diff --git a/.gitignore b/.gitignore
index 3a314b92..fe768acf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,7 @@ __pycache__/
 .Python
 build/
 develop-eggs/
+assets/
 dist/
 downloads/
 eggs/
diff --git a/data/pbmc3k_processed.h5ad b/data/pbmc3k_processed.h5ad
new file mode 100644
index 00000000..e529a6d8
Binary files /dev/null and b/data/pbmc3k_processed.h5ad differ
diff --git a/data/pbmc3k_raw.h5ad b/data/pbmc3k_raw.h5ad
new file mode 100644
index 00000000..460ee0b6
Binary files /dev/null and b/data/pbmc3k_raw.h5ad differ
diff --git a/rapids_singlecell/__init__.py b/rapids_singlecell/__init__.py
index f986277d..a07cfe71 100755
--- a/rapids_singlecell/__init__.py
+++ b/rapids_singlecell/__init__.py
@@ -5,4 +5,4 @@
 from . import pl
 from . import gr
 
-__version__ = "0.6.2"
+__version__ = "0.6.3"
diff --git a/rapids_singlecell/cunnData/__init__.py b/rapids_singlecell/cunnData/__init__.py
index f4b6d825..c926676e 100644
--- a/rapids_singlecell/cunnData/__init__.py
+++ b/rapids_singlecell/cunnData/__init__.py
@@ -31,6 +31,15 @@ def update_shape(self, shape):
 
     def __setitem__(self, key, item):
         if self.shape == item.shape:
+            if issparse_gpu(item):
+                item = item
+            elif isinstance(item, cp.ndarray):
+                item = item
+            elif not issparse_cpu(item):
+                inter = sparse.csr_matrix(item)
+                item = cp.sparse.csr_matrix(inter, dtype=cp.float32)
+            else:
+                item = cp.sparse.csr_matrix(item, dtype=cp.float32)
             if issparse_gpu(item):
                 if item.nnz > 2**31 - 1:
                     raise ValueError(
@@ -103,6 +112,7 @@ def __init__(
         obsm: Optional[Mapping[str, Any]] = None,
         varm: Optional[Mapping[str, Any]] = None,
     ):
+        # Initialize from adata
         if adata:
             if not issparse_cpu(adata.X):
                 inter = sparse.csr_matrix(adata.X)
@@ -119,12 +129,7 @@ def __init__(
             self.raw = None
             if adata.layers:
                 for key, matrix in adata.layers.items():
-                    if not issparse_cpu(matrix):
-                        inter = sparse.csr_matrix(matrix)
-                        inter = cp.sparse.csr_matrix(inter, dtype=cp.float32)
-                    else:
-                        inter = cp.sparse.csr_matrix(matrix, dtype=cp.float32)
-                    self._layers[key] = inter
+                    self._layers[key] = matrix
             if adata.obsm:
                 for key, matrix in adata.obsm.items():
                     self._obsm[key] = matrix.copy()
@@ -132,6 +137,7 @@ def __init__(
                 for key, matrix in adata.varm.items():
                     self._varm[key] = matrix.copy()
 
+        # Initialize from items
         else:
             if issparse_gpu(X):
                 self._X = X
@@ -143,11 +149,11 @@ def __init__(
                 del inter
             else:
                 self._X = cp.sparse.csr_matrix(X, dtype=cp.float32)
-            if obs:
+            if obs is not None:
                 self._obs = obs
             else:
                 self._obs = pd.DataFrame(index=range(self.shape[0]))
-            if var:
+            if var is not None:
                 self._var = var
             else:
                 self._var = pd.DataFrame(index=range(self.shape[1]))
@@ -162,16 +168,7 @@ def __init__(
 
             if layers:
                 for key, matrix in layers.items():
-                    if issparse_gpu(matrix):
-                        inter = matrix
-                    elif isinstance(matrix, cp.ndarray):
-                        inter = matrix
-                    elif not issparse_cpu(X):
-                        inter = sparse.csr_matrix(matrix)
-                        inter = cp.sparse.csr_matrix(inter, dtype=cp.float32)
-                    else:
-                        inter = cp.sparse.csr_matrix(matrix, dtype=cp.float32)
-                    self.layers[key] = inter
+                    self.layers[key] = matrix
             if obsm:
                 for key, matrix in obsm.items():
                     self.obsm[key] = matrix.copy()
diff --git a/rapids_singlecell/cunnData_funcs/_hvg.py b/rapids_singlecell/cunnData_funcs/_hvg.py
index e25961bd..e61b8e79 100644
--- a/rapids_singlecell/cunnData_funcs/_hvg.py
+++ b/rapids_singlecell/cunnData_funcs/_hvg.py
@@ -164,10 +164,9 @@ def highly_variable_genes(
             batches = cudata.obs[batch_key].cat.categories
             df = []
             genes = cudata.var.index.to_numpy()
+            X = cudata.layers[layer] if layer is not None else cudata.X
             for batch in batches:
-                inter_matrix = cudata.X[
-                    np.where(cudata.obs[batch_key] == batch)[0],
-                ].tocsc()
+                inter_matrix = X[np.where(cudata.obs[batch_key] == batch)[0],].tocsc()
                 thr_org = cp.diff(inter_matrix.indptr).ravel()
                 thr = cp.where(thr_org >= 1)[0]
                 thr_2 = cp.where(thr_org < 1)[0]
@@ -336,6 +335,12 @@ def _highly_variable_genes_single_batch(
         ].sort()  # interestingly, np.argpartition is slightly slower
         if n_top_genes > X.shape[1]:
             n_top_genes = X.shape[1]
+        if n_top_genes > dispersion_norm.size:
+            warnings.warn(
+                "`n_top_genes` > number of normalized dispersions, returning all genes with normalized dispersions.",
+                UserWarning,
+            )
+            n_top_genes = dispersion_norm.size
         disp_cut_off = dispersion_norm[n_top_genes - 1]
         gene_subset = np.nan_to_num(df["dispersions_norm"].values) >= disp_cut_off
     else:
@@ -448,6 +453,7 @@ def _highly_variable_genes_seurat_v3(
     ranked_norm_gene_vars = ranked_norm_gene_vars.get()
     ma_ranked = np.ma.masked_invalid(ranked_norm_gene_vars)
     median_ranked = np.ma.median(ma_ranked, axis=0).filled(np.nan)
+
     df["highly_variable_nbatches"] = num_batches_high_var.get()
     df["highly_variable_rank"] = median_ranked
     df["variances_norm"] = cp.mean(norm_gene_vars, axis=0).get()
@@ -598,6 +604,7 @@ def _highly_variable_pearson_residuals(
         if clip < 0:
             raise ValueError("Pearson residuals require `clip>=0` or `clip=None`.")
 
