recomb_size_founder_prop
Hao He
2019-09-23
Last updated: 2019-09-23
Checks: 6 1
Knit directory: csna_workflow/
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This script will plot the recombination block size and founder props across all generations
library
library(qtl2)
library(abind)
library(tidyverse)
library(plotly)
require(vcd)
require(MASS)
options(stringsAsFactors = F)
# Reformat for tidyverse.
reformat_probs = function(probs) {
mat = matrix(0, nrow = nrow(probs) * dim(probs)[3], ncol = 8,
dimnames = list(rep(rownames(probs), dim(probs)[3]),
names(CCcolors)))
for(i in 1:dim(probs)[3]) {
st = (i - 1) * nrow(probs) + 1
en = i * nrow(probs)
mat[st:en,] = probs[,,i]
} # for(i)
return(data.frame(chr = rep(markers$chr, each = nrow(probs)),
pos = rep(markers$pos, each = nrow(probs)), mat))
} # reformat_probs()
# NOTE: I'm using a lower case L as the beginning of 129 becuase
# the tidy functions won't allow a number or a "_" at the
# beginning of a variable name.
names(CCcolors) = c("A_J", "C57BL_6J", "l29S1_SvImJ", "NOD_ShiLtJ",
"NZO_HlLtJ", "CAST_EiJ", "PWK_PhJ", "WSB_EiJ")plot founder_proportions
#allele probs
load("data/Jackson_Lab_Bubier_MURGIGV01/apr_DO2437.RData")
#geno probs
load("data/Jackson_Lab_Bubier_MURGIGV01/pr_DO2437.RData")
#cross infor
load("data/Jackson_Lab_Bubier_MURGIGV01/gm_DO2437_qc.RData")
#combine all chrs into one 3d array
apr.3d.all <- do.call("abind",list(apr,along = 3))
rm(apr)
# Load in the markers.
load(url("ftp://ftp.jax.org/MUGA/GM_snps.Rdata"))
GM_snps <- GM_snps[!is.na(GM_snps$chr),]
GM_snps$chr <- factor(GM_snps$chr)
markers = GM_snps[intersect(dimnames(apr.3d.all)[[3]],GM_snps$marker),1:4]
markers$chr = factor(markers$chr, levels = c(1:19, "X"))
# subset to the markers
apr.3d.all <- apr.3d.all[,,markers$marker]
#subset to each generation
for(g in unique(gm_DO2437_qc$covar$ngen)){
#g = "21"
print(g)
apr.3d <- apr.3d.all[gm_DO2437_qc$covar[gm_DO2437_qc$covar$ngen == g,"id"],,]
#reformat
probs = reformat_probs(apr.3d)
gc()
# Summarize founder proportion by chromosome.
fp = probs %>% group_by(chr, pos) %>%
summarize_all(mean) %>%
gather(founder, prop, 3:10)
fp$founder = factor(fp$founder, levels = names(CCcolors))
#plot for all the chromosomes
png(paste0("output/DO_Gigamuga_founder_proportions_G", g, ".png"), width = 3600,
height = 2000, res = 128)
p1 <- ggplot(fp, aes(pos, prop, color = founder)) +
geom_line() +
geom_hline(yintercept=0.125, linetype="dashed", color = "black", size = 0.25) +
scale_color_manual(values = CCcolors) +
facet_grid(chr~founder)
print(p1)
dev.off()
print(p1)
