---
title: "32-transcript-viz"
output:
  html_document: default
  pdf_document: default
---

```{r}
library(tidyverse)
```


Here I want to explore the best way to visualize the transcript presence data.... eventually this will be integrated with DNA methylation data.

**A comparison of males and females irrespective of treatment**


```{bash}
cd ../data

curl -O https://raw.githubusercontent.com/epigeneticstoocean/2018_L18-adult-methylation/main/analyses/transcript_counts_per_gene_per_sample_males.csv

curl -O https://raw.githubusercontent.com/epigeneticstoocean/2018_L18-adult-methylation/main/analyses/transcript_counts_per_gene_per_sample_females.csv
```

```{r}
male.all <- read.csv("../data/transcript_counts_per_gene_per_sample_males.csv")
```


```{r}
female.all <- read.csv("../data/transcript_counts_per_gene_per_sample_females.csv")

```



```{r}
all <- inner_join(male.all, female.all, by = "gene_name")

```




```{r}
ggplot() +
  geom_histogram(data = male.all, aes(x = male_max_transcript_counts), fill = "blue", alpha = 0.2) +
  geom_histogram(data = female.all, aes(x = female_max_transcript_counts), fill = "green", alpha = 0.2) +
  scale_y_log10()
```



```{r}
all.wstats <- all %>% mutate(diffmean = abs(male_mean_transcript_counts - female_mean_transcript_counts)) %>% 
  mutate(m.covar = male_std_dev_transcript_counts / male_mean_transcript_counts) %>%
  mutate(f.covar = female_std_dev_transcript_counts / female_mean_transcript_counts)
```


```{r}
library("RColorBrewer")
```


```{r}

myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
sc <- scale_colour_gradientn(colours = myPalette(100), limits=c(0, 16))

ggplot(all.wstats, aes(x = male_mean_transcript_counts, y = female_mean_transcript_counts, color = diffmean)) +
  geom_point(alpha = 0.6, size = 3) +
  geom_smooth(method = "lm") +
  geom_abline(size=1.0,colour="red") +
  sc 
  #ylim(0,20) +
  #xlim(0,20)

```

```{r}
model <- lm(female_mean_transcript_counts ~ male_mean_transcript_counts, data = all.wstats)

summary(model)
```



```{r}

myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
sc <- scale_colour_gradientn(colours = myPalette(100), limits=c(0, 16))

ggplot(all.wstats, aes(x = male_median_transcript_counts, y = female_median_transcript_counts, color = diffmean)) +
  geom_point(alpha = 0.6, size = 3) +
  geom_smooth(method = "lm") +
  geom_abline(size=1.0,colour="red") +
  sc 
  #ylim(0,20) +
  #xlim(0,20)

```
```{r}

myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
sc <- scale_colour_gradientn(colours = myPalette(100), limits=c(0, 16))

ggplot(all.wstats, aes(x = m.covar, y = f.covar, color = diffmean)) +
  geom_point(alpha = 0.1, size = 2) +
  geom_smooth(method = "lm") +
  geom_abline(size=1.0,colour="red") +
  sc 
```