Analise de Componentes Principais e Discriminantes
LucianoRogerio e HenriqueBernardino
2021-11-02
Last updated: 2021-11-16
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Knit directory: HenriqueDGen/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
Introduction
suppressMessages(library(tidyverse))
suppressMessages(library(adegenet))
library(here)here() starts at /Users/lbd54/Documents/GitHub/HenriqueDGenBLUPS <- readRDS(here::here("output", "BLUPsDisease.RDS"))
### Analise de Componentes Principais
BLUPS[ , 2:6] <- scale(BLUPS[ , 2:6], center = T, scale = T)
BLUPS[is.na(BLUPS)] <- 0
PCA <- prcomp(BLUPS[,-1])
Perc <- 100*PCA$sdev^2/sum(PCA$sdev^2)
Perc[1] 47.2776653 21.0120007 17.0577803 14.0511752 0.6013786PercAc <- as.vector(rep(NA, times = length(Perc)))
for(i in 1:length(Perc)) {
PercAc[i] <- sum(Perc[1:i])
names(PercAc)[i] <- i
}
# Fig 1. Grafico de Barras das variancias acumuladas dos componentes principais para tolerancia as doencas foliares.
barplot(PercAc, main = "Variance explained by PCA",
ylab = "Cumulative variance (%)", xlab = "Number of retained PCs",
col = c("gray", "red", "gray", "gray", "gray"))
| Version | Author | Date |
|---|---|---|
| 97d638d | LucianoRogerio | 2021-11-02 |
##
PointPCA1 <- as.data.frame(PCA$x)
ArrowPCA1 <- as.data.frame(PCA$rotation)
LabelsPCA1 <- 3*ArrowPCA1
LabelsPCA1$PC2[1] <- 3
LabelsPCA1[2, 1:2] <- c(1.1, -1.1)
LabelsPCA1[3, 1:2] <- c(2.5, 0.45)
LabelsPCA1[4, 1:2] <- c(2.5, 0.15)
LabelsPCA1[5, 1:2] <- c(0.8, 0.6)
ggplot(data = PointPCA1, aes(x = PC1, y = PC2)) +
geom_point(na.rm = T, colour = "gray") + geom_rug(col = "steelblue", alpha = 0.2, size = 1.5) +
geom_segment(mapping = aes(x = 0, xend = 3*PC1, y = 0, yend = 3*PC2),
colour = "red",
data = ArrowPCA1, arrow = arrow(type = "closed",
length = unit(0.2,units = "cm"))) +
geom_text(mapping = aes(x = PC1, y = PC2, label = rownames(ArrowPCA1)),
data = LabelsPCA1, colour = "black") +
theme_bw() +
xlab("PC1 - 47.28%") + ylab("PC2 - 21.01%")
### Análise Discriminante de Componentes Principais
library(adegenet); library(ggplot2)
BLUPS <- readRDS(here::here("output", "BLUPsDisease.RDS"))
BLUPS[ , 2:6] <- scale(BLUPS[ , 2:6], center = T, scale = T)
BLUPS[is.na(BLUPS)] <- 0
rownames(BLUPS) <- BLUPS$CLONE
BLUPS$CLONE <- NULL
set.seed(1)
DAPCHen <- find.clusters(BLUPS, n.pca = 2, max.n.clust = 20, choose.n.clust = FALSE, criterion = "diffNgroup")
ClassDAPCHen <- DAPCHen$grp
DAPCHenGraph <- dapc(BLUPS, grp = ClassDAPCHen, n.pca = 2, n.da = 2)
saveRDS(DAPCHenGraph, here::here("output", "DAPCAn.RDS"))
DAPCIndPoint <- data.frame(DAPCHenGraph$ind.coord, grp = DAPCHenGraph$grp)
DAPCGrpEllip <- data.frame(DAPCHenGraph$grp.coord, grp = as.character(1:4))
ArrowDAPC <- as.data.frame(DAPCHenGraph$var.contr)
LabelsDAPC <- data.frame(Trait = rownames(ArrowDAPC), ArrowDAPC*5)
LabelsDAPC[1,3] <- 4.7
LabelsDAPC[2,2:3] <- c(0.7, 0.8)
LabelsDAPC[3,2:3] <- c(2.5, 0.45)
LabelsDAPC[4,2:3] <- c(2.5, -0.35)