# Make a plot of just Chr 2.
png(paste0("output/DO_Gigamuga_founder_proportions_chr2_G", g, ".png"), width = 1200,
height = 800, res = 128)
p2 <- fp %>% filter(chr == 2) %>%
ggplot(aes(pos, prop, color = founder)) +
geom_line() +
geom_hline(yintercept=0.125, linetype="dashed", color = "black", size = 0.25) +
scale_color_manual(values = CCcolors) +
facet_grid(founder~.)+
labs(title = "Chr 2")
print(p2)
dev.off()
print(p2)
#plot on chr2 for WSB
png(paste0("output/DO_Gigamuga_chr2_WSB_G", g, ".png"), width = 1200,
height = 800, res = 128)
chr2 = probs[probs$chr == 2, c(1,2,10)]
agg = aggregate(chr2$WSB_EiJ, list(chr2$pos), mean)
plot(agg, type = "l", ylab = "prop", xlab = "Chr2", col = "#B10DC9")
abline(h=0.125, col="black")
dev.off()
chr2 = probs[probs$chr == 2, c(1,2,10)]
agg = aggregate(chr2$WSB_EiJ, list(chr2$pos), mean)
plot(agg, type = "l", ylab = "prop", xlab = "Chr2", col = "#B10DC9")
abline(h=0.125, col="black")
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#locate xo pos
g <- maxmarg(pr, cores = 20)
pos <- locate_xo(g, gm_DO2437_qc$gmap, cores = 20)
pos_ind <- list()
for(i in ind_ids(gm_DO2437_qc)){
pos_ind[[i]] <- list()
for (j in c(1:19, "X")) {
pos_ind[[i]][[j]] <- diff(pos[[j]][[i]])
}
pos_ind[[i]] <- as.vector(unlist(pos_ind[[i]]))
}
# for each generation
#subset to each generation
pos_ind_gen <- list()
for(g in unique(gm_DO2437_qc$covar$ngen)){
#g = "21"
pos_ind_gen[[g]] <- as.vector(unlist(pos_ind[gm_DO2437_qc$covar[gm_DO2437_qc$covar$ngen == g,"id"]]))
}
save(pos_ind, pos_ind_gen, file = "data/Jackson_Lab_Bubier_MURGIGV01/recom_block_size.RData")plot Recombination Block Size
for(g in unique(gm_DO2437_qc$covar$ngen)){
#plot for recom block size
png(paste0("output/DO_recom_block_size_G", g, ".png"))
x <- pos_ind_gen[[g]][pos_ind_gen[[g]] != 0]
# estimate the parameters
fit1 <- fitdistr(x, "exponential")
# goodness of fit test
ks.test(x, "pexp", fit1$estimate) # p-value > 0.05 -> distribution not refused
# plot a graph
hist(x,
freq = FALSE,
breaks = 200,
xlim = c(0, 5+quantile(x, 1)),
#ylim = c(0,0.3),
xlab = "Recombination Block Size (Mb)",
main = paste0("Gen ", g))
curve(dexp(x, rate = fit1$estimate),
from = 0,
to = 5+quantile(x, 1),
col = "red",
add = TRUE)
dev.off()
}
for(g in unique(gm_DO2437_qc$covar$ngen)){
#plot for recom block size
#png(paste0("output/DO_recom_block_size_G", g, ".png"))
x <- pos_ind_gen[[g]][pos_ind_gen[[g]] != 0]
# estimate the parameters
fit1 <- fitdistr(x, "exponential")
# goodness of fit test
ks.test(x, "pexp", fit1$estimate) # p-value > 0.05 -> distribution not refused
# plot a graph
hist(x,
freq = FALSE,
breaks = 200,
xlim = c(0, 5+quantile(x, 1)),
#ylim = c(0,0.3),
xlab = "Recombination Block Size (Mb)",
main = paste0("Gen ", g))
curve(dexp(x, rate = fit1$estimate),
from = 0,
to = 5+quantile(x, 1),
col = "red",
add = TRUE)
#dev.off()
}
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plot for each generation
fp <- list()
#subset to each generation
for(g in unique(gm_DO2437_qc$covar$ngen)){
#g = "21"
apr.3d <- apr.3d.all[gm_DO2437_qc$covar[gm_DO2437_qc$covar$ngen == g,"id"],,]
#reformat
probs = reformat_probs(apr.3d)
gc()
