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improved Amatrix/Hmatrix support #148
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -241,6 +241,11 @@ get_relationship_mat <- function(geno_input, ped_file, type = c("Gmatrix", "Amat | |
| ploidy.correction = TRUE) | ||
| G.mat <- round(G.mat,3) #to be easy to invert | ||
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| # Ensure G.mat and Ped.mat have the same sample order | ||
| common_samples <- intersect(rownames(G.mat), rownames(Ped.mat)) | ||
| G.mat <- G.mat[common_samples, common_samples] | ||
| Ped.mat <- Ped.mat[common_samples, common_samples] | ||
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| #Computing H matrix (Martini) - Using the name Geno.mat for consistency | ||
| Geno.mat <- Hmatrix(A=Ped.mat, G=G.mat, | ||
| method="Martini", | ||
|
|
@@ -314,32 +319,44 @@ GBLUP_genomic_prediction <- function(pheno_dat, Geno.mat, cycles, folds, traits, | |
| #Mask phenotypes in testing group | ||
| Pheno_test <- pheno_dat | ||
| Pheno_test[test, traits[trait_idx]] <- NA | ||
| #Kin.blup | ||
| traitpred <- kin.blup(data = Pheno_test, | ||
| geno = colnames(pheno_dat)[1], | ||
| pheno = traits[trait_idx], | ||
| fixed = fixed_cat, | ||
| covariate = fixed_cov, | ||
| K=Geno.mat, | ||
| n.core = cores) | ||
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| # Add error handling for kin.blup | ||
| traitpred <- tryCatch({ | ||
| kin.blup(data = Pheno_test, | ||
| geno = colnames(pheno_dat)[1], # This assumes first column is sample ID | ||
|
||
| pheno = traits[trait_idx], | ||
| fixed = fixed_cat, | ||
| covariate = fixed_cov, | ||
| K = Geno.mat, | ||
| n.core = cores) | ||
| }, error = function(e) { | ||
| cat("Error in kin.blup for trait", traits[trait_idx], ":", e$message, "\n") | ||
| return(NULL) | ||
| }) | ||
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| # Check if kin.blup failed | ||
| if (is.null(traitpred)) { | ||
| cat("kin.blup failed for trait", traits[trait_idx], "- skipping this trait\n") | ||
| next | ||
|
Comment on lines
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+340
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| } | ||
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| #Cor between test values and predicted breeding values | ||
| results[(((r-1)*5)+fold), trait_idx] <- cor(pheno_dat[test, traits[trait_idx]], traitpred$g[test], use = "complete.obs") | ||
| results[(((r-1)*5)+fold), (length(traits)+1)] <- r | ||
| results[(((r-1)*5)+fold), (length(traits)+2)] <- fold | ||
| results[(((r-1)*folds)+fold), trait_idx] <- cor(pheno_dat[test, traits[trait_idx]], traitpred$g[test], use = "complete.obs") | ||
| results[(((r-1)*folds)+fold), (length(traits)+1)] <- r | ||
| results[(((r-1)*folds)+fold), (length(traits)+2)] <- fold | ||
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||
| # Extract GEBVs | ||
| GEBVs_fold[, trait_idx] <- traitpred$g[test] | ||
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||
| # Calculate heritability (*confirm this calculation* - either way will not report to user) | ||
| Vu <- traitpred$Vg | ||
| Ve <- traitpred$Ve | ||
| heritability_scores[(((r-1)*5)+fold), trait_idx] <- Vu / (Vu + Ve) | ||
| heritability_scores[(((r-1)*folds)+fold), trait_idx] <- Vu / (Vu + Ve) | ||
| } | ||
|
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||
| #Add iter and fold information for each trait/result | ||
| heritability_scores[(((r-1)*5)+fold), (length(traits)+1)] <- r | ||
| heritability_scores[(((r-1)*5)+fold), (length(traits)+2)] <- fold | ||
| heritability_scores[(((r-1)*folds)+fold), (length(traits)+1)] <- r | ||
| heritability_scores[(((r-1)*folds)+fold), (length(traits)+2)] <- fold | ||
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| #Add sample, iteration, and fold information to GEBVs_fold | ||
| GEBVs_fold[,"Iter"] = r | ||
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@@ -348,6 +365,12 @@ GBLUP_genomic_prediction <- function(pheno_dat, Geno.mat, cycles, folds, traits, | |
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| # Store GEBVs for this fold | ||
| GEBVs_cycle[[fold]] <- GEBVs_fold | ||
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| # Update progress bar within the fold loop | ||
| if(!is.null(session)) { | ||
| updateProgressBar(session = session, id = "pb_prediction", value = as.numeric(pb_value), | ||
| title = paste0("Iteration ", r, " of ", cycles, " - Fold ", fold, " of ", folds)) | ||
| } | ||
| } | ||
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| # Store GEBVs for this cycle | ||
|
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The sample alignment logic is redundant. Both G.mat (line 237) and Ped.mat (line 233) are already filtered to the same
valid_idsfrom the phenotype file just before this code. Theintersecthere will always return the same samples unless there's a bug in the earlier filtering. Consider adding validation that the samples already match instead of re-filtering, or document why this additional alignment is necessary.