---
title: "Getting started with 'SpotSweeper'"
author: 
  - name: Michael Totty
    affiliation: &id1 "Johns Hopkins Bloomberg School of Public Health, 
    Baltimore, MD, USA"
  - name: Boyi Guo
    affiliation: *id1
  - name: Stephanie Hicks
    affiliation: *id1
date: "`r Sys.Date()`"
output: BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{Getting Started with `SpotSweeper`}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```




## Introduction

`SpotSweeper` is an R package for spatial transcriptomics data quality control 
(QC). It provides functions for detecting and visualizing spot-level local 
outliers and artifacts using spatially-aware methods. The package is designed 
to work with [SpatialExperiment](https://github.com/drighelli/SpatialExperiment)
objects, and is compatible with data from 10X Genomics Visium and other spatial 
transcriptomics platforms.

## Installation

Currently, the only way to install `SpotSweeper` is by downloading the 
development version which can be installed from 
[GitHub](https://github.com/MicTott/SpotSweeper) using the following: 

```{r 'install_dev', eval = FALSE}
if (!require("devtools")) install.packages("devtools")
remotes::install_github("MicTott/SpotSweeper")
```

Once accepted in [Bioconductor](http://bioconductor.org/), `SpotSweeper` will be
installable using:

```{r 'install', eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("SpotSweeper")
```

## Spot-level local outlier detection

### Loading example data

Here we'll walk you through the standard workflow for using 'SpotSweeper' to 
detect and visualize local outliers in spatial transcriptomics data. We'll use 
the `Visium_humanDLPFC` dataset from the `STexampleData` package, which is a 
`SpatialExperiment` object.

Because local outliers will be saved in the `colData` of the `SpatialExperiment`
object, we'll first view the `colData` and drop out-of-tissue spots before 
calculating quality control (QC) metrics and running `SpotSweeper`. 


```{r example_spe}
library(SpotSweeper)

# load  Maynard et al DLPFC daatset
spe <- STexampleData::Visium_humanDLPFC()

# show column data before SpotSweeper
colnames(colData(spe))

# drop out-of-tissue spots
spe <- spe[, spe$in_tissue == 1]
```

### Calculating QC metrics using `scuttle`

We'll use the `scuttle` package to calculate QC metrics. To do this, we'll need 
to first change the `rownames` from gene id to gene names. We'll then get the 
mitochondrial transcripts and calculate QC metrics for each spot using 
`scuttle::addPerCellQCMetrics`.

```{r example_scuttle}
# change from gene id to gene names
rownames(spe) <- rowData(spe)$gene_name

# identifying the mitochondrial transcripts
is.mito <- rownames(spe)[grepl("^MT-", rownames(spe))]

# calculating QC metrics for each spot using scuttle
spe <- scuttle::addPerCellQCMetrics(spe, subsets = list(Mito = is.mito))
colnames(colData(spe))
```


### Identifying local outliers using `SpotSweeper`


We can now use `SpotSweeper` to identify local outliers in the spatial 
transcriptomics data. We'll use the `localOutliers` function to detect local 
outliers based on the unique detected genes, total library size, and percent of 
the total reads that are mitochondrial. These methods assume a normal 
distribution, so we'll use the log-transformed sum of the counts and the 
log-transformed number of detected genes. For mitochondrial percent, we'll use 
the raw mitochondrial percentage. 

```{r example_local_outliers}
# library size
spe <- localOutliers(spe,
    metric = "sum",
    direction = "lower",
    log = TRUE
)

# unique genes
spe <- localOutliers(spe,
    metric = "detected",
    direction = "lower",
    log = TRUE
)

# mitochondrial percent
spe <- localOutliers(spe,
    metric = "subsets_Mito_percent",
    direction = "higher",
    log = FALSE
)
```

The `localOutlier` function automatically outputs the results to the `colData` 
with the naming convention `X_outliers`, where `X` is the name of the input 
`colData`. We can then combine all outliers into a single column called 
`local_outliers` in the `colData` of the `SpatialExperiment` object.

```{r example_combine_local_outliers}
# combine all outliers into "local_outliers" column
spe$local_outliers <- as.logical(spe$sum_outliers) |
    as.logical(spe$detected_outliers) |
    as.logical(spe$subsets_Mito_percent_outliers)
```

### Visualizing local outliers

We can visualize the local outliers using the `plotQC` function. This 
function creates a scatter plot of the specified metric and highlights the 
local outliers in red using the `escheR` package. Here, we'll visualize local 
outliers of library size, unique genes, mitochondrial percent, and finally, all
local outliers. We'll then arrange these plots in a grid using 
`ggpubr::arrange`.

```{r local_outlier_plot, fig.width=3, fig.height=3, dpi=100}
library(escheR)


# all local outliers
plotQC(spe, metric = "sum_log", outliers = "local_outliers", point_size = 1.1, 
       stroke = 0.75) +
      ggtitle("All Local Outliers")

```


## Removing technical artifacts using `SpotSweeper`

### Loading example data

```{r example_artifactRemoval}
# load in DLPFC sample with hangnail artifact
data(DLPFC_artifact)
spe <- DLPFC_artifact

# inspect colData before artifact detection
colnames(colData(spe))
```


### Visualizing technical artifacts

Technical artifacts can commonly be visualized by standard QC metrics, including
library size, unique genes, or mitochondrial percentage. We can first visualize
the technical artifacts using the `plotQC` function. In this sample, we can 
clearly see a hangnail artifact on the right side of the tissue section
in the mitochondrial ratio plot.

```{r artifact_QC_plots, fig.width=4, fig.height=4, dpi=100}

plotQC(spe,
    metric = "expr_chrM_ratio",
    outliers = NULL, point_size = 1.1
) +
    ggtitle("Mitochondrial Percent")

```

### Identifying artifacts using `SpotSweeper`

We can then use the `findArtifacts` function to identify artifacts in the 
spatial transcriptomics (data. This function identifies technical artifacts 
based on the first principle component of the local variance of the specified QC
metric (`mito_percent`) at numerous neighorhood sizes (`n_rings=5`). Currently,
`kmeans` clustering is used to cluster the technical artifact vs high-quality 
Visium spots. Similar to `localOutliers`, the `findArtifacts` function then
outputs the results to the `colData`.

```{r artifact_plot}
# find artifacts using SpotSweeper
spe <- findArtifacts(spe,
    mito_percent = "expr_chrM_ratio",
    mito_sum = "expr_chrM",
    n_rings = 5,
    name = "artifact"
)

# check that "artifact" is now in colData
colnames(colData(spe))
```


### Visualizing artifacts

We can visualize the artifacts using the `escheR` package. Here, we'll visualize
the artifacts using the `plotQC` function and arrange these plots using 
`ggpubr::arrange`.

```{r artifact_visualization, fig.width=4, fig.height=4, dpi=100}
plotQC(spe,
    metric = "expr_chrM_ratio",
    outliers = "artifact", point_size = 1.1
) +
    ggtitle("Hangnail artifact")
```
# Session information

```{r}
utils::sessionInfo()
```