Performs adaptive shrinkage of correlation and covariance
matrices using a mixture model prior over the Fisher z-transformation of the
correlations. It allows separate shrinkage intensity for each cell of the
correlation matrix. Can also we flexibly extended to shrinking correlation like
quantities such as cosine similarities in word2vec models and to partial
correlations in conditional graph estimation. For details on methods, refer
to Stephens (2017) "False discovery rates: a new deal",
R package for adaptive correlation and covariance matrix shrinkage.
Copyright (c) 2017-2018, Kushal Dey.
If you find that this R package is useful for your work, please cite the following paper:
A companion package to the ashr package by Matthew Stephens see paper, CorShrink adaptive shrinks correlation between a pair of variables based on the number of pairwise complete observations. CorShrink can be applied to a vector or matrix of pairwise correlations and can also be generalized to quantities similar in nature to correlations - like partial correlations, rank correlations and cosine simialrities from word2vec model. CorShrink when applied to a data matrix, is able to learn an individual shrinkage intensity for a pair of variables from the number of missing observations between each such pair - which allows the method to handle large scale missing observations (a demo of which is presented in the example below).
The instructions for installing the package are as follows.
For CRAN version:
For the development version:
library(devtools) install_github("kkdey/CorShrink", build_vignettes = TRUE)
Then load the package with:
A demo example usage of CorShrink is given below. For detailed examples and methods, check here.
We first load an example data matrix of gene expression for a specific gene in a tissue sample drawn from a test individual in the GTEx Project. We note that there are many missing observations in this data matrix, which correspond to tissue samples not contributed by an individual.
data("sample_by_feature_data") sample_by_feature_data[1:5, 1:5] Adipose - Subcutaneous Adipose - Visceral (Omentum) GTEX-111CU 10.472332 10.84006 GTEX-111FC 7.335392 NA GTEX-111VG 9.118889 NA GTEX-111YS 10.806459 11.26113 GTEX-1122O 11.040446 11.71497 Adrenal Gland Artery - Aorta Artery - Coronary GTEX-111CU 2.721234 NA NA GTEX-111FC NA NA NA GTEX-111VG NA NA NA GTEX-111YS 3.454823 1.162059 NA GTEX-1122O 1.522667 1.674467 4.188002
We use CorShrink to estimate the correlation matrix taking account of the missing observations and compare the result with the matrix of pairwise correlations generated from complete observations for each pair of features.
out <- CorShrinkData(sample_by_feature_data, sd_boot = FALSE, image = "both", image.control = list(tl.cex = 0.2))
The above approach uses an asymototic version of CorShrink. Alternatively, one can use a re-sampling or Bootstrapping approach.
out <- CorShrinkData(sample_by_feature_data, sd_boot = TRUE, image = "both", image.control = list(tl.cex = 0.2))
Walk through some more detailed examples in the vignette:
The authors would like to thank the GTEx Consortium, John Blischak, Sarah Urbut, Chiaowen Joyce Hsiao, Peter Carbonetto and all members of the Stephens Lab. For any queries related to the CorShrink package, contact Kushal K. Dey here [email protected]