Skip to content
Generates components that capture sources of variability that are shared between two datasets.
R C++
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
R
man
src
.DS_Store
.Rbuildignore
.Rhistory
.gitignore
CONFINED.Rproj
DESCRIPTION
LICENSE
NAMESPACE
README.md

README.md

CONFINED - CCA ON Features for INter-dataset Effect Detection

Using an ultra-fast implementation for canonical correlation analysis (CCA)

CONFINED was developed for the purpose of capturing replicable sources of biological variability in methylation data. These sources include, for example, age, sex, and cell-type composition. Importantly, the variation captured by CONFINED does not include any variability from technical or batch effects.

CONFINED is implemented in R and requires packages Rcpp and RcppArmadillo. Please install them prior to trying to install CONFINED. If you are using a mac and having installation issues, try installing homebrew or xcode then reinstalling Rcpp and RcppArmadillo.

You can simply install CONFINED using devtools:

devtools::install_github("cozygene/CONFINED")

Please see troubleshooting at the bottom for compilation issues.

Usage

As input, CONFINED takes mandatory arguments:

  • Two matrices of size m by n1 and m by n2 (the number of rows is the same but not necessarily the same number of columns), where m > both n1 and n2
  • t - the number of features (methylation sites) to use (a sparsity parameter)
  • k - the number of components to save (can save up to min{n1, n2} components)

The following inputs are optional:

  • saveOP - boolean, save the output (CONFINED components for each dataset and the ranked list of features) or not
  • outfile - the prefix for saving the output files (default is "Xi_CONFINED_components.txt" and "CONFINED_ranked_features.txt")
  • thresh - The threshold for determining the rank of the low-rank approximation of the input matrices in the feature-selection step of the CONFINED algorithm. The default is .95, and if there are no canonical variables with correlation > thresh, the rank is set to 1.

CONFINED returns a list containing two items:

  • X1comps - the k components for dataset1 produced by CONFINED using t features
  • X2comps - the k components for dataset2 produced by CONFINED using t features

Demo

We provide two subsets of whole-blood methylation datasets from Liu et al.[1] and Hannum et al.[2] for an example of CONFINED's usage. Download the demo files here.

First load the CONFINED packages into R:

library(CONFINED)

Then, load the datasets:

dat1<-read.table("demo_data1.txt")
dat2<-read.table("demo_data2.txt")

Run CONFINED, saving the output with prefix "demo":

results<-CONFINED(X1=dat1, X2=dat2, t=3000, k=10, outfile="demo")

CONFINED will then save the files

  • demo_CONFINED_ranked_features.txt - A file containing the list of features as sorted by CONFINED's feature selection step
  • demo_X2_CONFINED_components_t_3000.txt - A file containing an n1 by k matrix of the k CONFINED components for the individuals in the first dataset
  • demo_X1_CONFINED_components_t_3000.txt - A file containing an n1 by k matrix of the k CONFINED components for the individuals in the first dataset

You can also use the components to predict various sources of biological variability. We provide two files of cell-type proportion estimates from the reference-based algorithm of Houseman et al.[3] as an example:

cellests1<-read.table(file = "demo_cellcomp1.txt")
sapply(1:10, function(i) sapply(1:dim(cellests1)[2], function(j) summary(lm(cellests1[,j] ~ results$X1_comps[,1:i]))$r.squared) )

cellests2<-read.table(file = "demo_cellcomp2.txt")
sapply(1:10, function(i) sapply(1:dim(cellests2)[2], function(j) summary(lm(cellests2[,j] ~ results$X2_comps[,1:i]))$r.squared) )

The call to sapply will return a matrix where each entry ij corresponds to the R^2 value of predicting the ith cell-type's proportion using 1:j components. Performance may not be optimal in the case of the demo as we have only provided about 5% of the entire datasets for the purpose of efficiency.

An ultra-fast implementation for canonical correlation analysis (CCA)

If you only wish to use this software for performing quick canonical correlation analysis, it can be accessed from the CONFINED package:

CONFINED::CCA(X,Y)

A and B are the loadings that will project input matrices X and Y into maximally correlated space. U=XA and V=YB are defined as the canonical variables of X and Y, where the columns of X and Y have been centered in order to make the columns of U orthogonal to each other as well as the columns of V orthogonal to each other.

