annotate_sce: Annotate a SingleCellExperiment With Gene Names and QC...

In combiz/scFlow: A Single-Cell/Nuclei Analysis Toolkit

Annotate a SingleCellExperiment With Gene Names and QC Metrics

Description

Adds biomaRt annotations (e.g. gene, gene_biotype) and QC metric annotations.

Usage

annotate_sce(
  sce,
  min_library_size = 300,
  max_library_size = "adaptive",
  min_features = 100,
  max_features = "adaptive",
  max_mito = "adaptive",
  min_ribo = 0,
  max_ribo = 1,
  min_counts = 2,
  min_cells = 2,
  drop_unmapped = TRUE,
  drop_mito = TRUE,
  drop_ribo = FALSE,
  annotate_genes = TRUE,
  annotate_cells = TRUE,
  nmads = 4,
  ensembl_mapping_file = NULL,
  species = getOption("scflow_species", default = "human")
)

Arguments

sce	a SingleCellExperiment object
min_library_size	the minimum number of counts per cell
max_library_size	the maximum number of counts per cell or "adaptive"
min_features	the minimum number of features per cell (i.e. the minimum number of genes with >0 counts)
max_features	the maximum number of features per cell or "adaptive"
max_mito	the maximum proportion of counts mapping to mitochondrial genes (0 - 1) or "adaptive"
min_ribo	the minimum proportion of counts mapping to ribosomal genes (0 - 1)
max_ribo	the maximum proportion of counts mapping to ribosomal genes (0 - 1)
min_counts	the minimum number of counts per cell in min_cells
min_cells	the minimum number of cells with min_counts
drop_unmapped	set TRUE to remove unmapped ensembl_gene_id
drop_mito	set TRUE to remove mitochondrial genes
drop_ribo	set TRUE to remove ribosomal genes
annotate_genes	optionally skip gene annotation with FALSE
annotate_cells	optionally skip cell annotation with FALSE
nmads	The number of median absolute deviations used to define outliers for adaptive thresholding.
ensembl_mapping_file	a local tsv file with ensembl_gene_id and additional columns for mapping ensembl_gene_id to gene info. If not provided, the biomaRt db is queried (slower).
species	The biological species of the sample.

Value

sce a annotated SingleCellExperiment object

Quality control options and thresholds

In addition to calculating QC metrics and annotating gene information, this function adds boolean (TRUE/FALSE) indicators of which cells/genes met the QC criteria. This enables QC reports, plots, and various QC-related tables to be saved before filtering with the filter_sce() function.

Annotations

With the default settings, the SingleCellExperiment object is annotated with:

Cell-level annotations

• total_counts - sum of counts across all genes

• total_features_by_counts - total number of unique genes with expression >0

• qc_metric_min_library_size - did the cell have at least min_library_size counts

• qc_metric_min_features - did the cell have counts >0 in at least min_features number of cells?

• pc_mito - percentage of counts mapping to mitochondrial genes in this cell

• qc_metric_pc_mito_ok was pc_mito <= the max_mito cutoff?

• pc_ribo - percentage of counts mapping to ribosomal genes in this cell

• qc_metric_pc_ribo_ok was pc_ribo <= the max_ribo cutoff?

• qc_metric_passed - did the cell pass all of the cell QC tests

Gene-level annotations

• gene - official gene name

• gene_biotype - protein_coding, lncRNA, pseudogene, etc.

• qc_metric_ensembl_mapped - was the ensembl_gene_id found in biomaRt

• qc_metric_is_mito - is the gene mitochondrial

• qc_metric_is_ribo - is the gene ribosomal

• qc_metric_n_cells_expressing - number of cells with at least min_counts

• qc_metric_is_expressive - did at least min_cells have min_counts?

See Also

Other annotation functions: .preprocess_seurat_object(), annotate_celltype_metrics(), annotate_integrated_sce(), annotate_merged_sce(), annotate_sce_cells(), annotate_sce_genes(), filter_sce(), find_cells(), find_singlets(), generate_sce(), map_ensembl_gene_id(), merge_sce(), read_metadata(), report_celltype_metrics(), report_celltype_model(), report_merged_sce(), report_qc_sce(), run_doubletfinder(), sce_to_seu()

combiz/scFlow documentation built on Feb. 25, 2024, 10:25 a.m.