+        clip = cp.array([clip], dtype=cp.float32)
         theta = cp.array([theta], dtype=cp.float32)
         sums_genes = cp.zeros(X_batch.shape[1], dtype=cp.float32)
         sums_cells = cp.zeros(X_batch.shape[0], dtype=cp.float32)
diff --git a/rapids_singlecell/cunnData_funcs/_normalize.py b/rapids_singlecell/cunnData_funcs/_normalize.py
index 52a2d7bf..1601a9f3 100644
--- a/rapids_singlecell/cunnData_funcs/_normalize.py
+++ b/rapids_singlecell/cunnData_funcs/_normalize.py
@@ -323,6 +323,8 @@ def normalize_pearson_residuals(
             "`flavor='pearson_residuals'` expects raw count data, but non-integers were found.",
             UserWarning,
         )
+    computed_on = layer if layer else "cudata.X"
+    settings_dict = dict(theta=theta, clip=clip, computed_on=computed_on)
     if theta <= 0:
         raise ValueError("Pearson residuals require theta > 0")
     if clip is None:
@@ -331,6 +333,7 @@ def normalize_pearson_residuals(
     if clip < 0:
         raise ValueError("Pearson residuals require `clip>=0` or `clip=None`.")
     theta = cp.array([1 / theta], dtype=cp.float32)
+    clip = cp.array([clip], dtype=cp.float32)
     sums_cells = cp.zeros(X.shape[0], dtype=cp.float32)
     sums_genes = cp.zeros(X.shape[1], dtype=cp.float32)
 
@@ -420,7 +423,9 @@ def normalize_pearson_residuals(
                 residuals.shape[1],
             ),
         )
+
     if inplace == True:
+        cudata.uns["pearson_residuals_normalization"] = settings_dict
         if layer:
             cudata.layers[layer] = residuals
         else:
diff --git a/rapids_singlecell/cunnData_funcs/_pca.py b/rapids_singlecell/cunnData_funcs/_pca.py
index 798e6011..8cc7561a 100644
--- a/rapids_singlecell/cunnData_funcs/_pca.py
+++ b/rapids_singlecell/cunnData_funcs/_pca.py
@@ -1,7 +1,8 @@
 from ..cunnData import cunnData
+from anndata import AnnData
 
 from cuml.decomposition import PCA, TruncatedSVD
-from typing import Optional
+from typing import Optional, Union
 
 from cupy.sparse import issparse
 import math
@@ -9,10 +10,11 @@
 
 
 def pca(
-    cudata: cunnData,
+    cudata: Union[cunnData, AnnData],
     layer: str = None,
-    n_comps: int = 50,
+    n_comps: Optional[int] = None,
     zero_center: bool = True,
+    random_state: Union[int, None] = 0,
     use_highly_variable: Optional[bool] = None,
     chunked: bool = False,
     chunk_size: int = None,
@@ -23,18 +25,22 @@ def pca(
     Parameters
     ----------
         cudata :
-            cunnData object
+            cunnData, AnnData object
 
         layer
             If provided, use `cudata.layers[layer]` for expression values instead of `cudata.X`.
 
         n_comps
-            Number of principal components to compute. Defaults to 50
+            Number of principal components to compute. Defaults to 50, or 1 - minimum
+            dimension size of selected representation
 
         zero_center
             If `True`, compute standard PCA from covariance matrix.
             If `False`, omit zero-centering variables
 
+        random_state
+            Change to use different initial states for the optimization.
+
         use_highly_variable
             Whether to use highly variable genes only, stored in
             `.var['highly_variable']`.
@@ -78,6 +84,13 @@ def pca(
     if use_highly_variable:
         X = X[:, cudata.var["highly_variable"]]
 
+    if n_comps is None:
+        min_dim = min(X.shape[0], X.shape[1])
+        if 50 >= min_dim:
+            n_comps = min_dim - 1
+        else:
+            n_comps = 50
+
     if chunked:
         from cuml.decomposition import IncrementalPCA
 
@@ -97,11 +110,15 @@ def pca(
             X_pca[start_idx:stop_idx] = pca_func.transform(chunk)
     else:
         if zero_center:
-            pca_func = PCA(n_components=n_comps, output_type="numpy")
+            pca_func = PCA(
+                n_components=n_comps, random_state=random_state, output_type="numpy"
+            )
             X_pca = pca_func.fit_transform(X)
 
         elif not zero_center:
-            pca_func = TruncatedSVD(n_components=n_comps, output_type="numpy")
+            pca_func = TruncatedSVD(
+                n_components=n_comps, random_state=random_state, output_type="numpy"
+            )
             X_pca = pca_func.fit_transform(X)
 
     cudata.obsm["X_pca"] = X_pca
diff --git a/rapids_singlecell/cunnData_funcs/_scale.py b/rapids_singlecell/cunnData_funcs/_scale.py
index 3a5de03d..4e5e1657 100644
--- a/rapids_singlecell/cunnData_funcs/_scale.py
+++ b/rapids_singlecell/cunnData_funcs/_scale.py
@@ -5,7 +5,7 @@
 
