Calcuate QC metrics from gene count matrix
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.
SingleCellExperiment with updated QC metrics
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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))
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