Data_exercise_template.Rmd

---
title: "conception_intro_data"
output: html_document
date: '2023-08-04'
---

# Yoshino 2021 Mouse OSC Data Exercise 

**Name:** Add your name here

## Instructions 

Please clone this repo, create a branch for yourself, and then explore the scRNAseq data from [this](https://www.science.org/doi/epdf/10.1126/science.abe0237) paper. 
<br>

Data is provided in the data directory as an RDS for a monocle3 object holding unprocessed matrices + metadata. Note: to reduce the RDS to within github size limits, we've filtered some non/lowly expressed genes and downsampled the data to 65% of the original cellcount per sample. 
<br>

Please perform the following tasks and present your work product as a knitted RMD (knit to html). <br>
- Preprocess and cluster the data - annotate decisions in the RMD, we'd like to see your process. 
- Roughly label celltypes -- this should include at least granulosa, soma, and germ cells. Can you get any more specific? Are there other additional celltypes? Show plots supporting your celltype labels. 
- How do celltypes compare across ages or across in vivo/in vitro samples? (open ended) 
<br>

Submit by pushing to your code & html files to your branch. 
<br>

We suggest to not spend more than ~3hrs total on this. Feel free to explore beyond the starter questions here. Find anything particularly interesting? Let's discuss! 
<br>

# Load Data and set up the RMD

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

```{r load_packages, include=FALSE}
library(monocle3)
library(ggplot2)

```

```{r load_data, include=FALSE}
cds<-readRDS("data/Yoshino_2021_unprocessed_downsampled.RDS")
```


# QC checks & Filtering

```{r QC_and_filter}


```


# Preprocess & Cluster

```{r preprocess}


```


# Celltype Identification


```{r celltypeID}


```



# Anything else of interest?

```{r play}


```



## SessionInfo
```{r sessionInfo}
sessionInfo()
```