 def scale(
     cudata: cunnData,
-    max_value: int = 10,
+    max_value: Optional[int] = None,
     layer: Optional[str] = None,
     inplace: bool = True,
 ) -> Optional[cp.ndarray]:
@@ -45,7 +45,8 @@ def scale(
     stddev = cp.sqrt(X.var(axis=0))
     X /= stddev
     del stddev
-    X = cp.clip(X, a_max=max_value)
+    if max_value:
+        X = cp.clip(X, a_max=max_value)
     if inplace:
         if layer:
             cudata.layers[layer] = X
diff --git a/rapids_singlecell/cunnData_funcs/_simple.py b/rapids_singlecell/cunnData_funcs/_simple.py
index 291c3a5c..da8d9d86 100644
--- a/rapids_singlecell/cunnData_funcs/_simple.py
+++ b/rapids_singlecell/cunnData_funcs/_simple.py
@@ -291,7 +291,7 @@ def calculate_qc_metrics(
             qc_vars = [qc_vars]
         for qc_var in qc_vars:
             sums_cells_sub = cp.zeros(X.shape[0], dtype=cp.float32)
-            mask = cp.array(cudata.var["MT"], dtype=cp.bool_)
+            mask = cp.array(cudata.var[qc_var], dtype=cp.bool_)
             if cp.sparse.issparse(X):
                 if cpx.scipy.sparse.isspmatrix_csr(X):
                     block = (32,)
diff --git a/rapids_singlecell/cunnData_funcs/_utils.py b/rapids_singlecell/cunnData_funcs/_utils.py
index 5d47cc65..f6a8bdea 100644
--- a/rapids_singlecell/cunnData_funcs/_utils.py
+++ b/rapids_singlecell/cunnData_funcs/_utils.py
@@ -98,7 +98,7 @@ def _check_nonnegative_integers(X):
     if issparse(X):
         data = X.data
     else:
-        data = data
+        data = X
     """Checks values of data to ensure it is count data"""
     # Check no negatives
     if cp.signbit(data).any():
diff --git a/rapids_singlecell/scanpy_gpu/__init__.py b/rapids_singlecell/scanpy_gpu/__init__.py
index 19c4c907..58916ba8 100644
--- a/rapids_singlecell/scanpy_gpu/__init__.py
+++ b/rapids_singlecell/scanpy_gpu/__init__.py
@@ -3,7 +3,7 @@
 from ._draw_graph import draw_graph
 from ._embedding_density import embedding_density
 from ._harmony_integrate import harmony_integrate
-from ._pca import pca
+from ..pp import pca
 from ._pymde import mde
 from ._rank_gene_groups import rank_genes_groups_logreg
 from ._tsne import tsne
diff --git a/rapids_singlecell/scanpy_gpu/_pca.py b/rapids_singlecell/scanpy_gpu/_pca.py
deleted file mode 100644
index 695cafc5..00000000
--- a/rapids_singlecell/scanpy_gpu/_pca.py
+++ /dev/null
@@ -1,121 +0,0 @@
-from cuml.decomposition import PCA, TruncatedSVD
-from anndata import AnnData
-from typing import Optional
-
-from scipy.sparse import issparse
-import warnings
-import math
-import numpy as np
-
-
-def pca(
-    adata: AnnData,
-    layer: str = None,
-    n_comps: int = 50,
-    zero_center: bool = True,
-    use_highly_variable: Optional[bool] = None,
-    chunked: bool = False,
-    chunk_size: int = None,
-) -> None:
-    """
-    Performs PCA using the cuML decomposition function for the :class:`~anndata.AnnData` object.
-
-    Parameters
-    ----------
-        adata
-            annData object
-
-        layer
-            If provided, use `adata.layers[layer]` for expression values instead of `adata.X`.
-
-        n_comps
-            Number of principal components to compute. Defaults to 50
-
-        zero_center
-            If `True`, compute standard PCA from covariance matrix.
-            If `False`, omit zero-centering variables
-
-        use_highly_variable
-            Whether to use highly variable genes only, stored in
-            `.var['highly_variable']`.
-            By default uses them if they have been determined beforehand.
-
-        chunked
-            If `True`, perform an incremental PCA on segments of `chunk_size`.
-            The incremental PCA automatically zero centers and ignores settings of
-            `random_seed` and `svd_solver`. If `False`, perform a full PCA.
-
-        chunk_size
-            Number of observations to include in each chunk.
-            Required if `chunked=True` was passed.
-
-    Returns
-    --------
-        adds fields to `adata`:
-
-            `.obsm['X_pca']`
-                    PCA representation of data.
-            `.varm['PCs']`
-                    The principal components containing the loadings.
-            `.uns['pca']['variance_ratio']`
-                    Ratio of explained variance.
-            `.uns['pca']['variance']`
-                    Explained variance, equivalent to the eigenvalues of the
-                    covariance matrix.
-    """
-
-    if use_highly_variable is True and "highly_variable" not in adata.var.keys():
-        raise ValueError(
-            "Did not find adata.var['highly_variable']. "
-            "Either your data already only consists of highly-variable genes "
-            "or consider running `highly_variable_genes` first."
-        )
-
-    X = adata.layers[layer] if layer is not None else adata.X
-
-    if use_highly_variable is None:
-        use_highly_variable = True if "highly_variable" in adata.var.keys() else False
-
-    if use_highly_variable:
-        X = X[:, adata.var["highly_variable"]]
-
-    if issparse(X) and zero_center:
-        warnings.warn(
-            "Your Countmatrix seems to be sparse, this can lead to a massive performance penalty.",
-            UserWarning,
-        )
-
-    if chunked:
-        from cuml.decomposition import IncrementalPCA
-
-        X_pca = np.zeros((X.shape[0], n_comps), X.dtype)
-
-        pca_func = IncrementalPCA(
-            n_components=n_comps, output_type="numpy", batch_size=chunk_size
-        )
-        pca_func.fit(X)
-
-        n_batches = math.ceil(X.shape[0] / chunk_size)
-        for batch in range(n_batches):
-            start_idx = batch * chunk_size
-            stop_idx = min(batch * chunk_size + chunk_size, X.shape[0])
-            chunk = X[start_idx:stop_idx, :]
-            chunk = chunk.toarray() if issparse(chunk) else chunk
-            X_pca[start_idx:stop_idx] = pca_func.transform(chunk)
-    elif zero_center:
-        pca_func = PCA(n_components=n_comps, output_type="numpy")
-        X_pca = pca_func.fit_transform(X)
-
-    elif not zero_center:
-        pca_func = TruncatedSVD(n_components=n_comps, output_type="numpy")
-        X_pca = pca_func.fit_transform(X)
-    adata.obsm["X_pca"] = X_pca
-    adata.uns["pca"] = {
-        "variance": pca_func.explained_variance_,
-        "variance_ratio": pca_func.explained_variance_ratio_,
-    }
-    if use_highly_variable:
-        adata.varm["PCs"] = np.zeros(shape=(adata.n_vars, n_comps))
-        adata.varm["PCs"][adata.var["highly_variable"]] = pca_func.components_.T
-    else:
-        adata.varm["PCs"] = pca_func.components_.T
diff --git a/rapids_singlecell/squidpy_gpu/_autocorr.py b/rapids_singlecell/squidpy_gpu/_autocorr.py
index 570a9acd..4b423ca2 100644
--- a/rapids_singlecell/squidpy_gpu/_autocorr.py
+++ b/rapids_singlecell/squidpy_gpu/_autocorr.py
@@ -110,11 +110,11 @@ def spatial_autocorr(
     # check sparse:
     if use_sparse:
         vals = sparse.csr_matrix(vals)
-        data = cp.sparse.csr_matrix(vals)
+        data = cp.sparse.csr_matrix(vals, dtype=cp.float32)
     else:
         if sparse.issparse(vals):
             vals = vals.toarray()
-        data = cp.array(vals)
+        data = cp.array(vals, dtype=cp.float32)
     # Run Spartial Autocorr
     if mode == "moran":
         score, score_perms = _morans_I_cupy(
@@ -136,7 +136,8 @@ def spatial_autocorr(
         raise NotImplementedError(f"Mode `{mode}` is not yet implemented.")
     g = sparse.csr_matrix(adj_matrix_cupy.get())
     score = score.get()
-    score_perms = score_perms.get()
+    if n_perms is not None:
+        score_perms = score_perms.get()
     with np.errstate(divide="ignore"):
         pval_results = _p_value_calc(score, score_perms, g, params)
 
