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Pooling visualization 2.Rmd
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---
title: 'Pooling: Visualization 2'
author: "Xianbin Cheng"
date: "January 24, 2019"
output: html_document
---
# Objective
Visualize the metrics for 48- and 96-well plate scenarios.
# Method
1. Load all the necessary packages and source codes.
```{r, message=FALSE, warning = FALSE}
library(tidyverse)
library(Hmisc)
source("1D_pooling_simulation.R")
source("2D_pooling_simulation.R")
source("STD_simulation.R")
```
2. Read rds files and necessary parameters.
* These files contain simulation results of STD(48; 7; 2) pooling, STD(96; 5; 3), one-dimensional and two-dimensional poolings. The iteration time is 1000. The seed is set at 123.
```{r, eval = FALSE}
# 48-well
STD_48 = readRDS(file = "STD_48_7_2_1000.rds")
pool_row_48 = readRDS("1D_row_48_1000.rds")
pool_col_48 = readRDS("1D_col_48_1000.rds")
pool_2d_48 = readRDS("2D_48_1000.rds")
STD_scheme_48 = read.csv("STD_48_7_2_1pos.csv", header = TRUE, row.names = 1)
# 96-well
STD_96 = readRDS(file = "STD_96_5_3_1000.rds")
pool_row_96 = readRDS("1D_row_96_1000.rds")
pool_col_96 = readRDS("1D_col_96_1000.rds")
pool_2d_96 = readRDS("2D_96_1000.rds")
STD_scheme_96 = read.csv("STD_96_5_3_1pos.csv", header = TRUE, row.names = 1)
```
3. Clean them up for visualization.
```{r, eval= FALSE}
### STD
# 48-well
metrics_STD_48 = do.call(what = rbind.data.frame, args = STD_48) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_STD_48 = calc_STD_cost(data = STD_48, n = 48, scheme = STD_scheme_48)
# 96-well
metrics_STD_96 = do.call(what = rbind.data.frame, args = STD_96) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_STD_96 = calc_STD_cost(data = STD_96, n = 96, scheme = STD_scheme_96)
```
```{r, eval= FALSE}
### 48-well
# Pool by rows
metrics_row_48 = do.call(what = rbind.data.frame, args = pool_row_48) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_row_48 = calc_1d_cost(data = pool_row_48, n = 48, by = "row")
# Pool by columns
metrics_col_48 = do.call(what = rbind.data.frame, args = pool_col_48) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_col_48 = calc_1d_cost(data = pool_col_48, n = 48, by = "column")
### 96-well
# Pool by rows
metrics_row_96 = do.call(what = rbind.data.frame, args = pool_row_96) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_row_96 = calc_1d_cost(data = pool_row_96, n = 96, by = "row")
# Pool by columns
metrics_col_96 = do.call(what = rbind.data.frame, args = pool_col_96) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_col_96 = calc_1d_cost(data = pool_col_96, n = 96, by = "column")
```
```{r, eval = FALSE}
## 2D Pool
# 48-well
metrics_2d_48 = do.call(what = rbind.data.frame, args = pool_2d_48) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_2d_48 = calc_2d_cost(data = pool_2d_48, n = 48)
# 96-well
metrics_2d_96 = do.call(what = rbind.data.frame, args = pool_2d_96) %>%
gather(data = ., key = "Type", value = "Value", - n_pos)
cost_2d_96 = calc_2d_cost(data = pool_2d_96, n = 96)
```
```{r, eval= FALSE}
#### 48-well
### All metrics
metrics_all_48 = bind_rows(metrics_STD_48, metrics_row_48, metrics_col_48, metrics_2d_48) %>%
mutate(Pooling = c(rep("STD", times = nrow(metrics_STD_48)),
rep("Row", times = nrow(metrics_row_48)),
rep("Column", times = nrow(metrics_col_48)),
rep("2D", times = nrow(metrics_2d_48))))
### All cost
cost_all_48 = bind_rows(cost_STD_48, cost_row_48, cost_col_48, cost_2d_48) %>%
mutate(Pooling = c(rep("STD", times = nrow(cost_STD_48)),
rep("Row", times = nrow(cost_row_48)),
rep("Column", times = nrow(cost_col_48)),
rep("2D", times = nrow(cost_2d_48))))
### 96-well
### All metrics
metrics_all_96 = bind_rows(metrics_STD_96, metrics_row_96, metrics_col_96, metrics_2d_96) %>%
mutate(Pooling = c(rep("STD", times = nrow(metrics_STD_96)),
rep("Row", times = nrow(metrics_row_96)),
rep("Column", times = nrow(metrics_col_96)),
rep("2D", times = nrow(metrics_2d_96))))
### All cost
cost_all_96 = bind_rows(cost_STD_96, cost_row_96, cost_col_96, cost_2d_96) %>%
mutate(Pooling = c(rep("STD", times = nrow(cost_STD_96)),
rep("Row", times = nrow(cost_row_96)),
rep("Column", times = nrow(cost_col_96)),
rep("2D", times = nrow(cost_2d_96))))
```
```{r, eval= FALSE}
write.csv(x = metrics_all_48, file = "Metrics_all_48_02_10_19.csv")
write.csv(x = metrics_all_96, file = "Metrics_all_96_02_10_19.csv")
write.csv(x = cost_all_48, file = "Cost_all_48_02_10_19.csv")
write.csv(x = cost_all_96, file = "Cost_all_96_02_10_19.csv")
```
```{r, echo= FALSE}
fix_levels = function(data){
data$Pooling = factor(x = data$Pooling, levels = c("Column", "Row", "2D","STD"))
levels(data$Pooling) = c("1D: Column", "1D: Row", "2D", "STD")
return(data)
}
metrics_all_48 = read.csv(file = "Metrics_all_48_02_10_19.csv", row.names = 1) %>% fix_levels(.)
metrics_all_96 = read.csv(file = "Metrics_all_96_02_10_19.csv", row.names = 1) %>% fix_levels(.)
cost_all_48 = read.csv(file = "Cost_all_48_02_10_19.csv", row.names = 1) %>% fix_levels(.)
cost_all_96 = read.csv(file = "Cost_all_96_02_10_19.csv", row.names = 1) %>% fix_levels(.)
```
# Result
1. Visualization of metrics
```{r, fig.show = "hold", out.width = "50%"}
# 48-well
p1 = draw_metrics_all(df = metrics_all_48, n = 48)
p1
# 96-well
p2 = draw_metrics_all(df = metrics_all_96, n = 96)
p2
```
2. Visualization of reagent cost.
```{r, fig.show = "hold", out.width = "50%"}
# 48-well
p3 = draw_cost_all(df = cost_all_48, n = 48, var = n_test_total, ylab = "The total number of tests needed")
p3
# 96-well
p4 = draw_cost_all(df = cost_all_96, n = 96, var = n_test_total, ylab = "The total number of tests needed")
p4
```
3. Visualization of labor cost
```{r, fig.show = "hold", out.width = "50%"}
# 48-well
p5 = draw_cost_all(df = cost_all_48, n = 48, var = n_pipette, ylab = "The number of transfers")
p5
# 96-well
p6 = draw_cost_all(df = cost_all_96, n = 96, var = n_pipette, ylab = "The number of transfers")
p6
```
```{r, eval = FALSE, echo= FALSE}
pdf(file = "Pooling Visualization 2.pdf")
p1
p2
p3
p4
p5
p6
dev.off()
```