Focussing on polyqtlR at the moment for first visualisations and adding...

Focussing on polyqtlR at the moment for first visualisations and adding functionality to the R Shiny buttons

test_script.R

 library(polyqtlR)
 data("phased_maplist.4x", "SNP_dosages.4x", "Phenotypes_4x")
 
+# IBD estimation
 IBD_4x <- estimate_IBD(phased_maplist = phased_maplist.4x,
                        genotypes = SNP_dosages.4x,
                        ploidy = 4,
-                       bivalent_decoding = FALSE,
+                       bivalent_decoding = TRUE,
                        ncores = 2)
 
-??Rcpp_precious_remove
+# haplo visualisation
+vis_hap <- visualiseHaplo(IBD_list = IBD_4x,
+               display_by = "name",
+               linkage_group = 1,
+               select_offspring = colnames(SNP_dosages.4x)[3:11],
+               multiplot = c(3, 3))
+
+# GIC visualisation
+GIC_4x <- estimate_GIC(IBD_list = IBD_4x)
+ 
+# Phenotypes structure
+par(mfrow = c(2,1))
+visualiseGIC(GIC_4x)
+
+dim(Phenotypes_4x)
+sapply(Phenotypes_4x, unique)
+
+#QTL scan
+qtl_LODs.4x <- QTLscan(IBD_list = IBD_4x,
+                       Phenotype.df = Phenotypes_4x,
+                       genotype.ID = "geno",
+                       trait.ID = "pheno",
+                       block = "year")
+
+# visualise QTLs
+plotQTL(LOD_data = qtl_LODs.4x,
+        multiplot = c(1,2), #1 row, 2 columns
+        col = "darkgreen")
+
+# linear plot
+plotLinearQTL(LOD_data = qtl_LODs.4x,
+              col = "dodgerblue")
+
+# sig thresholds with permutation tests
+qtl_LODs.4x <- QTLscan(IBD_list = IBD_4x,
+                       Phenotype.df = Phenotypes_4x,
+                       genotype.ID = "geno",
+                       trait.ID = "pheno",
+                       block = "year",
+                       perm_test = TRUE,
+                       ncores = 2)
+
+plotLinearQTL(LOD_data = qtl_LODs.4x,
+              col = "dodgerblue")
+
+
+# co-factor analysis
+findPeak(qtl_LODs.4x, linkage_group = 1)
+
+qtl_LODs.4x_cofactor <- QTLscan(IBD_list = IBD_4x,
+                                Phenotype.df = Phenotypes_4x,
+                                genotype.ID = "geno",
+                                trait.ID = "pheno",
+                                block = "year",
+                                cofactor_df = data.frame("LG" = 1,
+                                                         "cM" = 12.3),
+                                perm_test = FALSE,
+                                ncores = 2)#nc is the number of cores, defined earlier