```{r setup, include=FALSE}
library(magrittr)
library(tidyverse)
library(vroom)

library(DESeq2)
library(ggdendro)

knitr::opts_chunk$set(echo = TRUE)
```

## 2021-07-20: Getting Started with RNA-seq by Reading in the Data

Please email me your Rmarkdown document at <kkeith@coriell.org> with your **LAST NAME** in the filename by 9AM Wednesday 7/21. **THIS IS DUE TOMORROW, I NEED TO KNOW YOU CAN READ THE DATA IN BEFORE WE KEEP GOING**

---

### Lecture Questions

1. What is the minimum number of samples necessary for RNA-seq?

*WRITE YOUR ANSWER HERE*

---

2. When would you need to use ribosomal RNA depletion to make an RNA-seq library instead of using poly-A capture?

*WRITE YOUR ANSWER HERE*

---

3. If you have a gene covered by only 5 reads, what should you do with it?

*WRITE YOUR ANSWER HERE*

---

4. What are 3 qualities of RNA you can analyze using RNA-seq data?

*WRITE YOUR ANSWER HERE*

---

5. Give one advantage and one disadvantage of single cell RNA-seq compared to bulk RNA-seq.

*WRITE YOUR ANSWER HERE*

---

<br>

### RNA-seq with Full Data Set

#### Read / Wrangle Data

Taking the data in the zipped folder `full_count_files` which contains the full RNA-seq count tables for the human genome (not just the chr21 files we were using for the demo) and use the chunk below to read it in

```{r}
### use this chunk to read the files in
```

Use this chunk to transform the count tables we just read in into a DESeq object.

```{r}
### make the data into a DESeq object
```

Finally, in the last wrangling step, filter the data. Make sure to remove **BOTH** rows that only have zero counts and to filter for genes where at least 3 samples have a count of 10 transcripts or more

```{r}
### filter the object
```

<br>

















