unPC Analysis
Che-Wei Chang
2021-10-15
Last updated: 2022-01-18
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Knit directory: Barley1kGBS_Proj/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 5f53139 | Che-Wei Chang | 2022-01-18 | Build site. |
| Rmd | 597a627 | Che-Wei Chang | 2021-11-30 | update analyses. unPC, ResistanceGA and GO enrichment |
| html | 597a627 | Che-Wei Chang | 2021-11-30 | update analyses. unPC, ResistanceGA and GO enrichment |
About unPC
unPC was proposed by House and Hahn (2017; https://doi.org/10.1111/1755-0998.12747) that is used for visualizing unusual patterns of genetic differentiation across a landscape.
The unPC score of a pair of subpopulations is calculated as the PCA-based genetic distances divided by geographic distance. The PCA-based genetic distance are computed as Euclidean distance between the PCA coordinates for each pair of populations. The PCA coordinates of populations are obtained by averaging the PCA coordinates (the 1st and 2nd PC dimensions) of individuals in each population.
Preparation of required data
rm(list = ls())
source("./code/Rfunctions.R")
library(MASS)
library(rnaturalearth)
library(raster)
library(rgdal)
library(mapplots)
library(scales)
library(rcompanion)
library(vcfR)
library(unPC)
vcf <- read.vcfR("/home/cheweichang/barley/WildBarleyGBS/Formal_PopGen_Analysis/GenotypicData/2020-04-07-GBS_191B1K+53KS_1st+2nd_batches_58geogroup_indmis0.1_indhet0.05_19601SNP_maf0.01_snpmis0.1_rm_het_LDprune_LDprune0.1.vcf.gz")
Xmat <- vcf_to_nummatrix(vcf = vcf)
impX <- t(apply(Xmat,1,function(x){
x[is.na(x)] <- mean(x, na.rm = T)
return(x)
}))
barley.pc <- svd(t(impX))
# set color codes
mycol <- alpha(c('#e41a1c','#377eb8','#4daf4a', '#ffffbf'), alpha = 0.8)
ind.ID <- colnames(impX)
clusters <- as.numeric(gsub(sapply(strsplit(colnames(impX), split = "_"), function(x){x[1]}), pattern = "Group", replacement = ""))
# `svd_to_unPCfile` is a customized function to convert SVD output to the input format of unPC
svd_to_unPCfile(pc = barley.pc, ind.ID = colnames(impX), clusters = as.numeric(gsub(sapply(strsplit(colnames(impX), split = "_"), function(x){x[1]}), pattern = "Group", replacement = ""))
, file.n = "./output/unPC_PC_input.evec",K = 10)
## load geographic coordinates
geo <- read.table("./data/geo_coordinates_244accessions.tsv", header = T, stringsAsFactors = F)
## calculate mean geographic coordinates of samples from each deme
grp.coord <- cbind(tapply(geo$Longitude, INDEX = geo$GeoGroup, mean), tapply(geo$Latitude, INDEX = geo$GeoGroup, mean))
coord.out <- grp.coord[match(geo$GeoGroup,rownames(grp.coord)),]
write.table(coord.out[,c(2,1)], file = "./output/unPC_coord_input.txt", row.names = F, col.names = F)
# calculate ancestry coefficients
alsQ <- alstructure::alstructure(Xmat)$Q_hat
grp.Q <- apply(t(alsQ), 2, function(x){tapply(x, INDEX = geo$GeoGroup, mean)})Run unPC
setwd("./output")
unPC::unPC(inputToProcess = "./unPC_PC_input.evec", geogrCoords = "./unPC_coord_input.txt",
plotWithMap = TRUE, roundEarth = TRUE, outputPrefix = "191B1K+53KS_unPC_visualization")
unpcout <- readRDS("191B1K+53KS_unPC_visualization_pairwiseDistCalc_unPC.rds")Box Cox transformation
trans.unPC <- boxcox(unpcout$ratioPCToGeogrDist ~ 1, lambda = seq(-6,6,0.01))
| Version | Author | Date |
|---|---|---|
| 597a627 | Che-Wei Chang | 2021-11-30 |
lambda <- trans.unPC$x[which.max(trans.unPC$y)]
unPC.boxcox <- (unpcout$ratioPCToGeogrDist^lambda - 1)/lambda
| Version | Author | Date |
|---|---|---|
| 597a627 | Che-Wei Chang | 2021-11-30 |
Population pairs with significantly unPC scores
Outlier tests were carried out by a customized function plot.unPC. This function was modified from the unPC (House and Hahn. 2017). In this function, unPC scores are first normalized with scale and the corresponding probability cumulative densities are obtained with pt function. Then, unPC scores higher and lower than the significant threshold are regarded as population pairs violating the isolation-by-distance pattern. Here, we selected the 2.5% and 97.5% of Student's t distribution as significant thresholds.
