sc_sample_qc: quality control information for a small sample scRNA-seq...
In scPipe: pipeline for single cell RNA-seq data analysis

Description Usage Format Value Author(s) Source Examples

This data.frame contains cell quality control information for the 100 cells. For each cell it has:

unaligned the number of unaligned reads.
aligned_unmapped the number of reads that aligned to genome but fail to map to any features.
mapped_to_exon is the number of reads that mapped to exon.
mapped_to_intron is the number of reads that mapped to intron.
ambiguous_mapping is the number of reads that mapped to multiple features. They are not considered in the following analysis.
mapped_to_ERCC is the number of reads that mapped to ERCC spike-in controls.
mapped_to_MT is the number of reads that mapped to mitochondrial genes.
total_count_per_cell is the number of reads that mapped to exon after UMI deduplication. In contrast, 'mapped_to_exon' is the number of reads mapped to exon before UMI deduplication.
number_of_genes is the number of genes detected for each cells
non_ERCC_percent is 1 - (percentage of ERCC reads). Reads are UMI deduplicated.
non_mt_percent is 1 - (percentage of mitochondrial reads). Reads are UMI deduplicated.
non_ribo_percent is 1- (percentage of ribosomal reads). Reads are UMI deduplicated.

1	sc_sample_qc

a data.frame instance, one row per cell.

NULL, but makes a data frame with cell quality control data.frame

Luyi Tian

Christin Biben (WEHI). She FACS sorted cells from several immune cell types including B cells, granulocyte and some early progenitors.

data("sc_sample_data")
data("sc_sample_qc")
sce = SingleCellExperiment(assays = list(counts = as.matrix(sc_sample_data)))
organism(sce) = "mmusculus_gene_ensembl"
gene_id_type(sce) = "ensembl_gene_id"
QC_metrics(sce) = sc_sample_qc
head(QC_metrics(sce))
plot_mapping(sce,percentage=TRUE,dataname="sc_sample")