mpgex_differential_regr: Predict simple differntial gene expression from differential...

Description Usage Arguments Value See Also Examples

Description

mpgex_differential_regr is a function that wraps all the necessary subroutines for performing predictions on differential gene expression. Initially, it optimizes the parameters of the basis functions so as to learn the methylation profiles for the control case and the treatment case. Then the two learned profiles for each promoter region are subtracted and new coefficients showing the difference between the two profiles are learned using the Basis Linear Model. These coefficients are given as input features for performing linear regression in order to predict/regress the corresponding differential gene expression data.

Usage

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mpgex_differential_regr(formula = NULL, x, y, model_name = "svm",
  w = NULL, basis = NULL, train_ind = NULL, train_perc = 0.7,
  fit_feature = NULL, opt_method = "CG", opt_itnmax = 100,
  is_parallel = TRUE, no_cores = NULL, is_summary = TRUE)

Arguments

formula

An object of class formula needed when calling the lm function for performing linear regression. If NULL, the simple linear regression method is used.

x

The binomial distributed observations. A list containing two lists for control and treatment samples. Each of the two lists has a nested list where each element is an L x 3 dimensional matrix.

y

The gene expression data. A list containing two vectors for control and treatment samples.

model_name

A charcter denoting the regression model.

w

Optional vector of initial parameter / coefficient values.

basis

Optional basis function object, default is polynomial.object

train_ind

Optional vector containing the indices for the train set.

train_perc

Optional parameter for defining the percentage of the dataset to be used for training set, the remaining will be the test set.

fit_feature

Additional feature on how well the profile fits the methylation data.

opt_method

Parameter for defining the method to be used in the optimization procedure, see optim.

opt_itnmax

Optional parameter for defining the max number of iterations of the optimization procedure, see optim.

is_parallel

Logical, indicating if code should be run in parallel.

no_cores

Number of cores to be used, default is max_no_cores - 1.

is_summary

Logical, print the summary statistics.

Value

An mpgex object consisting of the following elements:

See Also

bpr_optim, bpr_likelihood, blm, polynomial.object, rbf.object

Examples

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obs <- list(control = bpr_control_data, treatment = bpr_treatment_data)
y   <- list(control = gex_control_data, treatment = gex_treatment_data)
basis <- rbf.object(M = 3)
set.seed(1234)
out   <- mpgex_differential_regr(x = obs, y = y, basis = basis,
                                 is_parallel = FALSE, opt_itnmax = 5)

andreaskapou/mpgex documentation built on May 12, 2019, 3:33 a.m.