Web: GitHub, (wiki)

Three modules ceuadmin/peer/[micromamba|full|1.3] are described below.

1. micromamba

This enables many programs including micromamba, R package to be avaiable, e.g.,

module load ceuadmin/peer/micromamba
micromamba repoquery depends r-peer
micromamba --help
python --version
R --version

The first micromama command gives,

 Name      Version Build            Channel     Subdir
 libgcc-ng 13.2.0  hc881cc4_6       conda-forge linux-64
 r-base    3.4.1   h4fe35fd_8       conda-forge linux-64
 r-peer    1.3     r341h470a237_1   bioconda    linux-64

From the R seesion, we can use library(peer).

2. full

This is activated with module load ceuadmin/peer/full, which is set up as follows,

git clone PMBio
cd PMBio
mkdir build && cd build
export dest=/usr/local/Cluster-Apps/ceuadmin/peer/full
module load cmake/2.8 R/3.4 python/2.7
sed -i 's|/usr/local/Cluster-Apps/python/2.7.5|/usr/local/Cluster-Apps/ceuadmin/peer/full|' python/cmake_install.cmake
make install
mkdir -p ${dest}/lib/R
export R_LIBS=${dest}/lib/R
cd R
R CMD INSTALL peer -l ${dest}/lib/R
## cran/ version
cd ../../cran
R CMD INSTALL peer -l ${dest}/lib/R

Note that R/3.4 (due to /usr/local/software/master/R/3.4/bin/python points to /usr/local/software/master/R/3.4/bin/python3.7) has to be loaded before python/2.7`.

The peertool, R and python packages are accessible, e.g.,

cd examples
sed 's|./peertool|peertool|' | bash
Rscript -e 'install.packages("qtl")'
R --no-save < r_demo.R

The output are briefly described here,

  1. The standalone tool produces files in 11 directories with prefix peer_out*.
  2. Later, qtl is installed and r_demo.R executed which generates r_demo_covs.pdf, r_demo_nk.pdf, and r_demo.pdf.
  3. The Python counterpart gives demo_simple.pdf, demo_covs.pdf, and demo_factors.pdf.

The versioning information of the standalone application is checked with

$CEUADMIN/peer/full/bin/peertool --version

/usr/local/Cluster-Apps/ceuadmin/peer/full/bin/peertool  version: 1.0

The Python counterpart can also be verified with python,

Python 2.7.18 (default, Apr 24 2020, 00:37:06)
[GCC 9.3.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pylab
>>> import scipy
>>> import peer
>>> peer
<module 'peer' from '/usr/local/Cluster-Apps/ceuadmin/peer/full/lib/python2.7/site-packages/'>

3. 1.3

It is called with module load ceuadmin/peer/1.3, a Miniconda/2 installation which follows (mamba is too slow to get going) and

As shall be seen, really Miniconda is a platform to distribute the Linux binary and R package (No python package).

module load miniconda/2
export mypath=/rds/project/jmmh2/rds-jmmh2-public_databases/software/peer/1.3
conda create --prefix=${mypath} -c conda-forge -c bioconda r-peer
conda init bash
source ~/.bashrc
source activate ${mypath}
conda install peer
conda config --show-sources
# All requested packages already installed:
conda install micromamba
micromamba install peer
#micromamba activate /rds/project/jmmh2/rds-jmmh2-public_databases/software/peer/1.3
#micromamba run -p /rds/project/jmmh2/rds-jmmh2-public_databases/software/peer/1.3 peer
micromamba repoquery search peer
micromamba repoquery search r-peer
micromamba repoquery depends r-peer
mkdir $CEUADMIN/peer
ln -s ${mypath} $CEUADMIN/peer/1.3
module load ceuadmin/peer/1.3
cd examples
Rscript -e 'install.packages("qtl")'
R --no-save < r_demo.R

This is somewhat heavy going, ideally for other R packages to be installed.

Iit turns out peertool ( according to conda-meta/peer-1.3-hdbdd923_0.json) instead of a Python package is installed.

We also have the following information,

peer               bioconda/linux-64::peer-1.3-hdbdd923_0

and peertool --version also gives,

peertool  version: 1.0


We can also check if it works under a module, e.g., ceuadmin/R, which we usually use. The following script is extracted from vigette("OUTRIDER").

ctsFile <- system.file('extdata', 'KremerNBaderSmall.tsv', package='OUTRIDER')
ctsTable <- read.table(ctsFile, check.names=FALSE)
ods <- OUTRIDER::OutriderDataSet(countData=ctsTable)
ods <- OUTRIDER::filterExpression(ods, minCounts=TRUE, filterGenes=TRUE)
peer <- function(ods, maxFactors=NA, maxItr=30)
# PEER implementation
        stop("Please install the 'peer' package from GitHub to use this ",
    # default and recommendation by PEER: min(0.25*n, 100)
    maxFactors <- min(as.integer(0.25*ncol(ods)), 100)
    logCts <- log2(t(t(OUTRIDER::counts(ods)+1)/sizeFactors(ods)))
    model <- PEER()
    PEER_setNmax_iterations(model, maxItr)
    PEER_setNk(model, maxFactors)
    PEER_setPhenoMean(model, logCts)
    PEER_setAdd_mean(model, TRUE)
    peerResiduals <- PEER_getResiduals(model)
    peerMean <- t(t(2^(logCts - peerResiduals)) * sizeFactors(ods))
    normalizationFactors(ods) <- pmax(peerMean, 1E-8)
    metadata(ods)[["PEER_model"]] <- list(
            alpha     = PEER_getAlpha(model),
            residuals = PEER_getResiduals(model),
            W         = PEER_getW(model))
ods <- OUTRIDER::estimateSizeFactors(ods)
ods <- peer(ods)
ods <- OUTRIDER::fit(ods)
ods <- computeZscores::computeZscores(ods, peerResiduals=TRUE)
ods <- OUTRIDER::computePvalues(ods)
ods <- OUTRIDER::plotCountCorHeatmap(ods, normalized=TRUE)

the rather confusing uses of ods seem to illustrate whereabout a peer model works.



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