R package for adaptive correlation and covariance matrix shrinkage.
Kushal K Dey, Matthew Stephens.
Copyright (c) 2017-2018, Kushal Dey.
All source code and software in this repository are made available under the terms of the GNU General Public License. See the LICENSE file for the full text of the license.
If you find that this R package is useful for your work, please cite the following papers:
Dey, Kushal K and Stephens, Matthew. CorShrink : Empirical Bayes shrinkage estimation of correlations, with applications. 2018. bioRxiv. Cold Spring Harbor Laboratory. 10.1101/368316. https://www.biorxiv.org/content/early/2018/07/24/368316.full.pdf
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:
install.packages("CorShrink")
For the development version:
library(devtools)
install_github("kkdey/CorShrink", build_vignettes = TRUE)
Then load the package with:
library(CorShrink)
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:
vignette("corshrink")If you want to reproduce the analysis from our paper, please check the codes and available data here.
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]