diff --git a/rapids_singlecell/squidpy_gpu/_ligrec.py b/rapids_singlecell/squidpy_gpu/_ligrec.py
index 337c430c..15970480 100644
--- a/rapids_singlecell/squidpy_gpu/_ligrec.py
+++ b/rapids_singlecell/squidpy_gpu/_ligrec.py
@@ -14,7 +14,7 @@
     Mapping,
     Iterable,
 )
-from ._utils import _create_sparse_df
+from ._utils import _create_sparse_df, _assert_categorical_obs
 
 
 SOURCE = "source"
@@ -245,8 +245,6 @@ def ligrec(
         interactions = _get_interactions(
             interactions_params, transmitter_params, receiver_params
         )
-    else:
-        interactions = interactions.copy()
 
     if isinstance(interactions, Mapping):
         interactions = pd.DataFrame(interactions)
@@ -277,6 +275,13 @@ def ligrec(
 
     assert isinstance(interactions, pd.DataFrame)
 
+    if corr_axis:
+        if corr_axis not in {"clusters", "interactions"}:
+            raise ValueError(f"Invalid option `{corr_axis}` for `CorrAxis`.")
+
+    if n_perms <= 0:
+        raise ValueError(f"Expected `n_perms` to be positive, found `{n_perms}`.")
+
     if interactions.empty:
         raise ValueError("The interactions are empty")
     # Prepare adata
@@ -355,18 +360,23 @@ def find_min_gene_in_complex(_complex: str | None) -> str | None:
             interactions, tgt, how="left", left_index=True, right_index=True
         )
     else:
-        raise NotImplementedError(
-            f"Complex policy {complex_policy!r} is not implemented."
-        )
+        raise ValueError(f"Invalid option `{complex_policy!r}` for `ComplexPolicy`.")
 
     interactions = interactions[
         interactions[SOURCE].isin(data.columns)
         & interactions[TARGET].isin(data.columns)
     ]
 
+    _assert_categorical_obs(adata, key=cluster_key)
+    if interactions.empty:
+        raise ValueError("After filtering by genes, no interactions remain.")
     filtered_data = data.loc[
         :, list(set(interactions[SOURCE]) | set(interactions[TARGET]))
     ]
+    if len(adata.obs[cluster_key].cat.categories) <= 1:
+        raise ValueError(
+            f"Expected at least `2` clusters, found `{len(adata.obs[cluster_key].cat.categories)}`."
+        )
     filtered_data["clusters"] = (
         adata.obs.copy()[cluster_key].astype("string").astype("category").values
     )
diff --git a/rapids_singlecell/squidpy_gpu/_utils.py b/rapids_singlecell/squidpy_gpu/_utils.py
index 5b15bd5c..8a9d01cf 100644
--- a/rapids_singlecell/squidpy_gpu/_utils.py
+++ b/rapids_singlecell/squidpy_gpu/_utils.py
@@ -8,6 +8,7 @@
 from scipy import stats
 from scipy.sparse import spmatrix, issparse
 import pandas as pd
+from pandas.api.types import infer_dtype, is_categorical_dtype
 
