You have to confirm everything it will do before it starts. It tells you exactly what it will do before it does it too. It is a careful script it can be run even if you have stuff installed in the preferred prefix already. This script installs Homebrew to its preferred prefix ( /usr/local for macOS Intel, /opt/homebrew for Apple Silicon and /home/linuxbrew/.linuxbrew for Linux) so that you don’t need sudo when you brew install.
#Install asreml r in mac install
#!/usr/bin/env Rscript #args = commandArgs(trailingOnly=TRUE) # test if there is at least one argument: if not, return an error #if (length(args)=0) require ( tidyverse ) require ( rlang ) # source("/home/pubjuenger/Alice/Github/Functions_ggplot-theme-adjustments_.R") # theme_set(theme_oeco) # source("/home/alice/CDBN/bin/R_Functions/Functions_VCF_to_GAPIT.R") #library(multtest) #library(gplots) #library(LDheatmap) library ( genetics ) library ( ape ) library ( EMMREML ) library ( compiler ) #this library is already installed in R library ( "scatterplot3d" ) source ( "/home/alice/CDBN/bin/R_Functions/GAPIT_functions.R" ) source ( "/home/alice/CDBN/bin/R_Functions/EMMA_functions.R" ) # Use GAPIT to run GWAS on the BLUPs for each phenotype #Step 1: Import and format phenotypic data create phenotype directory projectdir <- "/home/alice/Github/CDBNgenomics/data-raw/ASReml/GAPIT-2-PCs/" setwd ( projectdir ) gapit_phe_asreml <- read.table ( file = "/home/alice/Github/CDBNgenomics/data-raw/ASReml/ASReml_coefficients_13_GAPIT_.txt", head = TRUE ) # Run GAPIT on this LbY myGAPIT <- GAPIT ( Y = gapit_phe_asreml, PCA.total = 2, model = "CMLM", file.GD = "Numerical_format_GD_CDBN_001_359_pedigree_fillin_chr", = "txt", file.GM = "Numerical_format_GM_CDBN_001_359_pedigree_fillin_chr", = "txt", om = 1, file.to = 11, file.path = "/home/alice/Github/CDBNgenomics/data-raw/GAPIT_Numerical_format_files/" ) # other options for GAPIT: # lection = TRUE # SNP.fraction = 0.Instructions for a supported install of Homebrew are on the homepage. txdb: Annotation genomic ranges for Phaseolus vulgaris.scale_cov: Scale each covariance matrix in list Ulist by a scalar in.round_xy: Return a dataframe binned into 2-d bins by some x and y.round2: Return a number rounded to some number of digits.reorder_cormat: Reorder correlation matrix.Pv_kegg: Phaseolus vulgaris kegg information.Pv_GO: Phaseolus vulgaris gene ontology information.metadata: Metadata for 327 entries in the CDBN panel.mash_standard_run: A standard run of mashr.mash_plot_Ulist: ggplot of specific covariance matrix patterns.mash_plot_sig_by_condition: Significant SNPs per number of conditions.mash_plot_pairwise_sharing: Create a ggplot of pairwise sharing of mash effects.mash_plot_manhattan_by_condition: Manhattan plot in ggplot colored by significant conditions.mash_plot_effects: ggplot of single mash effect.mash_plot_covar: ggplot of covariance matrix masses.load_g2m_df: Read in the random and the strong datasets.get_U_by_mass: Get the positions of objects in a mash object Ulist that are.get_top_effects_log10p: Step One of bigsnp2mashr.get_SVD: Wrapper for the snp_autoSVD function for the CDBN.get_significant_results: From a mash result, get effects that are significant in at.get_results_in_folder: Identify phenotype names from bigsnpr results in a folder.get_qqplot: Create a quantile-quantile plot with ggplot2.get_pairwise_sharing: Compute the proportion of (significant) signals shared by.get_n_significant_conditions: Count number of conditions each effect is significant in.get_log10bf: Return the Bayes Factor for each effect.get_lambda_GC: Return lambda_GC for different numbers of PCs for GWAS on any.get_lambdagc: Find lambda_GC value for non-NA p-values.get_kinship: Find a kinship matrix using the van Raden method.get_G圎: Get data frames of types of G圎 from a mash object.get_estimated_pi: Return the estimated mixture proportions.get_date_filename: Get current date-time in a filename-appropriate format.get_colnames: Get column names from a mash object.get_best_PC_df: Return best number of PCs in terms of lambda_GC for the CDBN.
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