Filter cells outliers based on counts and numbers of genes expressed (pp.filter_cells)

For instance, only keep cells with at least min_counts counts or min_genes genes expressed. This is to filter measurement outliers, i.e., "unreliable" observations.

Only provide one of the optional parameters min_counts, min_genes, max_counts, max_genes per call.

More details on the scanpy documentation

Filter genes based on number of cells or counts (pp.filter_genes)

Keep genes that have at least min_counts counts or are expressed in at least min_cells cells or have at most max_counts counts or are expressed in at most max_cells cells.

Only provide one of the optional parameters min_counts, min_cells, max_counts, max_cells per call.

More details on the scanpy documentation

Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories (tl.filter_rank_genes_groups)

More details on the scanpy documentation

Annotate highly variable genes (pp.highly_variable_genes)

It expects logarithmized data.

Depending on flavor, this reproduces the R-implementations of Seurat or Cell Ranger. The normalized dispersion is obtained by scaling with the mean and standard deviation of the dispersions for genes falling into a given bin for mean expression of genes. This means that for each bin of mean expression, highly variable genes are selected.

Subsample to a fraction of the number of observations (pp.subsample)

More details on the scanpy documentation

Downsample counts (pp.downsample_counts)

Downsample counts so that each cell has no more than target_counts. Cells with fewer counts than target_counts are unaffected by this. This has been implemented by M. D. Luecken.

More details on the scanpy documentation