Our CCA algorithm is entirely based on that of R package CCA by Gonzalez and Dejean. We simply translated their code into C++ code using packages from RcppArmadillo, and all credit for the algorithm goes to them.

Troubleshooting

OSX (Mac) may have a problem where "math.h" is not found. This can usually be mitigated by running in the Terminal:

xcode-select --install

or, alternatively on Mojave:

sudo installer -pkg \
/Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg \
-target /

Another common problem on OSX (Mac) is an error concerning "lgfortran" or "quadmath." Below, we list steps suggested by The Coatless Professor. On that website, there are invaluable troubleshooting steps. Here, we will attempt to give the smallest number of required steps to take. Please visit the link for further details.

R >= 3.5.x

Instructions Install the latest version of clang from CRAN here and install gfortran from CRAN here.

Now, return to R and try installing CONFINED using:

devtools::install_github("cozygene/CONFINED")

If that did not work, try the following:

  # Create an R environment file if it doesn't exist to store a modified path
# VARIABLE
if [ ! -e "~/.Renviron" ] ; then
   touch ~/.Renviron
fi

# Add the clang6 binary path to R's local paths
echo 'PATH="/usr/local/clang6/bin:${PATH}"' >> ~/.Renviron

# Establish a symlink of gfortran into /usr/local/bin
sudo ln -s /usr/local/gfortran/bin/gfortran /usr/local/bin/gfortran

If the above does not work and you've upgraded from R 3.0.0-3.3.3, try removing the old gfortran build, then reinstall the latest gfortran build:

# Download installer into working directory
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2

# Remove _files_ associated with the binary
for file in $(tar tfz gfortran-4.8.2-darwin13.tar.bz2); do
sudo rm -f /$file; 
done

# Remove empty _folders_ associated with the binary
for file in $(tar tfz gfortran-4.8.2-darwin13.tar.bz2); do 
sudo rmdir -p /$file; 
done

# Delete the installer
rm -rf gfortran-4.8.2-darwin13.tar.bz2

# Run the above step again
curl -O https://cloud.r-project.org/bin/macosx/tools/gfortran-6.1.pkg
sudo installer -pkg gfortran-6.1.pkg -target /
rm -rf gfortran-6.1.pkg

# Establish a symlink of gfortran into /usr/local/bin
sudo ln -s /usr/local/gfortran/bin/gfortran /usr/local/bin/gfortran

Now, return to R and try installing CONFINED using:

devtools::install_github("cozygene/CONFINED")

If the above steps still were unsuccessful, try installing XCode CLI and reinstalling clang/gfortran. Copy and paste this into your Terminal window:

########### Xcode CLI

# Headless install of Xcode CLI
# Based on a script by Timothy Sutton, MIT licensed 2013 - 2014
# The code used is given at:
# https://github.com/timsutton/osx-vm-templates/blob/ce8df8a7468faa7c5312444ece1b977c1b2f77a4/scripts/xcode-cli-tools.sh#L8-L14

# Check if the Xcode CLI tool directory exists.
# See technical note: https://developer.apple.com/library/content/technotes/tn2339/_index.html#//apple_ref/doc/uid/DTS40014588-CH1-WHAT_IS_THE_COMMAND_LINE_TOOLS_PACKAGE_
# Note: This is not a rigorous check... So, if a user has deleted contents
# inside the folder but left the folder intact, then this will _not_ trigger
# an installation
if [ ! -d "/Library/Developer/CommandLineTools" ]; then

  # Create a temporary file for the header
  touch /tmp/.com.apple.dt.CommandLineTools.installondemand.in-progress

  # Figure out the correct Xcode CLI for the given mac OS
  PROD=$(sudo softwareupdate -l |
    grep "\*.*Command Line" |
    tail -n 1 | awk -F"*" '{print $2}' |
    sed -e 's/^ *//' |
    tr -d '\n')