 
 ### Taken from squidpy: https://github.com/scverse/squidpy/blob/main/squidpy/gr/_ppatterns.py
@@ -202,3 +203,13 @@ def _create_sparse_df(
     return pd.DataFrame._from_arrays(
         arrays, columns=columns, index=index, verify_integrity=False
     )
+
+
+def _assert_categorical_obs(adata, key):
+    if key not in adata.obs:
+        raise KeyError(f"Cluster key `{key}` not found in `adata.obs`.")
+
+    if not is_categorical_dtype(adata.obs[key]):
+        raise TypeError(
+            f"Expected `adata.obs[{key!r}]` to be `categorical`, found `{infer_dtype(adata.obs[key])}`."
+        )
diff --git a/rapids_singlecell/tests/_data/dummy.h5ad b/rapids_singlecell/tests/_data/dummy.h5ad
new file mode 100644
index 00000000..f1f55513
Binary files /dev/null and b/rapids_singlecell/tests/_data/dummy.h5ad differ
diff --git a/rapids_singlecell/tests/_data/test_data.h5ad b/rapids_singlecell/tests/_data/test_data.h5ad
new file mode 100644
index 00000000..0daf6243
Binary files /dev/null and b/rapids_singlecell/tests/_data/test_data.h5ad differ
diff --git a/rapids_singlecell/tests/_scripts/seurat_hvg.csv b/rapids_singlecell/tests/_scripts/seurat_hvg.csv
new file mode 100644
index 00000000..34f43688
--- /dev/null
+++ b/rapids_singlecell/tests/_scripts/seurat_hvg.csv
@@ -0,0 +1,766 @@
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diff --git a/rapids_singlecell/tests/_scripts/seurat_hvg_v3.csv.gz b/rapids_singlecell/tests/_scripts/seurat_hvg_v3.csv.gz
new file mode 100644
index 00000000..a2bb6739
Binary files /dev/null and b/rapids_singlecell/tests/_scripts/seurat_hvg_v3.csv.gz differ
diff --git a/rapids_singlecell/tests/_scripts/seurat_hvg_v3_batch.csv b/rapids_singlecell/tests/_scripts/seurat_hvg_v3_batch.csv
new file mode 100644
index 00000000..76f5486a
--- /dev/null
+++ b/rapids_singlecell/tests/_scripts/seurat_hvg_v3_batch.csv
@@ -0,0 +1,4001 @@
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+"1985" "CCT4"
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+"1993" "MID1IP1"
+"1994" "PCNX"
+"1995" "RAB21"
+"1996" "SELPLG"
+"1997" "CSRNP1"
+"1998" "MRTO4"
+"1999" "TMEM102"
+"2000" "METTL13"
+"2001" "PHF5A"
+"2002" "DEXI"
+"2003" "SIAH2"
+"2004" "HENMT1"
+"2005" "CD4"
+"2006" "RARS"
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+"2008" "CNPY3"
+"2009" "SLC6A6"
+"2010" "RASAL3"
+"2011" "RP11-792A8.4"
+"2012" "TFAM"
+"2013" "ERCC1"
+"2014" "GUCY1B3"
+"2015" "CLSPN"
+"2016" "MRPS36"
+"2017" "ATP6V1D"
+"2018" "AP3M2"
+"2019" "TCEAL1"
+"2020" "SYNE2"
+"2021" "DOCK10"
+"2022" "P2RY10"
+"2023" "DUSP22"
+"2024" "USP7"
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+"2031" "ILF3"
+"2032" "CTA-29F11.1"
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+"2037" "XXbac-BPG299F13.17"
+"2038" "MRPS18C"
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+"2041" "PHGDH"
+"2042" "TNFRSF25"
+"2043" "RFC5"
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diff --git a/rapids_singlecell/tests/test_autocorr.py b/rapids_singlecell/tests/test_autocorr.py
new file mode 100644
index 00000000..4c9ff4bf
--- /dev/null
+++ b/rapids_singlecell/tests/test_autocorr.py
@@ -0,0 +1,41 @@
+import pytest
+from rapids_singlecell.gr import spatial_autocorr
+import pandas as pd
+from anndata import read_h5ad
+from pathlib import Path
+import numpy as np
+
+MORAN_I = "moranI"
+GEARY_C = "gearyC"
+
+
+@pytest.mark.parametrize("mode", ["moran", "geary"])
+def test_autocorr_consistency(mode):
+    file = Path(__file__).parent / Path("_data/dummy.h5ad")
+    dummy_adata = read_h5ad(file)
+
+    spatial_autocorr(dummy_adata, mode=mode)
+    df1 = spatial_autocorr(dummy_adata, mode=mode, copy=True, n_perms=50)
+    df2 = spatial_autocorr(dummy_adata, mode=mode, copy=True, n_perms=50)
+
+    idx_df = df1.index.values
+    idx_adata = dummy_adata[:, dummy_adata.var.highly_variable.values].var_names.values
+
+    if mode == "moran":
+        UNS_KEY = MORAN_I
+    elif mode == "geary":
+        UNS_KEY = GEARY_C
+
+    assert UNS_KEY in dummy_adata.uns.keys()
+    assert "pval_sim_fdr_bh" in df1
+    assert "pval_norm_fdr_bh" in dummy_adata.uns[UNS_KEY]
+    assert dummy_adata.uns[UNS_KEY].columns.shape == (4,)
+    assert df1.columns.shape == (9,)
+    # test pval_norm same
+    np.testing.assert_allclose(
+        df1["pval_norm"].values, df2["pval_norm"].values, atol=1e-5, rtol=1e-5
+    )
+    # test highly variable
+    assert dummy_adata.uns[UNS_KEY].shape != df1.shape
+    # assert idx are sorted and contain same elements
+    assert not np.array_equal(idx_df, idx_adata)
diff --git a/rapids_singlecell/tests/test_clustering.py b/rapids_singlecell/tests/test_clustering.py
new file mode 100644
index 00000000..3baf62cf
--- /dev/null
+++ b/rapids_singlecell/tests/test_clustering.py
@@ -0,0 +1,18 @@
+import pytest
+import rapids_singlecell as rsc
+from scanpy.datasets import pbmc68k_reduced
+
+
+@pytest.fixture
