def integrate(datasets_full, genes_list, batch_size=BATCH_SIZE,
              verbose=VERBOSE, ds_names=None, dimred=DIMRED, approx=APPROX,
              sigma=SIGMA, alpha=ALPHA, knn=KNN, geosketch=False,
              geosketch_max=20000, n_iter=1, union=False, hvg=None):
    """Integrate a list of data sets.

    Parameters
    ----------
    datasets_full : `list` of `scipy.sparse.csr_matrix` or of `numpy.ndarray`
        Data sets to integrate and correct.
    genes_list: `list` of `list` of `string`
        List of genes for each data set.
    batch_size: `int`, optional (default: `5000`)
        The batch size used in the alignment vector computation. Useful when
        correcting very large (>100k samples) data sets. Set to large value
        that runs within available memory.
    verbose: `bool` or `int`, optional (default: 2)
        When `True` or not equal to 0, prints logging output.
    ds_names: `list` of `string`, optional
        When `verbose=True`, reports data set names in logging output.
    dimred: `int`, optional (default: 100)
        Dimensionality of integrated embedding.
    approx: `bool`, optional (default: `True`)
        Use approximate nearest neighbors, greatly speeds up matching runtime.
    sigma: `float`, optional (default: 15)
        Correction smoothing parameter on Gaussian kernel.
    alpha: `float`, optional (default: 0.10)
        Alignment score minimum cutoff.
    knn: `int`, optional (default: 20)
        Number of nearest neighbors to use for matching.
    hvg: `int`, optional (default: None)
        Use this number of top highly variable genes based on dispersion.

    Returns
    -------
    integrated, genes
        Returns a two-tuple containing a list of `numpy.ndarray` with
        integrated low dimensional embeddings and a single list of genes
        containing the intersection of inputted genes.
    """
    datasets_full = check_datasets(datasets_full)

    datasets, genes = merge_datasets(datasets_full, genes_list,
                                     ds_names=ds_names, union=union)
    datasets_dimred, genes = process_data(datasets, genes, hvg=hvg,
                                          dimred=dimred)

    for _ in range(n_iter):
        datasets_dimred = assemble(
            datasets_dimred, # Assemble in low dimensional space.
            verbose=verbose, knn=knn, sigma=sigma, approx=approx,
            alpha=alpha, ds_names=ds_names, batch_size=batch_size,
            geosketch=geosketch, geosketch_max=geosketch_max,
        )

    return datasets_dimred, genes
Integrate a list of data sets.

    Parameters
    ----------
    datasets_full : `list` of `scipy.sparse.csr_matrix` or of `numpy.ndarray`
        Data sets to integrate and correct.
    genes_list: `list` of `list` of `string`
        List of genes for each data set.
    batch_size: `int`, optional (default: `5000`)
        The batch size used in the alignment vector computation. Useful when
        correcting very large (>100k samples) data sets. Set to large value
        that runs within available memory.
    verbose: `bool` or `int`, optional (default: 2)
        When `True` or not equal to 0, prints logging output.
    ds_names: `list` of `string`, optional
        When `verbose=True`, reports data set names in logging output.
    dimred: `int`, optional (default: 100)
        Dimensionality of integrated embedding.
    approx: `bool`, optional (default: `True`)
        Use approximate nearest neighbors, greatly speeds up matching runtime.
    sigma: `float`, optional (default: 15)
        Correction smoothing parameter on Gaussian kernel.
    alpha: `float`, optional (default: 0.10)
        Alignment score minimum cutoff.
    knn: `int`, optional (default: 20)
        Number of nearest neighbors to use for matching.
    hvg: `int`, optional (default: None)
        Use this number of top highly variable genes based on dispersion.

    Returns
    -------
    integrated, genes
        Returns a two-tuple containing a list of `numpy.ndarray` with
        integrated low dimensional embeddings and a single list of genes
        containing the intersection of inputted genes.