FastMix: A wrapper for FastMix pipeline.

Description Usage Arguments Value Author(s) Examples

Description

A wrapper for the main function ols.eblup.trim() to conduct deconvolution gene expression analysis with matching cell proportions.

Usage

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FastMix(GeneExp, CellProp, Demo, random="all", include.demo=TRUE, ...)

Arguments

GeneExp

'GeneExp' is a m by n dimensional gene expression matrix, where m is the number of genes, and n is the number of subjects.

CellProp

'CellProp' is a n by K dimensional matrix of cell proportions, where K is the number of cell proportions.

random

random is an index vector that specifies which variable(s) requires random effects – by default, random="all", which means that all covariates are paired with a random effect.

Demo

Demo is a n by p dimensional matrix of clinical and demographic variables to be tested, where p is the number of covariates.

include.demo

Whether the demographical covariates should be included as the main effects in the model or not. Default to TRUE.

...

Additional parameters passed to ols.eblup.trim(). It includes the following useful options

  • independent: independent specifies the correlation structure among random effects. The default value is TRUE, which means that all random effects are assumed to be independent.

  • trim: trim is the trimming percentage when accounting for outliers. Default valie is 0.5 (50%).

  • test: the test method for DEGs. "1" is Gaussian mixture model, "2" is Anderson-darling normal test. Default value is "1".

  • robust: robust specifies whether robust covariance estimation is implemented and which method to use: "FALSE" for non-robust estimation; "mcd" for the MCD algorithm of Rousseeuw and Van Driessen; "weighted" for the Reweighted MCD; "donostah" for the Donoho-Stahel projection based estimator; "pairwiseQC" for the orthogonalized quadrant correlation pairwise estimator. All these algorithms come from the R package 'robust'. The default value is robust="FastMix", which is the proposed trimming method.

  • trim.fix: Whether only consider trimmed subjects in fix effect estiamtion. The default value is FALSE.

  • weight_matrix: The pre-specified weight structure among subjects.

Value

fixed.results

the estimated fix effects and their p-values. They are overall effects shared by all genes.

beta.mat

estimated linear coefficients for individual genes.

GeneExp.fitted

fitted gene expressions.

sigma.beta

the estimated covariance matrix of the fixed effects.

VC

variance component estimation. The first column is the one for common random error. The second column is the one for random effects.

cov

???

var.epsilon

the variance of the i.i.d. noise.

var.eblup.mean

the average of the variance of gamma.hat based on the EBLUP estimator. Note that in general, each gamma.hat.i has its own covariance matrix; so var.eblup.mean is provided only as a rough debugging tool.

eta

the chi-square type statsitics used for p-value calculation.

re.pvalue

the overall p-value for detecting outliers in random effects.

re.ind.pvalue

the individual p-value for outlier detection for each random effect.

out_idx

the potential covariates with outliers when robust = "FastMix. It is NULL when robust != "FastMix"

Author(s)

Hao Sun

Examples

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## load the data example
data(dataexample)

## fit the model by default parameters
mod1 <- FastMix(GeneExp, CellProp, Demo)

## some variants: only assign random effects to a subset of covariates, and uses non-robust method to estimate the covariance structure
mod2 <- FastMix(GeneExp, CellProp, Demo, random=c(1,2,10), robust = FALSE)

terrysun0302/FastMix documentation built on June 7, 2019, 4:40 p.m.