitted "sven" object.

Usage

mip.sven(object, threshold = 0)

Arguments

Value

The object returned is a data frame if the sven was run with a single matrix, or a list of two data frames if sven was run with a list of two matrices. The first column are the variable names (or numbers if column names of were absent). Only the nonzero marginal inclusion probabilities are stored.

Author(s)

Somak Dutta
Maintainer: Somak Dutta <somakd@iastate.edu>

Examples

n <- 50; p <- 100; nonzero <- 3
trueidx <- 1:3
truebeta <- c(4,5,6)
X <- matrix(rnorm(n*p), n, p) # n x p covariate matrix
y <- 0.5 + X[,trueidx] %*% truebeta + rnorm(n)
res <- sven(X=X, y=y)
res$model.map # the MAP model
mip.sven(res)

Z <- matrix(rnorm(n*p), n, p) # another covariate matrix
y2 = 0.5 + X[,trueidx] %*% truebeta  + Z[,1:2] %*% c(-2,-2) + rnorm(n)
res2 <- sven(X=list(X,Z), y=y2)
mip.sven(res2) # two data frames, one for X and another for Z

Select Optimal Tuning Parameters

Description

Full training step: cleans SNP matrix and tunes SVEN hyperparameters.

Usage

parameter_selection(
  X,
  R2 = 0.5,
  betamax = 1,
  n.cores = max(1, parallel::detectCores() - 1),
  hitsize = "all",
  MAF_threshold = 0.05
)

Arguments

Value

A list containing the original SNP matrix, cleaned SNP matrix, optimal parameters, MAF threshold, expected hit size, and number of cores used. The list is assigned class "svenetics_trained" for use in downstream functions.

Run GWAS for a Single Trait

Description

Runs the full GWAS pipeline for the i-th trait column.

Usage

pipeline_single_trait(svenetics_trained_object, i, hitsize = NULL)

Arguments

Make predictions from a fitted "sven" object.

Description

This function makes point predictions and computes prediction intervals from a fitted "sven" object.

Usage

## S3 method for class 'sven'
predict(
  object,
  newdata,
  model = c("WAM", "MAP"),
  interval = c("none", "MC", "Z"),
  return.draws = FALSE,
  Nsim = 10000,
  level = 0.95,
  alpha = 1 - level,
  ...
)

Arguments

Value

The object returned depends on "interval" argument. If interval = "none", the object is an \code{ncol(newdata)}\times 1 vector of the point predictions; otherwise, the object is an \code{ncol(newdata)}\times 3 matrix with the point predictions in the first column and the lower and upper bounds of prediction intervals in the second and third columns, respectively.

if return.draws is TRUE, a list with the following components is returned:

Author(s)

Dongjin Li and Somak Dutta
Maintainer: Dongjin Li <dongjl@iastate.edu>

References

Li, D., Dutta, S., Roy, V.(2020) Model Based Screening Embedded Bayesian Variable Selection for Ultra-high Dimensional Settings http://arxiv.org/abs/2006.07561

Examples

n = 80; p = 100; nonzero = 5
trueidx <- 1:5
nonzero.value <- c(0.50, 0.75, 1.00, 1.25, 1.50)
TrueBeta = numeric(p)
TrueBeta[trueidx] <- nonzero.value

X <- matrix(rnorm(n*p), n, p)
y <- 0.5 + X %*% TrueBeta + rnorm(n)
res <- sven(X=X, y=y)
newx <- matrix(rnorm(20*p), 20, p)
# predicted values at a new data matrix using MAP model
yhat <- predict(object = res, newdata = newx, model = "MAP", interval = "none")
# 95% Monte Carlo prediction interval using WAM
MC.interval <- predict(object = res, model = "WAM", newdata = newx, interval = "MC", level=0.95)
# 95% Z-prediction interval using MAP model
Z.interval <- predict(object = res, model = "MAP", newdata = newx, interval = "Z", level = 0.95)

Run SVEN Across Parameter Grid

Description

Simulates a phenotype and evaluates all parameter combinations via Jaccard index.

Usage

run_all_params(k, x, R2 = 0.5, nspike = 20, betamax)

Arguments

Selection of variables with embedded screening using Bayesian methods (SVEN) in Gaussian linear models (ultra-high, high or low dimensional).

Description

SVEN is an approach to selecting variables with embedded screening using a Bayesian hierarchical model. It is also a variable selection method in the spirit of the stochastic shotgun search algorithm. However, by embedding a unique model based screening and using fast Cholesky updates, SVEN produces a highly scalable algorithm to explore gigantic model spaces and rapidly identify the regions of high posterior probabilities. It outputs the log (unnormalized) posterior probability of a set of best (highest probability) models. For more details, see Li et al. (2023, https://doi.org/10.1080/10618600.2022.2074428)

Usage

sven(
  X,
  y,
  w = NULL,
  lam = NULL,
  Ntemp = 10,
  Tmax = NULL,
  Miter = 50,
  wam.threshold = 0.5,
  log.eps = -16,
  L = 20,
  verbose = FALSE
)

Arguments

Details

SVEN is developed based on a hierarchical Gaussian linear model with priors placed on the regression coefficients as well as on the model space as follows:

y | X, \beta_0,\beta,\gamma,\sigma^2,w,\lambda \sim N(\beta_01 + X_\gamma\beta_\gamma,\sigma^2I_n)

