README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# hitype <a href="https://pwwang.github.io/hitype/"><img src="man/figures/logo.png" align="right" height="139" alt="hitype website" /></a>

<!-- badges: start -->
<!-- badges: end -->

**Hi**erarchical and **hi**gh-resolution cell-type identification for single-cell RNA-seq data inspired by [ScType][1].

## Features

- [x] Compatibility with [ScType][1]
- [x] Hierarchical and high-resolution cell-type identification
- [x] Train weights for your markers with a reference dataset
- [x] Speed optimization
- [x] Support as an R package with unit tests

[1]: https://github.com/IanevskiAleksandr/sc-type

## Installation

You can install the development version of `hitype` like so:

``` r
if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")
}
devtools::install_github("pwwang/hitype")
```

## Quick start

### Prepare the dataset

See also <https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#setup-the-seurat-object>

<details>
  <summary>Click to expand</summary>

```{r, warning=FALSE, message=FALSE}
pbmc <- pbmc3k.SeuratData::pbmc3k
pbmc[["percent.mt"]] <- Seurat::PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- Seurat::NormalizeData(pbmc)
pbmc <- Seurat::FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
pbmc <- Seurat::ScaleData(pbmc, features = rownames(pbmc))
pbmc <- Seurat::RunPCA(pbmc, features = Seurat::VariableFeatures(object = pbmc))
pbmc <- Seurat::FindNeighbors(pbmc, dims = 1:10)
pbmc <- Seurat::FindClusters(pbmc, resolution = 0.5)
pbmc <- Seurat::RunUMAP(pbmc, dims = 1:10)
```
</details>

### Use as a Seurat extension

```{r, warning=FALSE, out.width="75%"}
library(hitype)

# Load gene sets
gs <- gs_prepare(hitypedb_pbmc3k)

# Assign cell types
obj <- RunHitype(pbmc, gs)

Seurat::DimPlot(obj, group.by = "hitype", label = TRUE, label.box = TRUE) +
  Seurat::NoLegend()
```

Compared to the manual marked cell types:
![Seurat manual marked cell types](https://satijalab.org/seurat/articles/pbmc3k_tutorial_files/figure-html/labelplot-1.png){width=75%}

See also <https://satijalab.org/seurat/articles/pbmc3k_tutorial.html#assigning-cell-type-identity-to-clusters>

### Use as standalone functions

```{r, warning=FALSE}
scores <- hitype_score(pbmc@assays$RNA@scale.data, gs, scaled = TRUE)
cell_types <- hitype_assign(pbmc$seurat_clusters, scores, gs)
summary(cell_types)
```

You may see that we have exactly the same assignment in the Seurat tutorial:

|Cluster ID	| Markers | Cell Type |
|:----------|:--------|:----------|
|0|	IL7R, CCR7 |	Naive CD4+ T |
|1|	CD14, LYZ |	CD14+ Mono|
|2|	IL7R, S100A4 | Memory CD4+|
|3|	MS4A1 | B|
|4|	CD8A | CD8+ T|
|5|	FCGR3A, MS4A7 | FCGR3A+ Mono|
|6|	GNLY, NKG7 | NK|
|7|	FCER1A, CST3 | DC|
|8|	PPBP | Platelet|

## Documentation

<https://pwwang.github.io/hitype/>