EstDim: Estimate dim and dJ by RMT (Random Matrix Theory)

In jinghan1018/tensorICA: tensorICA

Description

Auxiliary function to estimate dimensionality parameters

Usage

EstDim(Data)

Arguments

Data	The array or data-tensor format of input data. In this case, and in our specific applications, the first mode defines the tissue, cell or data-type, the second mode defines the samples and the third mode the features (e.g. CpGs or genes).

Value

dim a vector which contains the number of significant components of each data matrix to search for.

dJ the number of significant components of joint variation across data/tissue types.

References

Teschendorff AE, Han J, Paul D, Virta J, Nordhausen K. Tensorial Blind Source Separation for Improved Analysis of Multi-Omic Data. Genome Biology (2018) 19:76.

Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L.A., Guhr, T., Stanley, H.E. #' Random matrix approach tocross correlations in financial data. Phys Rev E Stat Nonlin Soft Matter Phys (2002) 65(6), 066126

Teschendorff, A.E., Zhuang, J., Widschwendter, M. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics (2011) 27(11), 1496-1505

Examples

data(buccalbloodtensor);
Dim.l <- EstDim(buccalbloodtensor$data);
dim <- Dim.l$dim;
dJ <- Dim.l$dJ;

jinghan1018/tensorICA documentation built on March 23, 2020, 5:26 a.m.