In darlanminussi/scrappy: R package for plotting quality of scRNA cells.

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

library(emo)

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:

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 objects as well as Seurat objects.

Example

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)

sce

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.

darlanminussi/scrappy documentation built on Aug. 31, 2020, 12:45 a.m.