LabelsDAPC[5,2:3] <- c(0.3, -0.35)
ggplot(data = DAPCIndPoint, aes(x = LD1, y = LD2, color = grp)) +
geom_point(na.rm = T) + geom_rug(col = "steelblue", alpha = 0.2, size = 1.5) +
theme_bw() +
scale_color_viridis_d() +
stat_ellipse(geom="polygon", aes(fill = grp),
alpha = 0.2,
show.legend = FALSE,
level = 0.95) + guides(color = "none") +
geom_label(data = DAPCGrpEllip, mapping = aes(x = LD1, y = LD2, label = grp)) +
geom_segment(mapping = aes(x = 0, xend = 5*LD1, y = 0, yend = 5*LD2),
colour = "red",
data = ArrowDAPC, arrow = arrow(type = "closed",
length = unit(0.2,units = "cm"))) +
geom_text(mapping = aes(x = LD1, y = LD2, label = Trait),
data = LabelsDAPC, colour = "black") +
xlab("LD1 - 56.54%") + ylab("LD2 - 11.75%")
library(reshape2)
Attaching package: 'reshape2'The following object is masked from 'package:tidyr':
smithsBLUPS$CLONE <- rownames(BLUPS)
BLUPS$Grp <- DAPCHenGraph$grp
BLUPSBoxplot <- melt(BLUPS, variable.name = "Trait", value.name = "Y", id.vars = c("CLONE", "Grp"))
ggplot(data = BLUPSBoxplot, mapping = aes(x = Grp, y = Y, fill = Grp)) + ylim(-3,4) +
geom_boxplot(outlier.shape = NA) + facet_wrap(facets = ~ Trait, ncol = 3) +
scale_fill_viridis_d() + guides(fill = "none") + ylab("BLUPs") + xlab("DACP Group")Warning: Removed 6 rows containing non-finite values (stat_boxplot).
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sessionInfo()R version 4.1.1 (2021-08-10)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.0.dylib
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 adegenet_2.1.5 ade4_1.7-18
[5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[9] readr_2.1.0 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[13] tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] nlme_3.1-153 fs_1.5.0 lubridate_1.8.0 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.1.1 backports_1.3.0 bslib_0.3.1
[9] utf8_1.2.2 R6_2.5.1 vegan_2.5-7 DBI_1.1.1
[13] mgcv_1.8-38 colorspace_2.0-2 permute_0.9-5 withr_2.4.2
[17] tidyselect_1.1.1 compiler_4.1.1 git2r_0.28.0 cli_3.1.0
[21] rvest_1.0.2 xml2_1.3.2 labeling_0.4.2 sass_0.4.0
[25] scales_1.1.1 digest_0.6.28 rmarkdown_2.11 pkgconfig_2.0.3
[29] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1 fastmap_1.1.0
[33] rlang_0.4.12 readxl_1.3.1 rstudioapi_0.13 shiny_1.7.1
[37] farver_2.1.0 jquerylib_0.1.4 generics_0.1.1 jsonlite_1.7.2
[41] magrittr_2.0.1 Matrix_1.3-4 Rcpp_1.0.7 munsell_0.5.0
[45] fansi_0.5.0 ape_5.5 lifecycle_1.0.1 stringi_1.7.5
[49] whisker_0.4 yaml_2.2.1 MASS_7.3-54 plyr_1.8.6
[53] grid_4.1.1 parallel_4.1.1 promises_1.2.0.1 crayon_1.4.2
[57] lattice_0.20-45 haven_2.4.3 splines_4.1.1 hms_1.1.1
[61] knitr_1.36 pillar_1.6.4 igraph_1.2.8 seqinr_4.2-8
[65] reprex_2.0.1 glue_1.5.0 evaluate_0.14 modelr_0.1.8
[69] vctrs_0.3.8 tzdb_0.2.0 httpuv_1.6.3 cellranger_1.1.0
[73] gtable_0.3.0 assertthat_0.2.1 xfun_0.28 mime_0.12
[77] xtable_1.8-4 broom_0.7.10 later_1.3.0 viridisLite_0.4.0
[81] workflowr_1.6.2 cluster_2.1.2 ellipsis_0.3.2