Preprocessing: pp

Filtering of highly-variable genes, batch-effect correction, per-cell normalization, preprocessing recipes.

Any transformation of the data matrix that is not a tool. Other than tools, preprocessing steps usually don’t return an easily interpretable annotation, but perform a basic transformation on the data matrix.

Basic Preprocessing

For visual quality control, see highest_expr_genes() and filter_genes_dispersion() in scanpy.pl.

pp.calculate_qc_metrics	Calculate quality control metrics.
pp.filter_cells	Filter cell outliers based on counts and numbers of genes expressed.
pp.filter_genes	Filter genes based on number of cells or counts.
pp.highly_variable_genes	Annotate highly variable genes [Satija et al., 2015, Stuart et al., 2019, Zheng et al., 2017].
pp.log1p	Logarithmize the data matrix.
pp.pca	Principal component analysis [Pedregosa et al., 2011].
pp.normalize_total	Normalize counts per cell.
pp.regress_out	Regress out (mostly) unwanted sources of variation.
pp.scale	Scale data to unit variance and zero mean.
pp.subsample	Subsample to a fraction of the number of observations.
pp.downsample_counts	Downsample counts from count matrix.

Recipes

pp.recipe_zheng17	Normalization and filtering as of Zheng et al. [2017].
pp.recipe_weinreb17	Normalization and filtering as of [Weinreb et al., 2017].
pp.recipe_seurat	Normalization and filtering as of Seurat [Satija et al., 2015].

Batch effect correction

Also see [Data integration]. Note that a simple batch correction method is available via pp.regress_out(). Checkout scanpy.external for more.

pp.combat

ComBat function for batch effect correction [Johnson et al., 2006, Leek et al., 2017, Pedersen, 2012].

Doublet detection

pp.scrublet	Predict doublets using Scrublet [Wolock et al., 2019].
pp.scrublet_simulate_doublets	Simulate doublets by adding the counts of random observed transcriptome pairs.

Neighbors

pp.neighbors

Computes the nearest neighbors distance matrix and a neighborhood graph of observations [McInnes et al., 2018].