---
title: "bbknnR Tutorial"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{bbknnR-tutorial}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
date: 'Compiled: `r Sys.Date()`'
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Setup library and data
```{r setup}
library(bbknnR)
library(Seurat)
library(dplyr)
library(patchwork)
data("panc8_small")
```

## Run BBKNN
Note that `RunBBKNN()` also compute t-SNE and UMAP by default.
```{r runbbknn}
panc8_small <- RunBBKNN(panc8_small, batch_key = "tech")
```

## Find Clusters using bbknn graph
```{r clustering}
panc8_small <- FindClusters(panc8_small, graph.name = "RNA_bbknn")
```

## Visualization
```{r umap, fig.width=5, fig.height=10}
p1 <- DimPlot(panc8_small, reduction = "umap", group.by = "celltype", label = TRUE,
             label.size = 3 , repel = TRUE) + NoLegend()
p2 <- DimPlot(panc8_small, reduction = "umap", group.by = "tech")
p3 <- DimPlot(panc8_small, reduction = "umap")

wrap_plots(list(p1, p2, p3), ncol = 1)
```

```{r tsne, fig.width=5, fig.height=10}
p1 <- DimPlot(panc8_small, reduction = "tsne", group.by = "celltype", label = TRUE,
             label.size = 3 , repel = TRUE) + NoLegend()
p2 <- DimPlot(panc8_small, reduction = "tsne", group.by = "tech")
p3 <- DimPlot(panc8_small, reduction = "tsne")

wrap_plots(list(p1, p2, p3), ncol = 1)
```

