Man pages for genMOSSplus
Application of MOSS algorithm to genome-wide association study (GWAS)

ex2plinkConvert example dataset to Plink format
genMOSSplus-packageApplication of MOSS algorithm to dense SNP array data
genos.cleanRemoves badly predicted SNPs by MaCH
genos.clean.batchRemoves badly predicted SNPs by MaCH for all files
get.data.dimsObtains matrix dimensions
get.file.copyCopies files from one directory to another
MOSS.GWASA function implementing the MOSS algorithm for the analysis...
pre0.dir.createGenerate working subdirectory structure
pre1.plink2machConvert Plink to MaCH input format
pre1.plink2mach.batchConvert Plink to MaCH input format for all files
pre2.remove.genosRemove genos with many empty values
pre2.remove.genos.batchRemove genos with many empty values for all files
pre3.call.machCall MaCH imputation with and without Hapmap
pre3.call.mach.batchCall MaCH imputation with and without Hapmap
pre4.combine.case.controlCombine CASE and CONTROL files
pre4.combine.case.control.batchCombine CASE and CONTROL files for all files
pre5.genos2numericCategorize genotype data into 3 levels
pre5.genos2numeric.batchCategorize genotype data into 3 levels for each file
pre6.merge.genosCombine geno files across all chromosomes
pre7.add.conf.varAppend confounding variables
pre7.add.conf.var.unixAppend confounding variables using Linux
pre8.split.train.testSplit dataset into TRAIN and TEST files
pre8.split.train.test.batchSplit dataset into TRAIN and TEST files for all files
run1.mossRuns MOSS regression algorithm
tune1.subsetsImputes dense map of SNPs on chromosome regions with MaCH
genMOSSplus documentation built on May 1, 2019, 10:31 p.m.