Performance of prime-seq across many experiments

###1. Load the following packages:

library(tidyverse)
library(ggsignif)
library(ggplotify)
library(ggrepel)
library(ggbeeswarm)
library(edgeR)
library(genefilter)
library(grid)
library(gridExtra)
library(ggsci)
library(UpSetR)
library(cowplot)

###2. Load following functions:

### all necessary custom functions are in the following script
source("/data/share/htp/prime-seq_Paper/Scripts/custom_functions.R")

theme_pub <- theme_bw() + theme(plot.title = element_text(hjust = 0.5, size=18, face="bold"),
                                     axis.text = element_text(colour="black", size=14), 
                                     axis.title=element_text(size=16,face="bold"), 
                                     legend.text=element_text(size=14),
                                     legend.position="right",
                                     axis.line.x = element_line(colour = "black"), 
                                     axis.line.y = element_line(colour = "black"),
                                     strip.background=element_blank(), 
                                     strip.text=element_text(size=16))  

theme_set(theme_pub)

#prevent scientific notation
options(scipen=999)

fig_path<-"/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/"

###3. Main - Sensitivity figure number of umis

ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/a1.pdf", a1, width = 13, height = 5, units = "in")
Error in ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/a1.pdf",  : 
  could not find function "ggsave"

###4. Supp - Sensitivity figure number of genes


prime_df <- read.csv("prime_seq_projects.csv", header = T, stringsAsFactors = T, sep = ";")
prime_df_long <- pivot_longer(prime_df, 
                              c(Genes_Exon_Only, Genes_Intron_Only, Genes_Both), 
                              values_to = "Number_Genes", 
                              names_to = "Feature")
#relevel factors
prime_df_long$Sample_Type<-factor(prime_df_long$Sample_Type,
                                levels=c("Nervous", "Cardiopulmonary", 
                                         "Digestive/Excretory", 
                                         "Renal/Urinary","Reproductive", 
                                         "Musculoskeletal","Immune","Cell Line",
                                         "PDX","Plant", 
                                         "Plant/Fungus","Fungus"))
prime_df_long$Feature<-factor(prime_df_long$Feature,
                                levels=rev(c("Genes_Both", "Genes_Exon_Only",
                                         "Genes_Intron_Only")))

# plot
a <- ggplot(prime_df_long[prime_df_long$Category == "A",], aes(x = reorder(Project, -Number_Genes), y = Number_Genes, fill = Feature, alpha = Average_reads_per_sample))+
    geom_bar(stat="identity", width = 1)+
    facet_grid(Sample_Type~., scales="free", space = "free", switch = "y") +
    ylab("Number of Genes") +
    coord_flip() +
    ylim(0,30500)+
    scale_fill_manual(labels = c("Intron", "Exon", "Both"), 
                      values = c("#118730", "#1a5084", "gray60"))+
    scale_alpha_continuous(range = c(0.6, 1), guide = FALSE)+
    guides(fill = guide_legend(reverse=T))+
    theme_pub+
    theme(legend.position="none", 
          axis.title.y = element_blank(), 
          axis.text.y = element_blank(),
          strip.text.y.left = element_text(angle = 0, size = 10),
          strip.placement = "outside",
          panel.grid.major = element_blank(), 
          panel.grid.minor.y = element_blank(),
          axis.ticks.y = element_blank()) 

b <- ggplot(prime_df_long[prime_df_long$Category == "B",], aes(x = reorder(Project, -Number_Genes), y = Number_Genes, fill = Feature, alpha = Average_reads_per_sample))+
    geom_bar(stat="identity", width = 1)+
    facet_grid(Sample_Type~., scales="free", space = "free", switch = "y") +
    ylab("Number of Genes") +
    coord_flip() +
    ylim(0,40500)+
    scale_fill_manual(labels = c("Intron", "Exon", "Both"), 
                      values = c("#118730", "#1a5084", "gray60"))+
    scale_alpha_continuous(range = c(0.6, 1), guide = FALSE)+
    guides(fill = guide_legend(reverse=T))+
    theme_pub+
    theme(legend.position="none", 
          axis.title.y = element_blank(), 
          axis.text.y = element_blank(),
          strip.text.y.left = element_text(angle = 0, size = 10),
          strip.placement = "outside",
          panel.grid.major = element_blank(), 
          panel.grid.minor.y = element_blank(),
          axis.ticks.y = element_blank()) 

