Description Usage Arguments Value
Creating a sample covariance matrix based on non-genetic independent components.
1 2 3 4 |
expr_data |
Expression matrix with dimensions g x n. |
snp_data |
Genotype matrix with dimensions s x n. |
return_all |
If TRUE, the full ICA results are returned. If set FALSE only the sample covariance matrix and lower dimensional phenotype matrix are returned. |
cores |
Number of cores to use for genotype association testing, and ICA multi runs (if ica_runs \> 1). |
k |
Number of components to be estimated or method to estimate it. |
var_cutoff |
Percent variance threshold to use when <k_est> is not supplied. |
ica_runs |
Number of times to run ICA. If this is set to a number larger than 1, only ICs that replicate between runs are going to be returned |
scale_pheno |
If set to TRUE the pre-processing step of the data will include a scaling step. This will divide all phenotypes by their standard deviation. |
h_clust_cutoff |
is the cutoff value used in hierarchical clustering. Default is set to 0.3. |
similarity_measure |
How to measure the similarity between ICs. |
threshold |
Bonferroni significance threshold for genotype association testing |
return_pval |
If set to TRUE, p-values for genotype ICA association testing are returned |
N x N covariance structure matrix.
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