Last updated: 2020-03-16

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Rmd 8848080 jgblanc 2020-03-16 wflow_publish(c(“analysis/index.Rmd”, “analysis/Expression_plots.Rmd”))

Intro

Here we have the code to plot Expression vs PC for any gen/PC combination in any tissue. This is the code used to generate Figure 6 in the main text. It is important to note that it is easy to plot Expression vs. PC with the line of best fit for any gene. However, to plot confidence intervals you need to get \(\text{var}(C_{11:20})\) from the selection test. To plot the correct confidence interval you would need to record the “var_t” value from the selection test and plug that into the code below used to calculate “k_plus”.

Figures

# Read in Population and Expression Info 
pop <- read.csv("../data/FlintGarciaTableS1_fixednew.csv")
exp <- read.table("../data/Mean_centered_expression/Kern.txt")
exp$lines <- row.names(exp)
m <- merge(pop, exp, by.x = "Inbred", by.y = "lines" )
pop_dat <- m[,1:8]

# Read in Kinship Matrix 
myF <- read.table('../data/Kinship_matrices/F_Kern.txt')

## Get Eigen Values and Vectors 
myE <- eigen(myF)
E_vectors <- myE$vectors
E_values <- myE$values

# Get PC and Expression
pop_dat$PC <- E_vectors[,2] # Can change PC
pop_dat$Gene <- exp[,"GRMZM2G093574"] # Can change gene name

# Get CI info
lambda <- E_values[2]
k_plus <-  1.96 * sqrt(4836.755 * lambda) 
k_minus <- -k_plus

# Do linear regression and get slope 
lR <- lm(pop_dat$Gene ~ pop_dat$PC) 
coeff <- lR$coefficients[[2]]

# Plot 
col <- c('#E69F00', '#56B4E9', "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
ggplot(data=pop_dat, aes(x = PC, y= Gene , color=Subpopulation)) + scale_colour_manual(values = col, labels=c("mixed", "non-stiff stalk", "popcorn", "stiff stalk", "sweet", "tropical")) + xlab("PC 2") +  ylab("Expression") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.title.y = element_text(size=12), axis.title.x  = element_text(size=12), legend.position = "bottom", legend.title = element_text(size = 12), legend.text = element_text(size = 10)) + geom_point(size = 2.5) + geom_abline(slope = k_plus, linetype = 2) + geom_abline(slope = coeff, size = 1.5)+  geom_abline(slope = k_minus, linetype = 2) + scale_y_continuous(limits = c(-300, 900), breaks = c(-300, 0, 300, 600, 900))


sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] ggplot2_3.2.1   workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3       compiler_3.6.2   pillar_1.4.3     later_1.0.0     
 [5] git2r_0.26.1     tools_3.6.2      digest_0.6.25    evaluate_0.14   
 [9] lifecycle_0.1.0  tibble_2.1.3     gtable_0.3.0     pkgconfig_2.0.3 
[13] rlang_0.4.4      yaml_2.2.1       xfun_0.12        withr_2.1.2     
[17] stringr_1.4.0    dplyr_0.8.4      knitr_1.28       fs_1.3.1        
[21] rprojroot_1.3-2  grid_3.6.2       tidyselect_1.0.0 glue_1.3.1      
[25] R6_2.4.1         rmarkdown_2.1    farver_2.0.3     purrr_0.3.3     
[29] magrittr_1.5     whisker_0.4      backports_1.1.5  scales_1.1.0    
[33] promises_1.1.0   htmltools_0.4.0  assertthat_0.2.1 colorspace_1.4-1
[37] httpuv_1.5.2     labeling_0.3     stringi_1.4.6    lazyeval_0.2.2  
[41] munsell_0.5.0    crayon_1.3.4