IMIX_ind {IMIX}

IMIX-ind

Description

Fitting an independent mixture model with restrictions on mean and variance. Input of summary statistics z scores or p values of two or three data types.

Usage

IMIX_ind(
  data_input,
  data_type = c("p", "z"),
  mu,
  sigma,
  p,
  tol = 1e-06,
  maxiter = 1000,
  seed = 10,
  verbose = FALSE
)

Arguments

data_input

An n x d data frame or matrix of the summary statistics z score or p value, n is the nubmer of genes, d is the number of data types. Each row is a gene, each column is a data type.

data_type

Whether the input data is the p values or z scores, default is p value

mu

Initial value for the mean of each component of the independent mixture model distribution. A vector of length 2*d, d is number of data types. Needs to be in a special format that corresponds to the initial value of mu, for example, if d=3, needs to be in the format of (null_1,alternative_1,null_2,alternative_2,null_3,alternative_3).

sigma

Initial value for the standard deviation of each component of the independent mixture model distribution. A vector of length 2*d, d is number of data types. Needs to be in a special format that corresponds to the initial value of mu, for example, if d=3, needs to be in the format of (null_1,alternative_1,null_2,alternative_2,null_3,alternative_3).

p

Initial value for the proportion of the distribution in the Gaussian mixture model

tol

The convergence criterion. Convergence is declared when the change in the observed data log-likelihood increases by less than epsilon.

maxiter

The maximum number of iteration, default is 1000

seed

set.seed, default is 10

verbose

Whether to print the full log-likelihood for each iteration, default is FALSE

Value

A list of the results of IMIX-ind

posterior prob

Posterior probability matrix of each gene for each component

Full LogLik all

Full log-likelihood of each iteration

Full MaxLogLik final

The final log-likelihood of the converged model

iterations

Number of iterations run

pi

Estimated proportion of each component, sum to 1

mu

Estimated mean for the null and alternative of each data type: for two data types (mu10,mu11,mu20,mu21), three data types (mu10,mu11,mu20,mu21,mu30,mu31), mui0 is the null for data type i, mui1 is the alternative for data type i.

sigma

Estimated standard deviation for the null and alternative of each data type: for two data types (sigma10,sigma11,sigma20,sigma21), three data types (sigma10,sigma11,sigma20,sigma21,sigma30,sigma31), sigmai0 is the null for data type i, sigmai1 is the alternative for data type i.

References

Ziqiao Wang and Peng Wei. 2020. “IMIX: a multivariate mixture model approach to association analysis through multi-omics data integration.” Bioinformatics. <doi:10.1093/bioinformatics/btaa1001>.