cortecon.Rmd

---
title: "--"
author: "rbarreiro"
date: "--"
output: 
  html_document: 
    theme: journal
    css: rmark.css
---
```{r setup, include=FALSE}
suppressMessages({
  library(tidyverse)
  library(scales)
  library(cowplot)
  library(ggbeeswarm)
  library(ggsci)
  library(viridis)
  library(knitr)
  library(ggpubr)
  library(ggrepel)
  library(eulerr)
  library(hrbrthemes)
  library(ggtext)
})

theme_set(
  theme_ipsum_rc(grid = 'Y') +
    theme(
      plot.title.position = "plot",
      plot.margin = margin(25,25,25,25),
      axis.title.x = element_markdown(hjust = .5, size = 12, margin = margin(10,0,0,0)),
      axis.title.y = element_markdown(hjust = .5, size = 12, margin = margin(0,10,0,0)),
      plot.subtitle = element_markdown(family = 'Roboto Condensed', color = 'grey60')
    )
)

options(scipen=15000000)

my_color_pal <- c(
  UP=ggsci::pal_jco()(4)[1],
  DOWN=ggsci::pal_jco()(4)[4]
)

```


```{r}
load('Cortecon_Base.RData')

my_data <-
  DEseq.genes %>% 
  as.data.frame() %>%
  select(-baseMean, -log2FoldChange,-lfcSE, -pvalue	,-padj) %>%
  rownames_to_column('geneid') %>%
  left_join(
    tibble(geneid = names(SYMBOLS), genesymbol = SYMBOLS), by = "geneid") %>% 
  filter(genesymbol %in% c('MAGOH','MAGOHB')) %>% 
  pivot_longer(
    names_to = "sample", values_to = "counts", -c(geneid, genesymbol)) %>% 
  separate(sample, into = c('day','sample'), sep = '\\.') %>%
  mutate(day = as.integer(str_replace(day,"D",''))) %>%
  group_by(day, genesymbol) %>%
  summarise(norm_counts = mean(counts), sd = sd(counts))

my_data %>%  
  ggplot(aes(x = day, y = norm_counts, color = genesymbol)) +
  geom_point() +
  geom_errorbar(
    aes(ymin = norm_counts-sd, ymax = norm_counts + sd), width = .8) +
  geom_line() +
  labs(
    x = '**Time** (days)',
    y = '**Normalized counts** (DESeq)',
    color = NULL
  ) +
  ggsci::scale_color_jco() 
```

```{r}
my_data <-
  edgeR.genes %>% 
  as.data.frame() %>%
  select(-c("FoldDiff","logFC","logCPM","LR","PValue","fdr")) %>%
  rownames_to_column('geneid') %>%
  left_join(
    tibble(geneid = names(SYMBOLS), genesymbol = SYMBOLS), by = "geneid") %>% 
  filter(genesymbol %in% c('MAGOH','MAGOHB')) %>% 
  pivot_longer(
    names_to = "sample", values_to = "counts", -c(geneid, genesymbol)) %>% 
  separate(sample, into = c('day','sample'), sep = '\\.') %>%
  mutate(day = as.integer(str_replace(day,"D",''))) %>%
  group_by(day, genesymbol) %>%
  summarise(norm_counts = mean(counts), sd = sd(counts))

my_data %>%  
  ggplot(aes(x = day, y = norm_counts, color = genesymbol)) +
  geom_point() +
  geom_errorbar(
    aes(ymin = norm_counts-sd, ymax = norm_counts + sd), width = .8) +
  geom_line() +
  labs(
    x = '**Time** (days)',
    y = '**Normalized counts** (edgeR)',
    color = NULL
  ) +
  ggsci::scale_color_jco()

```

```{r}
my_data <-
  norm.RNA.cts %>% 
  as.data.frame() %>%
  rownames_to_column('geneid') %>%
  left_join(
    tibble(geneid = names(SYMBOLS), genesymbol = SYMBOLS), by = "geneid") %>% 
  filter(genesymbol %in% c('MAGOH','MAGOHB')) %>% 
  pivot_longer(
    names_to = "sample", values_to = "counts", -c(geneid, genesymbol)) %>% 
  separate(sample, into = c('day','sample'), sep = '\\.') %>%
  mutate(day = as.integer(str_replace(day,"D",''))) %>%
  group_by(day, genesymbol) %>%
  summarise(norm_counts = mean(counts), sd = sd(counts))

my_data %>%  
  ggplot(aes(x = day, y = norm_counts, color = genesymbol)) +
  geom_point() +
  geom_errorbar(
    aes(ymin = norm_counts-sd, ymax = norm_counts + sd), width = .8) +
  geom_line() +
  labs(
    x = '**Time** (days)',
    y = '**Normalized counts** (?)',
    color = NULL
  ) +
  ggsci::scale_color_jco()
```

```{r}
my_data <-
  RNA.raw %>% 
  as.data.frame() %>%
  rownames_to_column('geneid') %>%
  left_join(
    tibble(geneid = names(SYMBOLS), genesymbol = SYMBOLS), by = "geneid") %>% 
  filter(genesymbol %in% c('MAGOH','MAGOHB')) %>% 
  pivot_longer(
    names_to = "sample", values_to = "counts", -c(geneid, genesymbol)) %>% 
  separate(sample, into = c('day','sample'), sep = '\\.') %>%
  mutate(day = as.integer(str_replace(day,"D",''))) %>%
  group_by(day, genesymbol) %>%
  summarise(norm_counts = mean(counts), sd = sd(counts))

my_data %>%  
  ggplot(aes(x = day, y = norm_counts, color = genesymbol)) +
  geom_point() +
  geom_errorbar(
    aes(ymin = norm_counts-sd, ymax = norm_counts + sd), width = .8) +
  geom_line() +
  labs(
    x = '**Time** (days)',
    y = '**Raw counts**',
    color = NULL
  ) +
  ggsci::scale_color_jco()
```