Higher than the top 2.5% threshold
Comparisons with unPC scores higher than the top 2.5% threshold that indicates a significantly low genetic similarity over a short geographical distance.
| Version | Author | Date |
|---|---|---|
| 597a627 | Che-Wei Chang | 2021-11-30 |
Lower than the bottom 2.5% threshold
Comparisons with unPC scores lower than the bottom 2.5% threshold that indicates a significantly high genetic similarity over a long geographical distance.
| Version | Author | Date |
|---|---|---|
| 597a627 | Che-Wei Chang | 2021-11-30 |
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] shape_1.4.4 unPC_0.1.0 vcfR_1.9.0
[4] rcompanion_2.0.3 scales_1.1.1 mapplots_1.5.1
[7] rgdal_1.4-8 raster_2.7-15 sp_1.4-5
[10] rnaturalearth_0.1.0 MASS_7.3-51.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-139 matrixStats_0.57.0 fs_1.5.0
[4] sf_1.0-3 RColorBrewer_1.1-2 rprojroot_1.3-2
[7] tools_3.6.0 backports_1.1.5 utf8_1.2.2
[10] R6_2.5.1 vegan_2.5-6 KernSmooth_2.23-15
[13] mgcv_1.8-28 nortest_1.0-4 DBI_1.1.1
[16] colorspace_2.0-2 permute_0.9-5 tidyselect_1.1.1
[19] Exact_3.0 compiler_3.6.0 git2r_0.26.1
[22] alstructure_0.1.0 expm_0.999-6 sandwich_3.0-1
[25] labeling_0.4.2 lmtest_0.9-39 classInt_0.4-3
[28] mvtnorm_1.0-12 proxy_0.4-26 multcompView_0.1-8
[31] stringr_1.4.0 digest_0.6.28 PBSmapping_2.72.1
[34] rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.2
[37] highr_0.9 maps_3.3.0 fastmap_1.1.0
[40] rlang_0.4.12 rstudioapi_0.13 farver_2.1.0
[43] generics_0.1.0 zoo_1.8-9 dplyr_1.0.7
[46] magrittr_2.0.1 modeltools_0.2-23 Matrix_1.2-18
[49] Rcpp_1.0.7 DescTools_0.99.44 munsell_0.5.0
[52] fansi_0.5.0 ape_5.3 lifecycle_1.0.1
[55] stringi_1.7.5 multcomp_1.4-17 whisker_0.4
[58] yaml_2.2.1 rootSolve_1.8.2.3 plyr_1.8.6
[61] pinfsc50_1.1.0 grid_3.6.0 parallel_3.6.0
[64] promises_1.2.0.1 crayon_1.4.1 lmom_2.8
[67] lattice_0.20-38 splines_3.6.0 mapproj_1.2.6
[70] knitr_1.36 pillar_1.6.4 EMT_1.2
[73] boot_1.3-24 gld_2.6.3 codetools_0.2-16
[76] stats4_3.6.0 glue_1.4.2 evaluate_0.14
[79] memuse_4.0-0 data.table_1.12.8 vctrs_0.3.8
[82] httpuv_1.6.3 gtable_0.3.0 purrr_0.3.4
[85] assertthat_0.2.1 ggplot2_3.3.5 xfun_0.27
[88] coin_1.4-2 libcoin_1.0-9 e1071_1.7-9
[91] rnaturalearthhires_0.2.0 later_1.3.0 viridisLite_0.4.0
[94] class_7.3-15 survival_3.2-13 tibble_3.1.5
[97] cluster_2.0.8 units_0.7-2 TH.data_1.1-0
[100] ellipsis_0.3.2