90 lines
3.2 KiB
R
90 lines
3.2 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/wrapper.R
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\name{dive_phe2mash}
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\alias{dive_phe2mash}
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\title{Wrapper to run mash given a phenotype data frame}
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\usage{
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dive_phe2mash(
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df,
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snp,
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type = "linear",
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svd = NULL,
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suffix = "",
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outputdir = ".",
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min.phe = 200,
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save.plots = TRUE,
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thr.r2 = 0.2,
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thr.m = c("sum", "max"),
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num.strong = 1000,
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num.random = NA,
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scale.phe = TRUE,
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roll.size = 50,
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U.ed = NA,
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U.hyp = NA
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)
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}
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\arguments{
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\item{df}{Dataframe containing phenotypes for mash where the first column is
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'sample.ID', which should match values in the snp$fam$sample.ID column.}
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\item{snp}{A "bigSNP" object; load with \code{snp_attach()}.}
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\item{type}{Character string, or a character vector the length of the number
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of phenotypes. Type of univarate regression to run for GWAS.
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Options are "linear" or "logistic".}
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\item{svd}{A "big_SVD" object; Optional covariance matrix to use for
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population structure correction.}
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\item{suffix}{Optional character vector to give saved files a unique search string/name.}
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\item{outputdir}{Optional file path to save output files.}
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\item{min.phe}{Integer. Minimum number of individuals phenotyped in order to
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include that phenotype in GWAS. Default is 200. Use lower values with
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caution.}
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\item{save.plots}{Logical. Should Manhattan and QQ-plots be generated and
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saved to the working directory for univariate GWAS? Default is TRUE.}
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\item{thr.r2}{Value between 0 and 1. Threshold of r2 measure of linkage
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disequilibrium. Markers in higher LD than this will be subset using clumping.}
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\item{thr.m}{"sum" or "max". Type of threshold to use to clump values for
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mash inputs. "sum" sums the -log10pvalues for each phenotype and uses
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the maximum of this value as the threshold. "max" uses the maximum
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-log10pvalue for each SNP across all of the univariate GWAS.}
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\item{num.strong}{Integer. Number of SNPs used to derive data-driven covariance
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matrix patterns, using markers with strong effects on phenotypes.}
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\item{num.random}{Integer. Number of SNPs used to derive the correlation structure
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of the null tests, and the mash fit on the null tests.}
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\item{scale.phe}{Logical. Should effects for each phenotype be scaled to fall
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between -1 and 1? Default is TRUE.}
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\item{roll.size}{Integer. Used to create the svd for GWAS.}
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\item{U.ed}{Mash data-driven covariance matrices. Specify these as a list or a path
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to a file saved as an .rds. Creating these can be time-consuming, and
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generating these once and reusing them for multiple mash runs can save time.}
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\item{U.hyp}{Other covariance matrices for mash. Specify these as a list. These
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matrices must have dimensions that match the number of phenotypes where
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univariate GWAS ran successfully.}
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}
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\value{
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A mash object made up of all phenotypes where univariate GWAS ran
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successfully.
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}
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\description{
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Though step-by-step GWAS, preparation of mash inputs, and mash
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allows you the most flexibility and opportunities to check your results
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for errors, once those sanity checks are complete, this function allows
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you to go from a phenotype data.frame of a few phenotypes you want to
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compare to a mash result. Some exception handling has been built into
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this function, but the user should stay cautious and skeptical of any
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results that seem 'too good to be true'.
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}
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