+def adata_neighbors():
+    return pbmc68k_reduced()
+
+
+def test_leiden_basic(adata_neighbors):
+    rsc.tl.leiden(adata_neighbors, use_weights=False)
+    rsc.tl.leiden(adata_neighbors, use_weights=True)
+
+
+def test_louvain_basic(adata_neighbors):
+    rsc.tl.louvain(adata_neighbors, use_weights=False)
+    rsc.tl.louvain(adata_neighbors, use_weights=True)
diff --git a/rapids_singlecell/tests/test_emdedding_density.py b/rapids_singlecell/tests/test_emdedding_density.py
new file mode 100644
index 00000000..357bcf7e
--- /dev/null
+++ b/rapids_singlecell/tests/test_emdedding_density.py
@@ -0,0 +1,21 @@
+import numpy as np
+from anndata import AnnData
+import rapids_singlecell as rsc
+
+
+def test_embedding_density():
+    # Test that density values are scaled
+    # Test that the highest value is in the middle for a grid layout
+    test_data = AnnData(X=np.ones((9, 10), dtype=np.float32))
+    test_data.obsm["X_test"] = np.array(
+        [[x, y] for x in range(3) for y in range(3)], dtype=np.float32
+    )
+    rsc.tl.embedding_density(test_data, "test")
+
+    max_dens = np.max(test_data.obs["test_density"])
+    min_dens = np.min(test_data.obs["test_density"])
+    max_idx = test_data.obs["test_density"].idxmax()
+
+    assert max_idx == "4"
+    assert max_dens == 1
+    assert min_dens == 0
diff --git a/rapids_singlecell/tests/test_hvg.py b/rapids_singlecell/tests/test_hvg.py
new file mode 100644
index 00000000..bbeef89b
--- /dev/null
+++ b/rapids_singlecell/tests/test_hvg.py
@@ -0,0 +1,323 @@
+import scanpy as sc
+import numpy as np
+import cupy as cp
+import cupyx as cpx
+import rapids_singlecell as rsc
+from rapids_singlecell.cunnData import cunnData
+import pandas as pd
+import pytest
+
+
+def test_highly_variable_genes_basic():
+    cudata = cunnData(sc.datasets.blobs())
+
+    rsc.pp.highly_variable_genes(cudata)
+
+    cudata = cunnData(sc.datasets.blobs())
+    np.random.seed(0)
+    cudata.obs["batch"] = np.random.binomial(3, 0.5, size=(cudata.n_obs))
+    cudata.obs["batch"] = cudata.obs["batch"].astype("category")
+    rsc.pp.highly_variable_genes(cudata, batch_key="batch")
+    assert "highly_variable_nbatches" in cudata.var.columns
+    assert "highly_variable_intersection" in cudata.var.columns
+
+    cudata = cunnData(sc.datasets.blobs())
+    batch = np.random.binomial(4, 0.5, size=(cudata.n_obs))
+    cudata.obs["batch"] = batch
+    cudata.obs["batch"] = cudata.obs["batch"].astype("category")
+    rsc.pp.highly_variable_genes(cudata, batch_key="batch", n_top_genes=3)
+    assert "highly_variable_nbatches" in cudata.var.columns
+    assert cudata.var["highly_variable"].sum() == 3
+    highly_var_first_layer = cudata.var["highly_variable"].copy()
+
+    cudata = cunnData(sc.datasets.blobs())
+    new_layer = cudata.X.toarray()
+    cp.random.shuffle(new_layer)
+    cudata.layers["test_layer"] = cpx.scipy.sparse.csr_matrix(new_layer)
+    cudata.obs["batch"] = batch
+    cudata.obs["batch"] = cudata.obs["batch"].astype("category")
+    rsc.pp.highly_variable_genes(
+        cudata, batch_key="batch", n_top_genes=3, layer="test_layer"
+    )
+    assert "highly_variable_nbatches" in cudata.var.columns
+    assert cudata.var["highly_variable"].sum() == 3
+    assert (highly_var_first_layer != cudata.var["highly_variable"]).any()
+
+    rsc.pp.highly_variable_genes(cudata)
+    no_batch_hvg = cudata.var.highly_variable.copy()
+    assert no_batch_hvg.any()
+    cudata.obs["batch"] = "batch"
+    cudata.obs["batch"] = cudata.obs["batch"].astype("category")
+    rsc.pp.highly_variable_genes(cudata, batch_key="batch")
+    assert np.all(no_batch_hvg == cudata.var.highly_variable)
+    assert np.all(cudata.var.highly_variable_intersection == cudata.var.highly_variable)
+
+    cudata.obs["batch"] = "a"
+    cudata.obs.batch.loc[::2] = "b"
+    rsc.pp.highly_variable_genes(cudata, batch_key="batch")
+    assert cudata.var["highly_variable"].any()
+
+    colnames = [
+        "means",
+        "dispersions",
+        "dispersions_norm",
+        "highly_variable_nbatches",
+        "highly_variable_intersection",
+        "highly_variable",
+    ]
+
+    assert np.all(np.isin(colnames, cudata.var.columns))
+
+
+from pathlib import Path
+
+FILE = Path(__file__).parent / Path("_scripts/seurat_hvg.csv")
+FILE_V3 = Path(__file__).parent / Path("_scripts/seurat_hvg_v3.csv.gz")
+FILE_V3_BATCH = Path(__file__).parent / Path("_scripts/seurat_hvg_v3_batch.csv")
+
+
+def test_higly_variable_genes_compare_to_seurat():
+    seurat_hvg_info = pd.read_csv(FILE, sep=" ")
+
+    pbmc = sc.datasets.pbmc68k_reduced()
+    pbmc.X = pbmc.raw.X
+    pbmc.var_names_make_unique()
+    pbmc = rsc.cunnData.cunnData(pbmc)
+
+    rsc.pp.normalize_total(pbmc, target_sum=1e4)
+    rsc.pp.log1p(pbmc)
+    rsc.pp.highly_variable_genes(
+        pbmc, flavor="seurat", min_mean=0.0125, max_mean=3, min_disp=0.5
+    )
