SexBreakdown.Rmd

---
title: "Sex breakdown"
output: html_notebook
---

```{r}
plate_metadata_filename = here('00_data_ingest', '00_facs_raw_data', 'metadata_FACS.csv')
  
plate_metadata <- read_csv(plate_metadata_filename)

droplet_metadata_filename = here('00_data_ingest', '01_droplet_raw_data', 'metadata_droplet.csv')
  
droplet_metadata <- read_csv(droplet_metadata_filename)
```

```{r}
plate_metadata
```

```{r}
plate_metadata %>% filter(tissue != 'Heart') %>% group_by(tissue, mouse.id) %>% count() %>% 
  spread(mouse.id, n, fill = 0)
```

Bladder has 3 males and 3 females. Fat has 3 and 4. Colon has 3 and 4. Kidney has 3 and 3.

That's enough to start to ask about batch and gender effects.

```{r}
droplet_metadata %>%  group_by(tissue, mouse.sex) %>% count() %>% 
  spread(mouse.sex, n, fill = 0)
```


Mammary: we have 2 
```{r}
old_facs_anno = read_csv(here('00_data_ingest', '00_facs_raw_data', 'annotations_FACS.csv'))
```



```{r}
tcells <- old_facs_anno %>% filter(cell_ontology_class == 'T cell') %>% pull(cell)
write(tcells, file = "tcells.csv")
```