# Summarize founder proportion by chromosome.
fp[[g]] = probs %>% group_by(chr, pos) %>%
summarize_all(mean) %>%
gather(founder, prop, 3:10)
fp[[g]]$founder = factor(fp[[g]]$founder, levels = names(CCcolors))
}
save(fp, file = "data/Jackson_Lab_Bubier_MURGIGV01/fp.RData")
# add gen
for(g in unique(gm_DO2437_qc$covar$ngen)){
#g = "21"
fp[[g]]$gen <- as.factor(g)
}
fp_data <- do.call(rbind.data.frame,fp)
p <- list()
for(c in unique(names(gm_DO2437_qc$geno))){
print(c)
fp_subdata <- fp_data[fp_data$chr == c,]
pp <- ggplot(data = fp_subdata,aes(pos, prop, group = gen, color = founder)) +
geom_line(aes(linetype=gen)) +
scale_linetype_manual(values=rep("solid",9)) +
geom_hline(yintercept=0.125, linetype="dashed", color = "black", size = 0.25) +
scale_color_manual(values = CCcolors) +
facet_grid(founder~.) +
labs(title = paste0("Chr ", c)) +
theme(legend.position='none')
p[[c]] <- ggplotly(pp, width = 1000, height = 1000)
#htmlwidgets::saveWidget(as_widget(p[[c]]), paste0("/projects/heh/csna_workflow/output/prop_across_generation_chr",c,".html"))
}[1] "1"
[1] "2"
[1] "3"
[1] "4"
[1] "5"
[1] "6"
[1] "7"
[1] "8"
[1] "9"
[1] "10"
[1] "11"
[1] "12"
[1] "13"
[1] "14"
[1] "15"
[1] "16"
[1] "17"
[1] "18"
[1] "19"
[1] "X"save(p, file = "output/prop_across_generation_chr_p.RData")
#as_widget(p[["1"]])
#as_widget(p[["2"]])
# as_widget(p[["3"]])
# as_widget(p[["4"]])
# as_widget(p[["5"]])
# as_widget(p[["6"]])
# as_widget(p[["7"]])
# as_widget(p[["8"]])
# as_widget(p[["9"]])
# as_widget(p[["10"]])
# as_widget(p[["11"]])
# as_widget(p[["12"]])
# as_widget(p[["13"]])
# as_widget(p[["14"]])
# as_widget(p[["15"]])
# as_widget(p[["16"]])
# as_widget(p[["17"]])
# as_widget(p[["18"]])
# as_widget(p[["19"]])
# as_widget(p[["X"]])
sessionInfo()R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.5 (Final)
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] grid stats graphics grDevices utils datasets base
other attached packages:
[1] MASS_7.3-50 vcd_1.4-4 plotly_4.9.0 forcats_0.4.0
[5] stringr_1.3.1 dplyr_0.8.3 purrr_0.3.2 readr_1.3.1
[9] tidyr_1.0.0 tibble_2.1.3 ggplot2_3.1.0 tidyverse_1.2.1
[13] abind_1.4-5 qtl2_0.18
loaded via a namespace (and not attached):
[1] httr_1.4.0 bit64_0.9-7 jsonlite_1.6
[4] viridisLite_0.3.0 modelr_0.1.4 shiny_1.3.2
[7] assertthat_0.2.1 highr_0.6 blob_1.1.1
[10] cellranger_1.1.0 yaml_2.2.0 pillar_1.3.1
[13] RSQLite_2.1.1 backports_1.1.2 lattice_0.20-35
[16] glue_1.3.1 digest_0.6.18 promises_1.0.1
[19] rvest_0.3.2 colorspace_1.4-0 htmltools_0.3.6
[22] httpuv_1.5.2 plyr_1.8.4 pkgconfig_2.0.1
[25] broom_0.5.2 haven_2.1.1 xtable_1.8-2
[28] scales_1.0.0 whisker_0.3-2 later_0.8.0
[31] git2r_0.23.0 generics_0.0.2 ellipsis_0.2.0.1
[34] withr_2.1.2 lazyeval_0.2.1 cli_1.1.0
[37] mime_0.6 magrittr_1.5 crayon_1.3.4
[40] readxl_1.3.1 memoise_1.1.0 evaluate_0.10
[43] methods_3.3.2 fs_1.2.6 nlme_3.1-128
[46] xml2_1.2.1 tools_3.3.2 data.table_1.11.4
[49] hms_0.5.1 lifecycle_0.1.0 munsell_0.5.0
[52] rlang_0.4.0 rstudioapi_0.10 htmlwidgets_1.3
[55] crosstalk_1.0.0 labeling_0.3 rmarkdown_1.11
[58] gtable_0.2.0 DBI_1.0.0 reshape2_1.4.3
[61] R6_2.4.0 zoo_1.8-6 lubridate_1.7.4
[64] knitr_1.20 bit_1.1-14 zeallot_0.1.0
[67] workflowr_1.4.0 rprojroot_1.3-2 stringi_1.2.4
[70] parallel_3.3.2 Rcpp_1.0.2 vctrs_0.2.0
[73] tidyselect_0.2.5 lmtest_0.9-36