Description Usage Arguments Value Examples
Performing ICA on a dataset and create a list object with results.
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pheno_mx |
Phenotype matrix with diemnsions g x N, where g is the number of genes and N is the number of samples. |
assay_idx |
The assay index to be used to retrieved a single assay from the SummarizedExperiment object. |
k_est |
Number of components to be estimated or method to estimate it. |
var_cutoff |
Percent variance threshold to use when <k_est> is not supplied. |
n_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 |
n_cores |
Number of cores to use when n_runs is larger than 1. |
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. |
max_iter |
Maximum iterations for estimating k for each run. Default value is set to 10. |
random_seed |
Set a specific value for random seed to reproduce the results of ICA. |
similarity_measure |
How to measure the similarity between ICs. If set to "peaks" only gene weights that are greater than 1 sd are used to calculate similarity. |
The following entries will be generated in the output list ica_object
after running the example above.
A
: The IC coefficient matrix, with each row representing coefficients for the corresponding independent component. (standard fastICA()
output)
S
: Matrix of gene weights for each independent component. Each column holds a single component. (standard fastICA()
output)
percent_var
: The percent variance each independent component is explaining.
peaks
: Indicating which gene has a gene weight larger than 2 standard deviations of its component gene weights.
order
: The order of independent components based on the variance that they explain.
X, K, W
: Standard outputs of fastICA()
. X
is the pre-processed data matrix, K
is the pre-whitening matrix projecting the data onto the first n principal components, and 'W' is the estimated unmixing matrix.
Three attributes are set within the list object. "ICAobject" for class
, "ica" for method
and "no" for covar_cor
.
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