Description Usage Arguments Value Author(s) Examples
A wrapper for the main function ols.eblup.trim()
to conduct deconvolution gene expression analysis with matching cell proportions.
1 
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 

Demo 

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

fixed.results 
the estimated fix effects and their pvalues. 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 
eta 
the chisquare type statsitics used for pvalue calculation. 
re.pvalue 
the overall pvalue for detecting outliers in random effects. 
re.ind.pvalue 
the individual pvalue for outlier detection for each random effect. 
out_idx 
the potential covariates with outliers when robust = "FastMix. It is NULL when robust != "FastMix" 
Hao Sun
1 2 3 4 5 6 7 8  ## 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 nonrobust method to estimate the covariance structure
mod2 < FastMix(GeneExp, CellProp, Demo, random=c(1,2,10), robust = FALSE)

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