Analise de Componentes Principais e Discriminantes
LucianoRogerio e HenriqueBernardino
2021-11-02
Last updated: 2023-11-30
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Knit directory: HenriqueDGen/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 54268fe | LucianoRogerio | 2023-07-31 | Adding Foliar Retention to final Boxplot |
| html | 54268fe | LucianoRogerio | 2023-07-31 | Adding Foliar Retention to final Boxplot |
| Rmd | fef5cf2 | LucianoRogerio | 2023-07-26 | Adicao Dados 2022 |
| html | fef5cf2 | LucianoRogerio | 2023-07-26 | Adicao Dados 2022 |
| html | 49c6d7c | LucianoRogerio | 2022-07-21 | Update Figures |
| Rmd | 963096a | LucianoRogerio | 2022-04-19 | Update Website |
| html | 963096a | LucianoRogerio | 2022-04-19 | Update Website |
| Rmd | 745d17f | LucianoRogerio | 2022-04-19 | Update Website |
| html | 745d17f | LucianoRogerio | 2022-04-19 | Update Website |
| Rmd | e08b1a6 | LucianoRogerio | 2022-04-05 | Last Analysis |
| html | e08b1a6 | LucianoRogerio | 2022-04-05 | Last Analysis |
| Rmd | e020351 | LucianoRogerio | 2022-03-29 | Update Henrique Analysis |
| html | e020351 | LucianoRogerio | 2022-03-29 | Update Henrique Analysis |
| Rmd | 89ac868 | HenriqueBernardino | 2022-01-29 | Análise de Correlações |
| html | 89ac868 | HenriqueBernardino | 2022-01-29 | Análise de Correlações |
| Rmd | d124c30 | LucianoRogerio | 2021-12-07 | Update of the webpage to english writting |
| html | d124c30 | LucianoRogerio | 2021-12-07 | Update of the webpage to english writting |
| Rmd | 73653b1 | LucianoRogerio | 2021-12-07 | fix the buttons at the final of each of the webpages |
| html | 73653b1 | LucianoRogerio | 2021-12-07 | fix the buttons at the final of each of the webpages |
| Rmd | f272038 | LucianoRogerio | 2021-12-07 | Update of the analysis and website layout |
| html | f51cdc6 | LucianoRogerio | 2021-11-18 | Add the Dendrogram analysis |
| Rmd | b30f532 | LucianoRogerio | 2021-11-16 | Add a header Introduction to PCA DAPC Analysis |
| html | b30f532 | LucianoRogerio | 2021-11-16 | Add a header Introduction to PCA DAPC Analysis |
| Rmd | cbf63bd | LucianoRogerio | 2021-11-16 | Add Dendrogram |
| html | cbf63bd | LucianoRogerio | 2021-11-16 | Add Dendrogram |
| html | 1faf8c1 | LucianoRogerio | 2021-11-09 | DAPC Analysis finished |
| Rmd | efcce5f | LucianoRogerio | 2021-11-09 | Merge branch ‘main’ of https://github.com/LucianoRogerio/HenriqueDGen |
| Rmd | e89306d | LucianoRogerio | 2021-11-09 | DAPC Analysis finished |
| html | e89306d | LucianoRogerio | 2021-11-09 | DAPC Analysis finished |
| Rmd | 33422ee | LucianoRogerio | 2021-11-09 | DAPC Analysis finished |
| html | 33422ee | LucianoRogerio | 2021-11-09 | DAPC Analysis finished |
| Rmd | 97d638d | LucianoRogerio | 2021-11-02 | Update of html links |
| html | 97d638d | LucianoRogerio | 2021-11-02 | Update of html links |
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/luciano/Documents/GitHub/HenriqueDGenBLUPS <- readRDS(here::here("output", "BLUPsDiseaseAgro.rds"))
BLUPS[, -1] <- scale(BLUPS[ , -1], center = T, scale = T)
BLUPS[is.na(BLUPS)] <- 0Estimation 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
}
write.table(PCA$rotation, file = here::here("output", "CorrelacoesPCAseTraits.csv"), sep = ",", quote = F)Table 1. Variance explained by each principal component
Fig 1. Barplot of the Accumulated variances of the principal components for foliar diseases.
quartz_off_screen
2 This part we prepare the print location for the labels of foliar disease traits
PointPCA1 <- as.data.frame(PCA$x)
ArrowPCA1 <- as.data.frame(PCA$rotation)
LabelsPCA1 <- 6*ArrowPCA1
LabelsPCA1[1, 1:2] <- c(-0.6, -1.8)
LabelsPCA1[2, 2] <- c(-1.8)
LabelsPCA1[3, 2] <- c(-2.5)
LabelsPCA1[4, 1] <- c(-0.8)
LabelsPCA1[5, 1:2] <- c(1.9, -0.6)
LabelsPCA1[6, 1] <- c(1.3)
LabelsPCA1[7, 2] <- c(-0.4)
LabelsPCA1[9, 2] <- c(0)
LabelsPCA1[10, 1:2] <- c(2, -1.7)
LabelsPCA1[11, 1:2] <- c(2.3, -1)Fig 2. Scatterplot of the Principal components 1 and 2 with the correlation arrows of the foliar disease resistance with the principal components.
