simba.pp.filter_cells_rna
- simba.pp.filter_cells_rna(adata, min_n_genes=None, max_n_genes=None, min_pct_genes=None, max_pct_genes=None, min_n_counts=None, max_n_counts=None, expr_cutoff=1)[source]
Filter out cells for RNA-seq based on different metrics.
- Parameters:
adata (AnnData) – Annotated data matrix.
min_n_genes (int, optional (default: None)) – Minimum number of genes expressed
min_pct_genes (float, optional (default: None)) – Minimum percentage of genes expressed
min_n_counts (int, optional (default: None)) – Minimum number of read count for one cell
expr_cutoff (float, optional (default: 1)) – Expression cutoff. If greater than expr_cutoff,the gene is considered ‘expressed’
assay (str, optional (default: ‘rna’)) – Choose from {{‘rna’,’atac’}},case insensitive
- Returns:
updates adata with a subset of cells that pass the filtering.
updates adata with the following fields if cal_qc() was not performed.
n_counts (pandas.Series (adata.obs[‘n_counts’],dtype int)) – The number of read count each cell has.
n_genes (pandas.Series (adata.obs[‘n_genes’],dtype int)) – The number of genes expressed in each cell.
pct_genes (pandas.Series (adata.obs[‘pct_genes’],dtype float)) – The percentage of genes expressed in each cell.
n_peaks (pandas.Series (adata.obs[‘n_peaks’],dtype int)) – The number of peaks expressed in each cell.
pct_peaks (pandas.Series (adata.obs[‘pct_peaks’],dtype int)) – The percentage of peaks expressed in each cell.