Preprocessing

This module contains helper functions for preparing anndata objects for integration. The most relevant preprocessing steps are:

• normalization

• scaling, batch-aware

• highly variable gene selection, batch-aware

• cell cycle scoring

Functions

hvg_batch(adata[, batch_key, target_genes, ...])	Batch-aware highly variable gene selection
hvg_intersect(adata, batch[, target_genes, ...])	Highly variable gene selection
normalize(adata[, min_mean, log, ...])	Normalise counts using the scran normalisation method
plot_qc(adata[, color, bins, legend_loc, ...])	Create QC Plots
read_conos(inPath[, dir_path])	Read conos object
read_seurat(path)	Read Seurat object from file and convert to anndata object
reduce_data(adata[, batch_key, flavor, ...])	Apply feature selection and dimensionality reduction steps.
save_seurat(adata, path, batch[, hvgs])	Save an anndata object to file as a Seurat object
scale_batch(adata, batch)	Batch-aware scaling of count matrix
score_cell_cycle(adata[, organism])	Score cell cycle score given an organism
summarize_counts(adata[, count_matrix, ...])	Summarise counts of the given count matrix