# Preprocessing: `pp`

```{eval-rst}
.. module:: scanpy.pp
```

```{eval-rst}
.. currentmodule:: scanpy
```

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 {func}`~scanpy.pl.highest_expr_genes` and
{func}`~scanpy.pl.highly_variable_genes` in {mod}`scanpy.pl`.

```{eval-rst}
.. autosummary::
   :nosignatures:
   :toctree: generated/

   pp.calculate_qc_metrics
   pp.filter_cells
   pp.filter_genes
   pp.highly_variable_genes
   pp.log1p
   pp.pca
   pp.normalize_total
   pp.regress_out
   pp.scale
   pp.sample
   pp.downsample_counts
```

## Recipes

```{eval-rst}
.. autosummary::
   :nosignatures:
   :toctree: generated/

   pp.recipe_zheng17
   pp.recipe_seurat
```

(pp-data-integration)=

## Data integration

Batch effect correction and other data integration.
Note that a simple batch correction method is available via {func}`pp.regress_out`.

```{eval-rst}
.. autosummary::
   :nosignatures:
   :toctree: generated/

   pp.combat
   pp.harmony_integrate
```

Batches can also be integrated at the level of the neighbor graph using {func}`pp.bbknn`.
Also see {ref}`data integration tools <data-integration>` and external {ref}`external data integration <external-data-integration>`.

## Doublet detection

```{eval-rst}
.. autosummary::
   :nosignatures:
   :toctree: generated/

   pp.scrublet
   pp.scrublet_simulate_doublets
```

## Sample demultiplexing

```{eval-rst}
.. autosummary::
   :nosignatures:
   :toctree: generated/

   pp.hashsolo
```

## Neighbors

```{eval-rst}
.. autosummary::
   :nosignatures:
   :toctree: generated/

   pp.bbknn
   pp.neighbors

```