README.md

In lingxuez/sLED: A Sparse Leading Eigenvalue Driven (sLED) Test for High-dimensional Matrices

sLED: A two-sample test for high-dimensional covariance matrices

This is the R Code for

Zhu, Lei, Devlin and Roeder (2017) "Testing high-dimensional covariance matrices, with application to detecting schizophrenia risk genes", Annals of Applied Statistics, 11(3):1810-1831. (arxiv)

Pease cite sLED in your publication if it helps your research:

@ARTICLE{zhu2017testing,
    AUTHOR = {Lingxue Zhu and Jing Lei and Bernie Devlin and Kathryn Roeder},
     TITLE = {Testing high-dimensional covariance matrices, with application to detecting schizophrenia risk genes},
   JOURNAL = {Ann. Appl. Statist.},
  FJOURNAL = {Annals of Applied Statistics},
      YEAR = {2017},
    VOLUME = {11},
    NUMBER = {3},
     PAGES = {1810-1831},
}

A short introduction to sLED

Suppose X, Y are p-dimensional random vectors independently coming from two populations. Let D be the differential matrix

D = cov(Y) - cov(X)

The goal for sLED is to test the following hypothesis:

H_0: D = 0 versus H_1: D != 0

and to identify the non-zero entries in D if the null hypothesis is rejected. sLED is more powerful than many existing two-sample testing procedures for high-dimensional covariance matrices (that is, when the dimension of features p is larger than the sample sizes), even when the signal is both weak and sparse.

sLED can also be used to compare other p-by-p relationship matrices, including correlation matrices and weighted adjacency matrices.

Installation

This package can be installed through devtools in R:

install.packages("devtools") ## if not installed
library("devtools")
devtools::install_github("lingxuez/sLED")

Examples

Size of sLED

First, let's try sLED under the null hypothesis. We generate 100 samples from standard Normal distributions with p=100:

n <- 50
p <- 100
set.seed(99)
X <- matrix(rnorm(n*p, mean=0, sd=1), nrow=n, ncol=p)
set.seed(42)
Y <- matrix(rnorm(n*p, mean=0, sd=1), nrow=n, ncol=p)

Now we apply sLED. For illustration, we use 50 permutations here and leave all other arguments as default:

library("sLED")
result <- sLED(X=X, Y=Y, npermute=50)

Let's check the p-value of sLED, which hopefully is not too small (since X and Y are identically distributed):

result$pVal
## [1] 0.86

Power of sLED

Now let's try a more interesting example to show the power of sLED. Let's generate another 50 Gaussian samples with a different covariance structure:

n <- 50
p <- 100

## The first population is still standard normal
set.seed(99)
X <- matrix(rnorm(n*p, mean=0, sd=1), nrow=n, ncol=p)

## For the second population, the first 10 features have different correlation structure
s <- 10
sigma.2 <- diag(p)
sigma.2[1:s, 1:s] <- sigma.2[1:s, 1:s] + 0.2

set.seed(42)
Y2 <- MASS::mvrnorm(n, mu=rep(0, p), Sigma=sigma.2)

Now we run sLED. Note that the changes in covariance matrices occur at 10% of the features, so the ideal sparsity parameter sumabs should be around (usually slightly less than) sqrt(0.1)=0.32. Here, we pick sumabs=0.25, and use 100 permutations:

## for reproducibility, let's also set the seeds for permutation
result <- sLED(X=X, Y=Y2, sumabs.seq=0.25, npermute=100, seeds = c(1:100))
result$pVal
## [1] 0.03

The p-value is near zero. Further more, let's check which features are detected by sLED, that is, have non-zero leverage:

which(result$leverage != 0)
## [1]  1  2  3  4  6  7  9 10 16 26 46 50 52 83

We see that sLED correctly identifies most of the first 10 signaling features. We can also run sLED across a range of sparsity parameters sumabs at once:

## here we let sumabs.seq to be a vector of 3 different sparsity parameters
result <- sLED(X=X, Y=Y2, sumabs.seq=c(0.2, 0.25, 0.3), npermute=100, seeds = c(1:100))

## we can check the 3 p-values, which are all < 0.05
result$pVal
## [1] 0.04 0.03 0.02

## let's also look at which features have non-zero leverage
detected.genes <- apply(result$leverage, 1, function(x){which(x!=0)})
names(detected.genes) <- paste0("sumabs=",result$sumabs.seq)
detected.genes
## $`sumabs=0.2`
## [1]  2  6  7  9 16 26 50 52
## 
## $`sumabs=0.25`
## [1]  1  2  3  4  6  7  9 10 16 26 46 50 52 83
## 
## $`sumabs=0.3`
## [1]  1  2  3  4  6  7  9 10 16 18 19 20 26 35 45 46 50 52 60 63 83

As shown above, the sLED p-value is usually pretty stable for a reasonable range of sparsity parameters. With larger sumabs, the solution becomes denser, i.e. more features will have non-zero leverage.

Sparsity parameter

A key tuning parameter for sLED is the sparsity parameter, sumabs. This corresponds to the parameter c in equation (2.16) in our paper. Larger values of sumabs correspond to denser solutions. Roughly speaking, sumabs^2 provides a loose lowerbound on the proportion of features to be detected. sumabs can take any value between 1/\sqrt{p} and 1. In practice, a smaller value is usually preferred for better interpretability.

Parallelization

The test can get computationally expensive with large number of permutations. For larger data sets, the multi-core version is recommended:

result_multicore <- sLED(X=X, Y=Y, npermute=1000, useMC=TRUE, mc.cores=2)

Please note that this requires the R packages doParallel and parallel.

Simulations

We provide the code to re-produce the simulation results in Section 3 of the paper. Please refer to the R script

simulations/simulate.R

Note that the simulation can take hours to finish. Multi-core parallelization is highly recommended here.

Tests

This package is still under developement, and has only been tested on Mac OS X 10.11.6.

lingxuez/sLED documentation built on May 7, 2019, 2:55 a.m.