1_Discovery.Rmd

---
title: 1_Discovery
output: html_notebook
---

Basic installation procedure (not run)

```{r, eval=FALSE}
natmanager::install('natverse')
vfbconnectr::vc_install()
```

### How to find neurons across datasets

VirtualFlyBrain integrates images and connectomics profiles of neurons from many sources.  It classifies and records their properties using a standard, queryable classification ([The Drosophila Anatomy Ontology](https://www.ebi.ac.uk/ols/ontologies/fbbt)). This standardises the names of neuron types across sources, so you don't need to worry about differences in nomenclature and supports queries for neurons by their classification. 

```{r, message=FALSE}
# load packages
library(vfbconnectr)
library(natverse)
library(dplyr)
```

Set up connections to VFB APIs and VFB CATMAID server.

```{r}
# for latest version of ontology?
# vc = VfbConnect(neo_endpoint="http://pdb.p2.virtualflybrain.org")
vc = VfbConnect()

# Connect to the VFB CATMAID server hosting the FAFB data
catmaid::vfbcatmaid('fafb')
```

### Find neurons by type (classification) across datasets

We can use the `vc$get_instances` method in combination with the name of a neuron type on VFB to find individual neurons from multiple sources.

```{r}
DA3adPN = vc$get_instances("'adult antennal lobe projection neuron DA3 adPN'", summary=T) 
DA3adPN %>%
  vc_df()
```

### Find neurons by location

We can use the same method to search for neurons by location, using simple queries.

```{r}
# Find neurons by location. The following query works across multiple data sources and both sides of the brain.  
# Results may be incomplete & may include minor overlap inferred from low synapse counts

neurons_in_DA3 = vc$get_instances("'neuron' that 'overlaps' some 'antennal lobe glomerulus DA3'", summary=T)
neurons_in_DA3_tab = vc_df(neurons_in_DA3)
neurons_in_DA3_tab
```

```{r}
# Find local interneurons (intrinsic neurons) of the AL, overlapping DA3:

local_in_DA3 = vc$get_instances("'local interneuron of adult antennal lobe' that 'overlaps' some 'antennal lobe glomerulus DA3'",
                                summary=T)
vc_df(local_in_DA3)
```


### Find neurons by dataset/paper

On CATMAID

```{r}
bates = catmaid_get_neuronnames('Paper: Bates and Schlegel et al 2020')
# easier to read as a data.frame
bates %>% as.data.frame()

```

```{r}
# Inspect what datasets are available on VFB
ds = vc$neo_query_wrapper$get_datasets(summary=T) %>% 
  vc_df() %>%
  arrange(id)
ds
```

```{r}
sayin_tab = vc$get_instances_by_dataset('Sayin2019', summary=T) %>% vc_df()
sayin_tab
```
Find connections between neurons by type across datasets. NB: 

1. results are limited by how detailed annotation currently is in the DB.
2. this is a rather slow query 

```{r}
vc$get_connected_neurons_by_type(
  upstream_type = 'GABAergic neuron',
  downstream_type = 'adult descending neuron',
  weight = 10
) %>%
  arrange(desc(weight))
```


This is an interesting related query, but there seem to be speed issues.

```{r, eval=FALSE}
vc$get_connected_neurons_by_type(
  upstream_type = 'antennal lobe projection neuron',
  downstream_type = 'adult descending neuron',
  weight = 10
) %>%
  arrange(desc(weight))
```