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%"
)

library(emo)
```

# scrappy

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scrappy provides an easy way to visualize the quality of scRNA single-cells.


## Installation

You can install the development version with:

``` r
# install.packages("remotes")
remotes::install_github("darlanminussi/scrappy")
```

scrappy uses the percentage of mitochondrial gene expression to classify the cells as:

`r emo::ji("smile")`: Great Quality.

`r emo::ji("slightly_smiling_face")`: Good quality.

`r emo::ji("nauseated")`: OK quality.

`r emo::ji("poop")`: Low quality.

scrappy can be used on [SingleCellExperiment](https://osca.bioconductor.org/) objects as well as [Seurat](https://satijalab.org/seurat/) objects.

## Example

```{r, include = FALSE}
library(scRNAseq)
sce <- ZeiselBrainData()

# Quality control.
library(scater)
is.mito <- grepl("^mt-", rownames(sce))
table(is.mito)
sce <- addPerCellQC(sce, subsets=list(Mito=is.mito))

# Normalization.
sce <- logNormCounts(sce)

# Feature selection.
library(scran)
dec <- modelGeneVar(sce)
hvg <- getTopHVGs(dec, prop=0.1)

# Dimensionality reduction.
set.seed(1234)
sce <- runPCA(sce, ncomponents=25, subset_row=hvg)
sce <- runTSNE(sce, dimred = 'PCA', external_neighbors=TRUE)
```

```{r}
sce
```


```{r example, fig.width=6, fig.height=6, dpi = 300}
library(scrappy)
scrappyPlot(sce, "TSNE")
```

PS: If you want to learn about QC metrics and how to filter your single-cell datasets follow this [link](https://osca.bioconductor.org/quality-control.html#choice-of-qc-metrics).