calculate_QC_metrics: Calcuate QC metrics from gene count matrix

In scPipe: pipeline for single cell RNA-seq data analysis

Description

Calcuate QC metrics from gene count matrix

Usage

calculate_QC_metrics(sce)

Arguments

sce	a SingleCellExperiment object containing gene counts

Details

get QC metrics using gene count matrix. The QC statistics added are

• number_of_genes number of genes detected.

• total_count_per_cell sum of read number after UMI deduplication.

• non_mt_percent 1 - percentage of mitochondrial gene counts. Mitochondrial genes are retrived by GO term GO:0005739

• non_ERCC_percent ratio of exon counts to ERCC counts

• non_ribo_percent 1 - percentage of ribosomal gene counts ribosomal genes are retrived by GO term GO:0005840.

Value

an SingleCellExperiment with updated QC metrics

Examples

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
demultiplex_info(sce) = cell_barcode_matching
UMI_dup_info(sce) = UMI_duplication

# The sample qc data already run through function `calculate_QC_metrics`.
# So we delete these columns and run `calculate_QC_metrics` to get them again:
colnames(colnames(QC_metrics(sce)))
QC_metrics(sce) = QC_metrics(sce)[,c("unaligned","aligned_unmapped","mapped_to_exon")]
sce = calculate_QC_metrics(sce)
colnames(QC_metrics(sce))

scPipe documentation built on Nov. 8, 2020, 8:28 p.m.