0.36.6 and 0.39.2
Full name: Scalable and Accurate Implementation of GEneralized mixed model (SAIGE)
GitHub page: https://github.com/weizhouUMICH/SAIGE.
The following is based on source from GitHub (so with the possibility to git pull),
module load cmake/3.9 gcc/5 module load python/2.7 virtualenv py27 source py27/bin/activate pip install cget git clone https://github.com/weizhouUMICH/SAIGE R CMD INSTALL SAIGE
Now we see
.../SAIGE.so: undefined symbol: sgecon_. One can get away with it by renaming
configure.sav (so avoid repeated downloads) and amend the last
g++ ... -o SAIGE.so with
-L$HPC_WORK/lib64 -llapack and then rerun
R CMD INSTALL SAIGE. After successful installation, we can try
cd SAIGE/extdata; bash cmd.sh.
One of the third party software is
bgenix (BE careful with a buggy
wscript uses Python 2 syntax so it is necessary to stick to python/2.7 explicitly since gcc/5 automatically loads python 3.
cd SAIGE cd thirdParty cd bgen ./waf configure --prefix=$HPC_WORK ./waf ./waf install build/test/unit/test_bgen build/apps/bgenix -g example/example.16bits.bgen -list cd ../../..
For the latest version 0.39.2 which deals with the chromosome X ploidy, the following steps are necessary
R -e "devtools::install_github('leeshawn/MetaSKAT')" R -e "devtools::install_github('leeshawn/SPAtest')" git clone --depth 1 -b 0.39.2 https://github.com/weizhouUMICH/SAIGE R CMD INSTALL SAIGE
which first installs MetaSKAT 0.80 also at CRAN but SPAtest 3.1.2 instead of 3.0.2 from CRAN.
GitHub: https://github.com/saigegit/SAIGE (documentation, https://saigegit.github.io/SAIGE-doc/)
As before it requires Python to be functional; by default this is bundled to anaconda. However, it is possible with a plain Python virtual environment under Python 3.x.
module load gcc/6 git clone https://github.com/saigegit/SAIGE R CMD INSTALL SAIGE
Note that I have already used R 4.2.0 and libreadline 6.x as default; it is possible that R/4.2.0 and/or some readline module are also needed to be loaded. My call to
Information on package ‘SAIGE' Description: Package: SAIGE Type: Package Title: Efficiently controlling for case-control imbalance and sample relatedness in single-variant assoc tests (SAIGE) and controlling for sample relatedness in region-based assoc tests in large cohorts and biobanks (SAIGE-GENE+) Version: 1.0.8 Date: 2022-05-13 Author: Wei Zhou, Zhangchen Zhao, Wenjian Bi, Seunggeun Lee, Cristen Willer Maintainer: SAIGE team <firstname.lastname@example.org> Description: an R package that implements the Scalable and Accurate Implementation of Generalized mixed model that uses the saddlepoint approximation (SPA)(mhof, J. P. , 1961; Kuonen, D. 1999; Dey, R. et.al 2017) and large scale optimization techniques to calibrate case-control ratios in logistic mixed model score tests (Chen, H. et al. 2016) in large PheWAS. It conducts both single-variant association tests and set-based tests for rare variants. License: GPL (>= 2) Imports: Rcpp (>= 1.0.7), RcppParallel, Matrix, data.table, RcppArmadillo (>= 0.10.7.5) LinkingTo: Rcpp, RcppArmadillo (>= 0.10.7.5), RcppParallel, data.table, SPAtest (== 3.1.2), RcppEigen, Matrix, methods, BH, optparse, SKAT, MetaSKAT, qlcMatrix, RhpcBLASctl, RSQLite, dplyr Depends: R (>= 3.5.0) SystemRequirements: GNU make RoxygenNote: 7.1.2 NeedsCompilation: yes Encoding: UTF-8 Packaged: 2021-05-13 EST Built: R 4.2.0; x86_64-pc-linux-gnu; 2022-05-16 14:45:22 UTC; unix Index: SAIGE-package Efficiently controlling for unbalanced case-control ratios and sample relatedness for binary traits in PheWAS by large cohorts SPAGMMATtest Run single variant score tests with SPA based on the logistic mixed model. fitNULLGLMM Fit the null logistic mixed model and estimate the variance ratio by a set of randomly selected variants hello A simple function doing little rcpparma_hello_world Set of functions in example RcppArmadillo package