+
+    np.testing.assert_array_equal(
+        seurat_hvg_info["highly_variable"], pbmc.var["highly_variable"]
+    )
+
+    # (still) Not equal to tolerance rtol=2e-05, atol=2e-05
+    # np.testing.assert_allclose(4, 3.9999, rtol=2e-05, atol=2e-05)
+    np.testing.assert_allclose(
+        seurat_hvg_info["means"],
+        pbmc.var["means"],
+        rtol=2e-05,
+        atol=2e-05,
+    )
+    np.testing.assert_allclose(
+        seurat_hvg_info["dispersions"],
+        pbmc.var["dispersions"],
+        rtol=2e-05,
+        atol=2e-05,
+    )
+    np.testing.assert_allclose(
+        seurat_hvg_info["dispersions_norm"],
+        pbmc.var["dispersions_norm"],
+        rtol=2e-05,
+        atol=2e-05,
+    )
+
+
+def test_higly_variable_genes_compare_to_seurat_v3():
+    seurat_hvg_info = pd.read_csv(
+        FILE_V3, sep=" ", dtype={"variances_norm": np.float64}
+    )
+
+    pbmc = sc.datasets.pbmc3k()
+    pbmc.var_names_make_unique()
+    pbmc = rsc.cunnData.cunnData(pbmc)
+
+    rsc.pp.highly_variable_genes(pbmc, n_top_genes=1000, flavor="seurat_v3")
+
+    np.testing.assert_array_equal(
+        seurat_hvg_info["highly_variable"], pbmc.var["highly_variable"]
+    )
+    np.testing.assert_allclose(
+        seurat_hvg_info["variances"],
+        pbmc.var["variances"],
+        rtol=2e-05,
+        atol=2e-05,
+    )
+
+    batch = np.zeros((pbmc.shape[0]), dtype=int)
+    batch[1500:] = 1
+    pbmc.obs["batch"] = batch
+    rsc.pp.highly_variable_genes(
+        pbmc, n_top_genes=4000, flavor="seurat_v3", batch_key="batch"
+    )
+    df = pbmc.var.copy()
+    df.sort_values(
+        ["highly_variable_nbatches", "highly_variable_rank"],
+        ascending=[False, True],
+        na_position="last",
+        inplace=True,
+    )
+    df = df.iloc[:4000]
+    seurat_hvg_info_batch = pd.read_csv(
+        FILE_V3_BATCH, sep=" ", dtype={"variances_norm": np.float64}
+    )
+
+    # ranks might be slightly different due to many genes having same normalized var
+    seu = pd.Index(seurat_hvg_info_batch["x"].values)
+    assert len(seu.intersection(df.index)) / 4000 > 0.95
+
+    rsc.pp.log1p(pbmc)
+    with pytest.warns(
+        UserWarning,
+        match="`flavor='seurat_v3'` expects raw count data, but non-integers were found.",
+    ):
+        rsc.pp.highly_variable_genes(pbmc, n_top_genes=1000, flavor="seurat_v3")
+
+
+def test_seurat_v3_mean_var_output_with_batchkey():
+    pbmc = sc.datasets.pbmc3k()
+    pbmc.var_names_make_unique()
+    pbmc = rsc.cunnData.cunnData(pbmc)
+
+    n_cells = pbmc.shape[0]
+    batch = np.zeros((n_cells), dtype=int)
+    batch[1500:] = 1
+    pbmc.obs["batch"] = batch
+
+    # true_mean, true_var = _get_mean_var(pbmc.X)
+    true_mean = cp.mean(pbmc.X.toarray().astype(cp.float64), axis=0)
+    true_var = cp.var(
+        pbmc.X.toarray().astype(cp.float64), axis=0, dtype=np.float64, ddof=1
+    )
+
+    rsc.pp.highly_variable_genes(
+        pbmc, batch_key="batch", flavor="seurat_v3", n_top_genes=4000
+    )
+    cp.testing.assert_allclose(true_mean, pbmc.var["means"], rtol=2e-05, atol=2e-05)
+    cp.testing.assert_allclose(true_var, pbmc.var["variances"], rtol=2e-05, atol=2e-05)
+
+
+def test_cellranger_n_top_genes_warning():
+    X = np.random.poisson(2, (100, 30))
+    cudata = rsc.cunnData.cunnData(X=X)
+    rsc.pp.normalize_total(cudata, target_sum=1e4)
+    rsc.pp.log1p(cudata)
+
+    with pytest.warns(
+        UserWarning,
+        match="`n_top_genes` > number of normalized dispersions, returning all genes with normalized dispersions.",
+    ):
+        rsc.pp.highly_variable_genes(cudata, n_top_genes=1000, flavor="cell_ranger")
+
+
+def _check_pearson_hvg_columns(output_df, n_top_genes):
+    assert pd.api.types.is_float_dtype(output_df["residual_variances"].dtype)
+
+    assert output_df["highly_variable"].values.dtype is np.dtype("bool")
+    assert np.sum(output_df["highly_variable"]) == n_top_genes
+
+    assert np.nanmax(output_df["highly_variable_rank"].values) <= n_top_genes - 1
+
+
+@pytest.mark.parametrize("clip", [None, np.Inf, 30])
+@pytest.mark.parametrize("theta", [100, np.Inf])
+@pytest.mark.parametrize("n_top_genes", [100, 200])
+def test_highly_variable_genes_pearson_residuals_general(clip, theta, n_top_genes):
+    adata = sc.datasets.pbmc3k().copy()
+    adata = adata[:1000, :500]
+    np.random.seed(42)
+    adata.obs["batch"] = np.random.randint(0, 3, size=adata.shape[0])
+    sc.pp.filter_genes(adata, min_cells=1)
+    # cleanup var
+    del adata.var
+
+    cudata = rsc.cunnData.cunnData(adata)
+
+    # compute reference output
+    residuals_reference = rsc.pp.normalize_pearson_residuals(
+        cudata, clip=clip, theta=theta, inplace=False
+    )
+    residual_variances_reference = cp.var(residuals_reference, axis=0)
+
+    # compute output to be tested
+    rsc.pp.highly_variable_genes(
+        cudata,
