appRIVER: Application of RIVER
In RIVER: R package for RIVER (RNA-Informed Variant Effect on Regulation)

Description Usage Arguments Value Warning Author(s) See Also Examples

appRIVER trains RIVER with all instances and computes posterior probabilities of FR for downstream analyses.

1
2
3

appRIVER(dataInput, pseudoc = 50, theta_init = matrix(c(0.99, 0.01, 0.3,
  0.7), nrow = 2), costs = c(100, 10, 1, 0.1, 0.01, 0.001, 1e-04),
  verbose = FALSE)

`dataInput`	An object of ExpressionSet class which contains input data required for all functions in RIVER including genomic features, outlier status, and N2 pairs.
`pseudoc`	Pseudo count.
`theta_init`	Initial values of theta.
`costs`	Candidate penalty parameter values for L2-regularized logistic regression.
`verbose`	Logical option for showing extra information on progress.

A list which contains subject IDs, gene names, posterior probabilities from GAM and RIVER, and estimated parameters from RIVER with used hyperparameters.

To input a vector of candidate penalty values makes glmnet faster than to input a single penalty value

Yungil Kim, ipw012@gmail.com

cv.glmnet, predict, integratedEM, testPosteriors, getData, exprs

1
2
3

dataInput <- getData(filename=system.file("extdata", "simulation_RIVER.gz",
        package = "RIVER"), ZscoreThrd=1.5)
postprobs <- appRIVER(dataInput, verbose=TRUE)