fitModel: Fit model with proportions and phenotypes.

In ziyili20/TOAST: Tools for the analysis of heterogeneous tissues

Fit model with proportions and phenotypes.

Description

This function receives design matrix from makeDesign() and fits the model including all cell types and phenotypes.

Usage

fitModel(Design_out, Y)

Arguments

Design_out	The output from function makeDesign().
Y	A G*N matrix, G is the number of features, N is the number of subjects; or a SummarizedExperiment object.

Value

Design_out	The input Design_out object.
N	Number of samples from matrix Y.
coefs	Estimated coefficients (beta) in the model.
coefs_var	Estimated variance of the coefficients (beta variance) in the model.
Y	Observation Y matrix.
Ypred	Predicted Y from the fitted model.
all_coefs	The names of all phenotypes.
all_cell_types	The names of all cell types.
MSE	Estimated mean squared error.
model_names	The names of all terms in the fitted model.

Author(s)

Ziyi Li <[e-mail]>

References

Ziyi Li, Zhijin Wu, Peng Jin, Hao Wu. "Dissecting differential signals in high-throughput data from complex tissues."

Examples

N = 300 # simulation a dataset with 300 samples
K = 3 # 3 cell types
P <- 500 # 500 features

### simulate proportion matrix
Prop = matrix(runif(N*K, 10,60), ncol=K)
Prop = sweep(Prop, 1, rowSums(Prop), FUN="/")
colnames(Prop) = c("Neuron", "Astrocyte", "Microglia")
Y <- matrix(rnorm(N*P, N, P), ncol = N)

### simulate phenotype names
design <- data.frame(disease=factor(sample(0:1,
                     size = N,replace=TRUE)),
                     age=round(runif(N, 30,50)),
                     race=factor(sample(1:3, size = N,replace=TRUE)))
Design_out <- makeDesign(design, Prop)

### fit model
fitted_model <- fitModel(Design_out, Y)

ziyili20/TOAST documentation built on Aug. 28, 2022, 11:28 a.m.