---
title: "01-methylation-explore"
output: md_document
date: "2023-02-17"
---

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

Bismark + was done on Hyak.

Genmome stats

```{r}
kt <- read.csv("../data/Pver-karytotype.tab", header = FALSE, sep = "\t")
```

There are `r nrow(kt)` scaffolds

```{r}
nrow(kt)
```

```{r}
ggplot(kt, aes(x = V2)) +
  geom_histogram(bins = 100) +
  scale_x_log10()
```


```{r}
ggplot(kt, aes(x = V2)) +
  geom_histogram(bins = 100) +
  xlim(0, 5000)
```


```{r}
kt %>% filter(V2 < 1000) %>% count()
```





First thing I want to do is do a simple histogram showing distribution of methylation levels. This will likely be done by taking 10 bedgraphs and concatenating then, making a histogram.

After that I would want to look at distribution across features..