c <- ggplot(prime_df_long[prime_df_long$Category == "C",], aes(x = reorder(Project, -Number_Genes), y = Number_Genes, fill = Feature, alpha = Average_reads_per_sample))+
    geom_bar(stat="identity", width = 1)+
    facet_grid(Sample_Type~., scales="free", space = "free", switch = "y") +
    ylab("Number of Genes") +
    coord_flip() +
    ylim(0,30500)+
    scale_fill_manual(labels = c("Intron", "Exon", "Both"), 
                      values = c("#118730", "#1a5084", "gray60"))+
    scale_alpha_continuous(range = c(0.6, 1), guide = FALSE)+
    guides(fill = guide_legend(reverse=T))+
    theme_pub+
    theme(legend.position="none", 
          axis.title.y = element_blank(), 
          axis.text.y = element_blank(),
          strip.text.y.left = element_text(angle = 0, size = 10),
          strip.placement = "outside",
          panel.grid.major = element_blank(), 
          panel.grid.minor.y = element_blank(),
          axis.ticks.y = element_blank()) 


ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/a.pdf", a, width = 8, height = 11, units = "in")

ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/b.pdf", b, width = 8, height = 5.97, units = "in")

ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/c.pdf", c, width = 8, height = 2, units = "in")
---
title: "Sensitivity_graphic"
output: html_notebook
---

Performance of prime-seq across many experiments


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```

###1. Load the following packages:

```{r packages}
library(tidyverse)
library(ggsignif)
library(ggplotify)
library(ggrepel)
library(ggbeeswarm)
library(edgeR)
library(genefilter)
library(grid)
library(gridExtra)
library(ggsci)
library(UpSetR)
library(cowplot)

```

###2. Load following functions:


```{r functions}
### all necessary custom functions are in the following script
source("/data/share/htp/prime-seq_Paper/Scripts/custom_functions.R")

theme_pub <- theme_bw() + theme(plot.title = element_text(hjust = 0.5, size=18, face="bold"),
                                     axis.text = element_text(colour="black", size=14), 
                                     axis.title=element_text(size=16,face="bold"), 
                                     legend.text=element_text(size=14),
                                     legend.position="right",
                                     axis.line.x = element_line(colour = "black"), 
                                     axis.line.y = element_line(colour = "black"),
                                     strip.background=element_blank(), 
                                     strip.text=element_text(size=16))  

theme_set(theme_pub)

#prevent scientific notation
options(scipen=999)

fig_path<-"/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/"

```

###3. Main - Sensitivity figure number of umis

```{r }

prime_df <- read.csv("prime_seq_projects.csv", header = T, stringsAsFactors = T, sep = ",")
prime_df_long <- pivot_longer(prime_df, 
                              c(UMI_Exonic, UMI_Intronic), 
                              values_to = "Number_UMIs", 
                              names_to = "Feature")

#relevel factors
prime_df_long$Sample_Type<-factor(prime_df_long$Sample_Type,
                                levels=c("Nervous", "Cardiopulmonary", 
                                         "Digestive", 
                                         "Urinary", 
                                         "Immune","Cancer Cell",
                                         "Pluripotent Cell"))
prime_df_long$Feature<-factor(prime_df_long$Feature,
                                levels=rev(c("UMI_Exonic", "UMI_Intronic")))

# plot
a1 <- ggplot(prime_df_long, aes(x = reorder(Project, -frac_inex_umi), y = Number_UMIs, fill = Feature, alpha = as.numeric(Average_reads_per_sample)))+
    geom_bar(stat="identity", position = "fill")+
    facet_grid(~Sample_Type, scales="free", space = "free", switch = "x") +
    ylab("Fraction of UMIs") +
    scale_fill_manual(labels = c("Intron", "Exon"), 
                      values = c("#118730", "#1a5084"))+
    scale_alpha_continuous(range = c(0.75, 1), guide = FALSE)+
    guides(fill = guide_legend(reverse=T))+
    theme_pub+
    theme(legend.position="none", 
          axis.title.x = element_blank(), 
          axis.text.x = element_blank(),
          strip.text.x = element_text(angle = 90, size = 10),
          strip.placement = "outside",
          panel.grid.major = element_blank(), 
          panel.grid.minor.x = element_blank(),
          axis.ticks.x = element_blank()) 

ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/a1.pdf", a1, width = 10, height = 5, units = "in")