Table 2. Analise de correlação dos Caracteristicas
Correlogram
library(corrplot)corrplot 0.92 loaded#tiff(filename = here::here("output", "Correlogram.tiff"), height = 10, width = 10, compression = "lzw",
# units = "cm", res = 350)
DadosManchasFoliares %>% dplyr::select(-CLONE) %>% cor(use = "complete.obs") %>%
corrplot::corrplot(tl.col = "black",order = "hclust") %>%
corrRect(name = c("Anth", "DMC", "RF", "NR", "PPA"))
#dev.off()Discriminant Analysis of Principal Components
library(adegenet); library(ggplot2)
BLUPS <- readRDS(here::here("output", "BLUPsDiseaseAgro.rds"))
BLUPS[, -1] <- scale(BLUPS[, -1], center = T, scale = T)
BLUPS[is.na(BLUPS)] <- 0
rownames(BLUPS) <- BLUPS$CLONE
BLUPS$CLONE <- NULL
set.seed(1)
DAPCHen <- find.clusters(BLUPS, n.pca = 3, max.n.clust = 20, choose.n.clust = F, criterion = "diffNgroup")
ClassDAPCHen <- DAPCHen$grp
DAPCHenGraph <- dapc(BLUPS, grp = ClassDAPCHen, n.pca = 3, n.da = 2)
saveRDS(DAPCHenGraph, here::here("output", "DAPCAn.rds"))
DAPCHenGraph <- readRDS(file = here::here("output", "DAPCAn.rds"))
VarDAPC <- 10*sum(DAPCHenGraph$pca.eig[1:3])*DAPCHenGraph$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(1.1)
LabelsDAPC[2, 1] <- c(0.4)
LabelsDAPC[3, 1] <- c(-0.2)
LabelsDAPC[5, 2] <- c(0.3)
LabelsDAPC[6, 2] <- c(-0.2)
LabelsDAPC[7, 1] <- c(-0.3)
LabelsDAPC[8, 2] <- c(0.7)
LabelsDAPC[9, 2] <- c(-0.35)
LabelsDAPC[10, 1] <- c(1.95)
LabelsDAPC[11, 2] <- c(-0.3)Fig 3. Scatterplot of the first and second linear discriminant function of the discriminant analysis of principal components for cassava foliar diseases, with three clusters
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
suppressMessages(library(reshape2))
BLUPS <- readRDS(here::here("output", "BLUPsDiseaseAgro.rds"))
DAPCHenGraph<- readRDS(here::here("output", "DAPCAn.rds"))
BLUPS$CLONE <- rownames(BLUPS)
BLUPS$Grp <- DAPCHenGraph$grp
BLUPSBoxplot <- reshape2::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
Attaching package: 'data.table'The following objects are masked from 'package:reshape2':
dcast, meltThe following objects are masked from 'package:lubridate':
hour, isoweek, mday, minute, month, quarter, second, wday, week,
yday, yearThe following objects are masked from 'package:dplyr':
between, first, lastThe following object is masked from 'package:purrr':
transpose
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.0
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Sao_Paulo
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.6.0 data.table_1.14.8 multcompView_0.1-9 reshape2_1.4.4
[5] corrplot_0.92 here_1.0.1 reactable_0.4.4 adegenet_2.1.10
[9] ade4_1.7-22 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[13] dplyr_1.1.2 purrr_1.0.2 readr_2.1.4 tidyr_1.3.0
[17] tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 viridisLite_0.4.2 farver_2.1.1 fastmap_1.1.1
[5] promises_1.2.1 digest_0.6.33 timechange_0.2.0 mime_0.12
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[21] knitr_1.43 labeling_0.4.2 htmlwidgets_1.6.2 plyr_1.8.8
[25] abind_1.4-5 workflowr_1.7.0 withr_2.5.0 grid_4.3.1
[29] fansi_1.0.4 git2r_0.32.0 xtable_1.8-4 colorspace_2.1-0
[33] scales_1.2.1 MASS_7.3-60 cli_3.6.1 rmarkdown_2.24
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[41] tzdb_0.4.0 ape_5.7-1 cachem_1.0.8 splines_4.3.1
[45] parallel_4.3.1 vctrs_0.6.3 Matrix_1.6-1 carData_3.0-5
[49] jsonlite_1.8.7 car_3.1-2 hms_1.1.3 rstatix_0.7.2
[53] seqinr_4.2-30 crosstalk_1.2.0 jquerylib_0.1.4 glue_1.6.2
[57] reactR_0.4.4 cowplot_1.1.1 stringi_1.7.12 gtable_0.3.3
[61] later_1.3.1 munsell_0.5.0 pillar_1.9.0 htmltools_0.5.6
[65] R6_2.5.1 rprojroot_2.0.3 evaluate_0.21 shiny_1.7.5
[69] lattice_0.21-8 highr_0.10 backports_1.4.1 broom_1.0.5
[73] httpuv_1.6.11 bslib_0.5.1 Rcpp_1.0.11 nlme_3.1-163
[77] permute_0.9-7 mgcv_1.9-0 whisker_0.4.1 xfun_0.40
[81] fs_1.6.3 pkgconfig_2.0.3