Last updated: 2022-03-29

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Analysis of Principal Components

This previoulsy analysis were performed aiming to select the best number of principal components. The phenotypic data were centered using the function scale to remove the effect of trait variance at the principal components analysis. The selection criteria for the number of principal components were variance bigger than one.

suppressMessages(library(tidyverse))
suppressMessages(library(adegenet))
library(reactable)
library(here)
here() starts at /Users/lbd54/Documents/GitHub/HenriqueDGen
BLUPS <- readRDS(here::here("output", "BLUPsDiseaseAgro.rds"))

BLUPS[, -1] <- scale(BLUPS[ , -1], center = T, scale = T)
BLUPS[is.na(BLUPS)] <- 0

Estimation of the Variance acumulated and selection of the number of Principal Components

PCA <- prcomp(BLUPS[,-1])

Perc <- 100*PCA$sdev^2/sum(PCA$sdev^2)

PercAc <- as.vector(rep(NA, times = length(Perc)))
for(i in 1:length(Perc)) {
  PercAc[i] <- sum(Perc[1:i])
  names(PercAc)[i] <- i
}

Table 1. Variance explained by each principal component

Fig 1. Barplot of the Accumulated variances of the principal components for foliar diseases.

This part we prepare the print location for the labels of foliar disease traits

PointPCA1 <- as.data.frame(PCA$x)
ArrowPCA1 <- as.data.frame(PCA$rotation)
LabelsPCA1 <- 5*ArrowPCA1
LabelsPCA1[2, 1] <- c(-1)
LabelsPCA1[3, 1] <- c(0)
LabelsPCA1[5, 1:2] <- c(1.25, -0.45)
LabelsPCA1[6, 1:2] <- c(1.9, -0.9)
LabelsPCA1[7, 1:2] <- c(2.8, -0.1)
LabelsPCA1[9, 1:2] <- c(2.7, -0.55)
LabelsPCA1[10, 1:2] <- c(1.9, -0.1)

Fig 2. Scatterplot of the Principal components 1 and 2 with the correlation arrows of the foliar disease resistance with the principal components.

Table 2. Analise de correlação dos Caracteristicas

Discriminant Analysis of Principal Components

library(adegenet); library(ggplot2)

BLUPS <- readRDS(here::here("output", "BLUPsDiseaseAgro.rds"))
BLUPS[, -1] <- scale(BLUPS[, -1], center = T, scale = T)
BLUPS[is.na(BLUPS)] <- 0
rownames(BLUPS) <- BLUPS$CLONE
BLUPS$CLONE <- NULL

set.seed(1)
DAPCHen <- find.clusters(BLUPS, n.pca = 5, max.n.clust = 20, choose.n.clust = FALSE, criterion = "diffNgroup")
ClassDAPCHen <- DAPCHen$grp

DAPCHenGraph <- dapc(BLUPS, grp = ClassDAPCHen, n.pca = 5, n.da = 2)
saveRDS(DAPCHenGraph, here::here("output", "DAPCAn.rds"))

VarDAPC <- 10*sum(DAPCHenGraph$pca.eig[1:5])*DAPCHenGraph$var*DAPCHenGraph$eig/sum(DAPCHenGraph$eig)

DAPCIndPoint <- data.frame(DAPCHenGraph$ind.coord, grp = DAPCHenGraph$grp)
DAPCGrpEllip <- data.frame(DAPCHenGraph$grp.coord, grp = as.character(1:3))
ArrowDAPC <- as.data.frame(DAPCHenGraph$var.contr)
LabelsDAPC <- data.frame(ArrowDAPC*7)
LabelsDAPC[1, 2] <- c(1.45)
LabelsDAPC[2, 1:2] <- c(0.1, 1.69)
LabelsDAPC[3, 1:2] <- c(0.7, 1.69)
LabelsDAPC[5, 2] <- c(0)
LabelsDAPC[6, 2] <- c(0.4)
LabelsDAPC[9, 2] <- c(0.35)
LabelsDAPC[10, 2] <- c(0.05)

Fig 3. Scatterplot of the first and second linear discriminant function of the discriminant analysis of principal components for cassava foliar diseases, with four clusters

suppressMessages(library(reshape2))
BLUPS$CLONE <- rownames(BLUPS)
BLUPS$Grp <- DAPCHenGraph$grp

BLUPSBoxplot <- melt(BLUPS, variable.name = "Trait", value.name = "Y", id.vars = c("CLONE", "Grp"))

Fig 4. Boxplots of the BLUPS of the accessions grouped by the discriminant analysis of principal components for cassava foliar diseases traits

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sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.6.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] reshape2_1.4.4  here_1.0.1      reactable_0.2.3 adegenet_2.1.5 
 [5] ade4_1.7-18     forcats_0.5.1   stringr_1.4.0   dplyr_1.0.8    
 [9] purrr_0.3.4     readr_2.1.2     tidyr_1.2.0     tibble_3.1.6   
[13] ggplot2_3.3.5   tidyverse_1.3.1

loaded via a namespace (and not attached):
 [1] nlme_3.1-155      fs_1.5.2          lubridate_1.8.0   httr_1.4.2       
 [5] rprojroot_2.0.2   tools_4.1.2       backports_1.4.1   bslib_0.3.1      
 [9] utf8_1.2.2        R6_2.5.1          vegan_2.5-7       DBI_1.1.2        
[13] mgcv_1.8-38       colorspace_2.0-2  permute_0.9-7     withr_2.5.0      
[17] tidyselect_1.1.2  compiler_4.1.2    git2r_0.29.0      cli_3.2.0        
[21] rvest_1.0.2       xml2_1.3.3        labeling_0.4.2    sass_0.4.1       
[25] scales_1.1.1      digest_0.6.29     rmarkdown_2.13    pkgconfig_2.0.3  
[29] htmltools_0.5.2   highr_0.9         dbplyr_2.1.1      fastmap_1.1.0    
[33] htmlwidgets_1.5.4 rlang_1.0.2       readxl_1.3.1      rstudioapi_0.13  
[37] shiny_1.7.1       farver_2.1.0      jquerylib_0.1.4   generics_0.1.2   
[41] jsonlite_1.8.0    crosstalk_1.2.0   magrittr_2.0.2    Matrix_1.4-0     
[45] Rcpp_1.0.8.3      munsell_0.5.0     fansi_1.0.3       ape_5.6-1        
[49] lifecycle_1.0.1   stringi_1.7.6     whisker_0.4       yaml_2.3.5       
[53] MASS_7.3-55       plyr_1.8.6        grid_4.1.2        parallel_4.1.2   
[57] promises_1.2.0.1  crayon_1.5.1      lattice_0.20-45   haven_2.4.3      
[61] splines_4.1.2     hms_1.1.1         knitr_1.38        pillar_1.7.0     
[65] igraph_1.2.11     seqinr_4.2-8      reprex_2.0.1      glue_1.6.2       
[69] evaluate_0.15     modelr_0.1.8      vctrs_0.3.8       tzdb_0.2.0       
[73] httpuv_1.6.5      cellranger_1.1.0  gtable_0.3.0      reactR_0.4.4     
[77] assertthat_0.2.1  xfun_0.30         mime_0.12         xtable_1.8-4     
[81] broom_0.7.12      later_1.3.0       viridisLite_0.4.0 workflowr_1.7.0  
[85] cluster_2.1.2     ellipsis_0.3.2