Description Usage Arguments Value Examples
Fit the Gaussian-like data to LMM and compute the VPC values for one or more features.
1 2 3 4 5 6 7 | fitComputeVPC.lmer(
CountMatrix,
Strains,
PriorWeights = NULL,
test = FALSE,
VPCname = "LMM"
)
|
CountMatrix |
Sequencing count matrix for one or more features. Each row is for one feature, and the columns are for samples. |
Strains |
Strain labels for the samples. |
PriorWeights |
Weights used in the lmer function in the package lme4. It is an optional vector used in the fitting process. |
test |
TRUE or FALSE (default). Test the presence of heritability through examining the random effect variance . |
VPCname |
Name of the VPC result, default = "LMM". |
A list with two objects. The first object is a 1 x G vector indicating the variance partition coefficients (VPC). If the argument test is set to be true, the second object of the list consists of p-values for testing the hypothesis that random effects sigma_a2 = 0; otherwise, the second object is NULL.
1 2 3 4 5 6 7 8 9 10 11 12 | ## Compute VPC for the first two features under linear mixed models for Gaussian-like datasets.
## Provide normalized data and include hypothesis testing on presence of
## heritability:
result.vst <- fitComputeVPC.lmer(simData_vst[1:2,], strains, test = TRUE)
## Extract parameters
vpc.vst <- result.vst[[1]]
## Extract p-values
pval.vst <- result.vst[[2]]
## Visulize the distribution of p-values.
hist(pval.vst, breaks = 30, col = "cyan")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.