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top3.Rmd
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top3.Rmd
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---
title: "Covariance Correlations"
output:
workflowr::wflow_html:
toc: true
latex_engine: "xelatex"
code_folding: "hide"
editor_options:
chunk_output_type: console
---
```{r 0-setup, include=FALSE, warning=FALSE}
#regular
library(dplyr)
library(data.table)
library(ggplot2)
library(cowplot)
library(qqman)
library(viridis)
library(scales)
library(tidyverse)
#options
options(bitmapType = "cairo")
options(error = function() traceback(3))
#seed
set.seed(123)
#loop count and data limit
iter <- 50
#ggplot holder list
gg <- vector(mode='list', length=12)
setwd('/data2/morgante_lab/nklimko/rep/dgrp-starve/')
```
The following methods are currently implemented:
- Mr. Mash
- GBLUP
- BayesC
```{r cor-female, warning=FALSE}
mash <- readRDS('snake/data/30_summary/mr.mash_f_top3.Rds')
bayesC <- readRDS('snake/data/30_summary/multibayesC_f_top3.Rds')
blup <- readRDS('snake/data/30_summary/multigblup_f_top3.Rds')
lasso <- readRDS('snake/data/30_summary/mlasso_f_top3.Rds')
allData <- list(mash, bayesC, blup, lasso)
#correlations----
hold <- 0
for(i in allData){
raw <- unlist(i[1,])
trim <- raw[seq(1, length(raw), by=16)]
hold <- cbind(hold, trim)
}
corData <- hold[,-1]
#runtime----
hold <- 0
for(i in allData){
raw <- unlist(i[2,])
trim <- raw[seq(3, length(raw), by=5)]
hold <- cbind(hold, trim)
}
timeData <- hold[,-1]
#graphing----
iter <- 50
label <- c(rep("mr.mash", iter), rep("bayesC", iter), rep("gblup", iter), rep("lasso", iter))
data <- data.table(cor=as.numeric(unlist(corData)), method=label)
gg[[1]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Correlation',tag='F') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
data <- data.table(cor=as.numeric(unlist(timeData)), method=label)
gg[[2]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Runtime',tag='F') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
```
```{r cor-male, warning=FALSE}
mash <- readRDS('snake/data/30_summary/mr.mash_m_top3.Rds')
bayesC <- readRDS('snake/data/30_summary/multibayesC_m_top3.Rds')
blup <- readRDS('snake/data/30_summary/multigblup_m_top3.Rds')
lasso <- readRDS('snake/data/30_summary/mlasso_m_top3.Rds')
allData <- list(mash, bayesC, blup, lasso)
#correlations----
hold <- 0
for(i in allData){
raw <- unlist(i[1,])
trim <- raw[seq(1, length(raw), by=16)]
hold <- cbind(hold, trim)
}
corData <- hold[,-1]
#runtime----
hold <- 0
for(i in allData){
raw <- unlist(i[2,])
trim <- raw[seq(3, length(raw), by=5)]
hold <- cbind(hold, trim)
}
timeData <- hold[,-1]
#graphing----
iter <- 50
label <- c(rep("mr.mash", iter), rep("bayesC", iter), rep("gblup", iter), rep("lasso", iter))
data <- data.table(cor=as.numeric(unlist(corData)), method=label)
gg[[3]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Correlation',tag='F') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
data <- data.table(cor=as.numeric(unlist(timeData)), method=label)
gg[[4]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Runtime',tag='F') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
```
### Female Correlation + Runtime
```{r plot-print-f, warning=FALSE, eval=TRUE}
plot_grid(gg[[1]],gg[[2]], ncol=2)
```
### Male Correlation + Runtime
```{r plot-print-m, warning=FALSE, eval=TRUE}
plot_grid(gg[[3]],gg[[4]], ncol=2)
```