scrappy

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

Installation

You can install the development version with:

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

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

😄: Great Quality.

🙂: Good quality.

🤢: OK quality.

💩: Low quality.

scrappy can be used on SingleCellExperiment objects as well as Seurat objects.

Example

sce
#> class: SingleCellExperiment 
#> dim: 20006 3005 
#> metadata(0):
#> assays(2): counts logcounts
#> rownames(20006): Tspan12 Tshz1 ... mt-Rnr1 mt-Nd4l
#> rowData names(1): featureType
#> colnames(3005): 1772071015_C02 1772071017_G12 ... 1772066098_A12
#>   1772058148_F03
#> colData names(26): tissue group # ... altexps_repeat_percent total
#> reducedDimNames(2): PCA TSNE
#> spikeNames(0):
#> altExpNames(2): ERCC repeat

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.