```


###4. Supp - Sensitivity figure number of genes

```{r }

prime_df <- read.csv("prime_seq_projects_gene_num.csv", header = T, stringsAsFactors = T, sep = ",")
prime_df_long <- pivot_longer(prime_df, 
                              c(Genes_Exon_Only, Genes_Intron_Only, Genes_Both), 
                              values_to = "Number_Genes", 
                              names_to = "Feature")

#relevel factors
prime_df_long$Sample_Type<-factor(prime_df_long$Sample_Type,
                                levels=c("Nervous", "Cardiopulmonary", 
                                         "Digestive", 
                                         "Urinary","Reproductive", 
                                         "Musculoskeletal","Immune","Cancer Cell",
                                         "PDX",
                                         "Pluripotent Cell","Plant", 
                                         "Plant/Fungus","Fungus"))
prime_df_long$Feature<-factor(prime_df_long$Feature,
                                levels=rev(c("Genes_Exon_Only", "Genes_Both", 
                                         "Genes_Intron_Only")))

# plot
a <- ggplot(prime_df_long[prime_df_long$Category == "A",], aes(x = reorder(Project, -Number_Genes), y = Number_Genes, fill = Feature, alpha = as.numeric(Average_reads_per_sample)))+
    geom_bar(stat="identity", width = 1)+
    facet_grid(Sample_Type~., scales="free", space = "free", switch = "y") +
    ylab("Number of Genes") +
    coord_flip() +
    ylim(0,30500)+
    scale_fill_manual(labels = c("Intron", "Both", "Exon"), 
                      values = c("#118730", "#5F8A8B", "#1a5084"))+
    scale_alpha_continuous(range = c(0.75, 1), guide = FALSE)+
    guides(fill = guide_legend(reverse=T))+
    theme_pub+
    theme(legend.position="none", 
          axis.title.y = element_blank(), 
          axis.text.y = element_blank(),
          strip.text.y.left = element_text(angle = 0, size = 10),
          strip.placement = "outside",
          panel.grid.major = element_blank(), 
          panel.grid.minor.y = element_blank(),
          axis.ticks.y = element_blank()) 

b <- ggplot(prime_df_long[prime_df_long$Category == "B",], aes(x = reorder(Project, -Number_Genes), y = Number_Genes, fill = Feature, alpha = as.numeric(Average_reads_per_sample)))+
    geom_bar(stat="identity", width = 1)+
    facet_grid(Sample_Type~., scales="free", space = "free", switch = "y") +
    ylab("Number of Genes") +
    coord_flip() +
    ylim(0,40000)+
    scale_fill_manual(labels = c("Intron", "Both", "Exon"), 
                      values = c("#118730", "#5F8A8B", "#1a5084"))+
    scale_alpha_continuous(range = c(0.75, 1), guide = FALSE)+
    guides(fill = guide_legend(reverse=T))+
    theme_pub+
    theme(legend.position="none", 
          axis.title.y = element_blank(), 
          axis.text.y = element_blank(),
          strip.text.y.left = element_text(angle = 0, size = 10),
          strip.placement = "outside",
          panel.grid.major = element_blank(), 
          panel.grid.minor.y = element_blank(),
          axis.ticks.y = element_blank()) 

c <- ggplot(prime_df_long[prime_df_long$Category == "C",], aes(x = reorder(Project, -Number_Genes), y = Number_Genes, fill = Feature, alpha = as.numeric(Average_reads_per_sample)))+
    geom_bar(stat="identity", width = 1)+
    facet_grid(Sample_Type~., scales="free", space = "free", switch = "y") +
    ylab("Number of Genes") +
    coord_flip() +
    ylim(0,30500)+
    scale_fill_manual(labels = c("Intron", "Both", "Exon"), 
                      values = c("#118730", "#5F8A8B", "#1a5084"))+
    scale_alpha_continuous(range = c(0.75, 1), guide = FALSE)+
    guides(fill = guide_legend(reverse=T))+
    theme_pub+
    theme(legend.position="none", 
          axis.title.y = element_blank(), 
          axis.text.y = element_blank(),
          strip.text.y.left = element_text(angle = 0, size = 10),
          strip.placement = "outside",
          panel.grid.major = element_blank(), 
          panel.grid.minor.y = element_blank(),
          axis.ticks.y = element_blank()) 


ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/a.pdf", a, width = 8, height = 11, units = "in")

ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/b.pdf", b, width = 8, height = 5.97, units = "in")

ggsave("/data/share/htp/prime-seq_Paper/Fig_graphic/sensitivity/c.pdf", c, width = 7.9, height = 2, units = "in")



```