  # Install Xcode CLI    
  sudo softwareupdate -i "$PROD" --verbose;

  rm -rf /tmp/.com.apple.dt.CommandLineTools.installondemand.in-progress
else
  echo "Xcode CLI is installed..."  
fi

########### clang6

# Download and Install the clang6 binary 
# Download ~440mb -> 2 gb installed
curl -O https://cran.r-project.org/bin/macosx/tools/clang-6.0.0.pkg
sudo installer -pkg clang-6.0.0.pkg -target /

Enter your password, then enter:

rm -rf clang-6.0.0.pkg

# Create an R environment file if it doesn't exist to store a modified path
# VARIABLE
if [ ! -e "~/.Renviron" ] ; then
   touch ~/.Renviron
fi

# Add the clang6 binary path to R's local paths
echo 'PATH="/usr/local/clang6/bin:${PATH}"' >> ~/.Renviron

########### gfortran

# Download and install the gfortran used in R 3.5.0
curl -O https://cloud.r-project.org/bin/macosx/tools/gfortran-6.1.pkg
sudo installer -pkg gfortran-6.1.pkg -target /

Enter your password once more (if prompted), and lastly:

rm -rf gfortran-6.1.pkg

# Establish a symlink of gfortran into /usr/local/bin
sudo ln -s /usr/local/gfortran/bin/gfortran /usr/local/bin/gfortran

If the above does not work and you've upgraded from R 3.0.0-3.3.3, try removing the old gfortran build, then reinstall the latest gfortran build:

# Download installer into working directory
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2

# Remove _files_ associated with the binary
for file in $(tar tfz gfortran-4.8.2-darwin13.tar.bz2); do
sudo rm -f /$file; 
done

# Remove empty _folders_ associated with the binary
for file in $(tar tfz gfortran-4.8.2-darwin13.tar.bz2); do 
sudo rmdir -p /$file; 
done

# Delete the installer
rm -rf gfortran-4.8.2-darwin13.tar.bz2

# Run the above step again
curl -O https://cloud.r-project.org/bin/macosx/tools/gfortran-6.1.pkg
sudo installer -pkg gfortran-6.1.pkg -target /
rm -rf gfortran-6.1.pkg

# Establish a symlink of gfortran into /usr/local/bin
sudo ln -s /usr/local/gfortran/bin/gfortran /usr/local/bin/gfortran

Now, return to R and try installing CONFINED using:

devtools::install_github("cozygene/CONFINED")

R 3.4.x

Instructions The same link from the 3.5.x section will still be of help. You may try installing these tools from The coatless professor.

Now, return to R and try installing CONFINED using:

devtools::install_github("cozygene/CONFINED")

R 3.3.x

Instructions

Detailed instructions are provided by The coatless professor here. Open the terminal and make sure xcode and gcc are installed:

xcode-select --install

Choose "Install" and verify that it was installed:

gcc --version

Now type:

cd /Applications/Utilities
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

Now, return to R and try installing CONFINED using:

devtools::install_github("cozygene/CONFINED")

[1]Yun Liu, Martin J Aryee, Leonid Padyukov, M Daniele Fallin, Espen Hesselberg, Arni Runarsson, Lovisa Reinius, Nathalie Acevedo, Margaret Taub, Marcus Ronninger, Klementy Shchetynsky, Annika Scheynius, Juha Kere, Lars Alfredsson, Lars Klareskog, Tomas J Ekstrom, and Andrew P Feinberg. Epigenome-wide association data implicate dna methylation as an intermediary of genetic risk in rheumatoid arthritis. Nature Biotechnology, 31:142 EP –, 01 2013.
[2]Gregory Hannum, Justin Guinney, Ling Zhao, Li Zhang, Guy Hughes, SriniVas Sadda, Brandy Klotzle, Marina Bibikova, Jian-Bing Fan, Yuan Gao, Rob Deconde, Menzies Chen, Indika Rajapakse, Stephen Friend, Trey Ideker, and Kang Zhang. Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular cell, 49(2):359–367, 01 2013.
[3] Eugene Andres Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit, Heather H. Nelson, John K. Wiencke, and Karl T. Kelsey. Dna methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics, 13(1):86, May 2012.

You can’t perform that action at this time.