csTest {TOAST} | R Documentation

## Testing differential signals for specified phenotype and cell type(s).

### Description

This function conducts statistical tests for specified phenotype and cell type(s).

### Usage

    csTest(fitted_model, coef = NULL, cell_type = NULL,
           contrast_matrix = NULL, var_shrinkage = TRUE,
           verbose = TRUE, sort = TRUE)

### Arguments

`fitted_model` | The output from fitModel() function.
`coef` | A phenotype name, e.g. "disease", or a vector of contrast terms, e.g. c("disease", "case", "control").
`cell_type` | A cell type name, e.g. "celltype1", or "neuron". If cell_type is NULL or specified as "ALL", compound effect of coef in all cell types will be tested.
`contrast_matrix` | If contrast_matrix is specified, coef and cell_type will be ignored! A matrix (or a vector) to specify contrast, e.g., cmat <- matrix(0, 2, 6); cmat[1,3] <- 1: cmat[2,4] <- 1 is to test whether the 3rd parameter and 4th parameter are zero simultaneously i.e. beta3 = beta4 = 0.
`var_shrinkage` | Whether to apply shrinkage on estimated MSE or not. Applying shrinkage helps remove extremely small variance estimation and stablize statistics.
`verbose` | A boolean parameter. Testing information will be printed if verbose = TRUE.
`sort` | A boolean parameter. The output results will be sorted by p value if sort = TRUE.

### Value

A matrix including the results from testing the phenotype in specified cell type(s).

### Author(s)

Ziyi Li <ziyi.li@emory.edu>

### 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")

    ### 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)))
    Y <- matrix(rnorm(N*P, N, P), ncol = N)

    ### generate design matrix and fit model
    Design_out <- makeDesign(design, Prop)
    fitted_model <- fitModel(Design_out, Y)

    ### check the names of cell types and phenotypes
    fitted_model$all_cell_types
    fitted_model$all_coefs

    ### detect age effect in neuron
    test <- csTest(fitted_model, coef = "age",
    cell_type = "Neuron", contrast_matrix = NULL)

    ## coef can be specified in different ways:
    #### jointly test a phenotype:
    test <- csTest(fitted_model, coef = "age",
    cell_type = "joint", contrast_matrix = NULL)

    #### if I do not specify cell_type
    test <- csTest(fitted_model, coef = "age",
    cell_type = NULL, contrast_matrix = NULL)
    ## this is exactly the same as
    test <- csTest(fitted_model, coef = "age",
    contrast_matrix = NULL)

    #### other examples
    test <- csTest(fitted_model, coef = "race",
    cell_type = "Astrocyte", contrast_matrix = NULL)
    test <- csTest(fitted_model, coef = "age",
    cell_type = "Microglia", contrast_matrix = NULL)

    #### specify contrast levels
    test <- csTest(fitted_model, coef = c("race", 3, 2),
    cell_type = "Neuron", contrast_matrix = NULL)
    #### specify contrast levels in all cell types
    test <- csTest(fitted_model, coef = c("race", 3, 2),
    cell_type = "joint", contrast_matrix = NULL)

    #### csTest can tolerate different ways of specifying contrast level
    #### note race=1 is used as reference when fitting model
    #### we can here specify race=2 as reference
    test <- csTest(fitted_model, coef = c("race", 1, 2),
    cell_type = "Neuron", contrast_matrix = NULL)
    ## get exactly the same results as
    test <- csTest(fitted_model, coef = c("race", 2, 1),
    cell_type = "Neuron", contrast_matrix = NULL)

    #### specify a contrast matrix:
    cmatrix = rep(0,15)
    cmatrix[c(4,5)] = c(1,-1)
    test <- csTest(fitted_model, coef = NULL,
    cell_type = NULL, contrast_matrix = cmatrix)
    #### specific a contrast matrix with two rows:
    cmatrix = matrix(rep(0,30),2,15)
    cmatrix[1,4] = 1
    cmatrix[2,5] = 1
    test <- csTest(fitted_model, coef = NULL,
    contrast_matrix = cmatrix)

[Package TOAST version 1.4.0 Index]