\beta_i|\beta_0,\gamma,\sigma^2,w,\lambda \stackrel{indep.}{\sim} N(0, \gamma_i\sigma^2/\lambda),~i=1,\ldots,p,

(\beta_0,\sigma^2)|\gamma,w,p \sim p(\beta_0,\sigma^2) \propto 1/\sigma^2

\gamma_i|w,\lambda \stackrel{iid}{\sim} Bernoulli(w)

where X_\gamma is the n \times |\gamma| submatrix of X consisting of those columns of X for which \gamma_i=1 and similarly, \beta_\gamma is the |\gamma| subvector of \beta corresponding to \gamma. Degenerate spike priors on inactive variables and Gaussian slab priors on active covariates makes the posterior probability (up to a normalizing constant) of a model P(\gamma|y) available in explicit form (Li et al., 2020).

The variable selection starts from an empty model and updates the model according to the posterior probability of its neighboring models for some pre-specified number of iterations. In each iteration, the models with small probabilities are screened out in order to quickly identify the regions of high posterior probabilities. A temperature schedule is used to facilitate exploration of models separated by valleys in the posterior probability function, thus mitigate posterior multimodality associated with variable selection models. The default maximum temperature is guided by the asymptotic posterior model selection consistency results in Li et al. (2020).

SVEN provides the maximum a posteriori (MAP) model as well as the weighted average model (WAM). WAM is obtained in the following way: (1) keep the best (highest probability) K distinct models \gamma^{(1)},\ldots,\gamma^{(K)} with

\log P\left(\gamma^{(1)}|y\right) \ge \cdots \ge \log P\left(\gamma^{(K)}|y\right)

where K is chosen so that \log \left\{P\left(\gamma^{(K)}|y\right)/P\left(\gamma^{(1)}|y\right)\right\} > \code{log.eps}; (2) assign the weights

w_i = P(\gamma^{(i)}|y)/\sum_{k=1}^K P(\gamma^{(k)}|y)

to the model \gamma^{(i)}; (3) define the approximate marginal inclusion probabilities for the jth variable as

\hat\pi_j = \sum_{k=1}^K w_k I(\gamma^{(k)}_j = 1).

Then, the WAM is defined as the model containing variables j with \hat\pi_j > \code{wam.threshold}. SVEN also provides all the top K models which are stored in an p \times K sparse matrix, along with their corresponding log (unnormalized) posterior probabilities.

When X is a list with two matrices, say, W and Z, the above method is extended to ncol(W)+ncol(Z) dimensional regression. However, the hyperparameters lam and w are chosen separately for the two matrices, the default values being nrow(W)/ncol(W)^2 and nrow(Z)/ncol(Z)^2 for lam and sqrt(nrow(W))/ncol(W) and sqrt(nrow(Z))/ncol(Z) for w.

The marginal inclusion probabities can be extracted by using the function mip.

Value

A list with components

Author(s)

Dongjin Li, Debarshi Chakraborty, and Somak Dutta
Maintainer: Dongjin Li <liyangxiaobei@gmail.com>

References

Li, D., Dutta, S., and Roy, V. (2023). Model based screening embedded Bayesian variable selection for ultra-high dimensional settings. Journal of Computational and Graphical Statistics, 32(1), 61-73.

See Also

[mip.sven()] for marginal inclusion probabilities, [predict.sven()](via [predict()]) for prediction for .

Examples

n <- 50; p <- 100; nonzero <- 3
trueidx <- 1:3
truebeta <- c(4,5,6)
X <- matrix(rnorm(n*p), n, p) # n x p covariate matrix
y <- 0.5 + X[,trueidx] %*% truebeta + rnorm(n)
res <- sven(X=X, y=y)
res$model.map # the MAP model


Z <- matrix(rnorm(n*p), n, p) # another covariate matrix
y2 = 0.5 + X[,trueidx] %*% truebeta  + Z[,1:2] %*% c(-2,-2) + rnorm(n)
res2 <- sven(X=list(X,Z), y=y2)

Launch the SVENETICS Shiny App

Description

Opens the SVENETICS GUI in your browser.

Usage

svenetics()

Full Multi-Trait GWAS Pipeline

Description

Runs GWAS across all traits and saves results as CSVs.

Usage

svenetics_pipeline(
  svenetics_trained_object,
  traitfile,
  hitsizes = NULL,
  save_dir = "~/SVENETICS_RESULTS"
)

Arguments

Value

Saves the selected SNPs and their MIPs for each trait as separate CSV files in the specified directory. Also returns a list of data frames with the results for each trait.

Tune SVEN Parameters

Description

Runs 100 parallel simulations and returns the optimal (lambda, w) pair.

Usage

tune.sven(x, R2 = 0.5, ehits = 20, betamax = 1, n.cores)

Arguments

Tune SVEN for All Hit Sizes

Description

Tunes SVEN parameters for small, medium, and large expected hit sizes.

Usage

tune.sven.all(x, R2 = 0.5, betamax = 1, n.cores)

Arguments

UNITE: Post-process SVEN Model

Description

Propagates MIPs from SVEN hits to the full SNP matrix via LD.

Usage

unite.sven(x, bigx, basic_sven_object, y, ehits, threshold = 0)

Arguments