+        flavor="pearson_residuals",
+        n_top_genes=n_top_genes,
+        clip=clip,
+        theta=theta,
+    )
+
+    # check output is complete
+    for key in [
+        "highly_variable",
+        "means",
+        "variances",
+        "residual_variances",
+        "highly_variable_rank",
+    ]:
+        assert key in cudata.var.columns
+
+    assert np.allclose(
+        cudata.var["residual_variances"].values, residual_variances_reference
+    )
+
+    # check hvg flag
+    hvg_idx = np.where(cudata.var["highly_variable"])[0]
+    topn_idx = np.sort(
+        np.argsort(-cudata.var["residual_variances"].values)[:n_top_genes]
+    )
+    assert np.all(hvg_idx == topn_idx)
+
+    # check ranks
+    assert np.nanmin(cudata.var["highly_variable_rank"].values) == 0
+
+    # more general checks on ranks, hvg flag and residual variance
+    _check_pearson_hvg_columns(cudata.var, n_top_genes)
+
+
+@pytest.mark.parametrize("n_top_genes", [100, 200])
+def test_highly_variable_genes_pearson_residuals_batch(n_top_genes):
+    adata = sc.datasets.pbmc3k().copy()
+    adata = adata[:1000, :500]
+    np.random.seed(42)
+    adata.obs["batch"] = np.random.randint(0, 3, size=adata.shape[0])
+    sc.pp.filter_genes(adata, min_cells=1)
+    # cleanup var
+    del adata.var
+
+    cudata = rsc.cunnData.cunnData(adata)
+    n_genes = cudata.shape[1]
+
+    rsc.pp.highly_variable_genes(
+        cudata,
+        flavor="pearson_residuals",
+        n_top_genes=n_top_genes,
+        batch_key="batch",
+    )
+
+    # check output is complete
+    for key in [
+        "highly_variable",
+        "means",
+        "variances",
+        "residual_variances",
+        "highly_variable_rank",
+        "highly_variable_nbatches",
+        "highly_variable_intersection",
+    ]:
+        assert key in cudata.var.keys()
+
+    # general checks on ranks, hvg flag and residual variance
+    _check_pearson_hvg_columns(cudata.var, n_top_genes)
+
+    # check intersection flag
+    nbatches = len(np.unique(cudata.obs["batch"]))
+    assert cudata.var["highly_variable_intersection"].values.dtype is np.dtype("bool")
+    assert np.sum(cudata.var["highly_variable_intersection"]) <= n_top_genes * nbatches
+    assert np.all(
+        cudata.var["highly_variable"][cudata.var.highly_variable_intersection]
+    )
+
+    # check ranks (with batch_key these are the median of within-batch ranks)
+    assert pd.api.types.is_float_dtype(cudata.var["highly_variable_rank"].dtype)
+
+    # check nbatches
+    assert cudata.var["highly_variable_nbatches"].values.dtype is np.dtype("int")
+    assert np.min(cudata.var["highly_variable_nbatches"].values) >= 0
+    assert np.max(cudata.var["highly_variable_nbatches"].values) <= nbatches
+
+    assert len(cudata.var) == n_genes
diff --git a/rapids_singlecell/tests/test_ligrec.py b/rapids_singlecell/tests/test_ligrec.py
new file mode 100644
index 00000000..69be230f
--- /dev/null
+++ b/rapids_singlecell/tests/test_ligrec.py
@@ -0,0 +1,148 @@
+import sys
+from itertools import product
+from time import time
+from typing import TYPE_CHECKING, Mapping, Optional, Sequence, Tuple
+
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+from anndata import AnnData, read_h5ad
+from pandas.testing import assert_frame_equal
+from scanpy import settings as s
+from scanpy.datasets import blobs
+from itertools import product
+
+from rapids_singlecell.gr import ligrec
+from pathlib import Path
+
+_CK = "leiden"
+Interactions_t = Tuple[Sequence[str], Sequence[str]]
+Complexes_t = Sequence[Tuple[str, str]]
+
+
+@pytest.fixture()
+def adata() -> AnnData:
+    file = Path(__file__).parent / Path("_data/test_data.h5ad")
+    adata = read_h5ad(file)
+    adata.raw = adata.copy()
+    return adata
+
+
+@pytest.fixture()
+def interactions(adata: AnnData) -> Tuple[Sequence[str], Sequence[str]]:
+    return tuple(product(adata.raw.var_names[:5], adata.raw.var_names[:5]))
+
+
+class TestInvalidBehavior:
+    def test_not_adata(self, interactions: Interactions_t):
+        with pytest.raises(
+            TypeError, match=r"Expected `adata` to be of type `anndata.AnnData`"
+        ):
+            ligrec(None, _CK, interactions=interactions)
+
+    def test_adata_no_raw(self, adata: AnnData, interactions: Interactions_t):
+        del adata.raw
+        with pytest.raises(AttributeError, match=r"No `.raw` attribute"):
+            ligrec(adata, _CK, use_raw=True, interactions=interactions)
+
+    def test_raw_has_different_n_obs(
+        self, adata: AnnData, interactions: Interactions_t
+    ):
+        adata.raw = sc.datasets.blobs(n_observations=adata.n_obs + 1)
+        # raise below happend with anndata < 0.9
+        # with pytest.raises(ValueError, match=rf"Expected `{adata.n_obs}` cells in `.raw`"):
+        with pytest.raises(
+            ValueError,
+            match=rf"Index length mismatch: {adata.n_obs} vs. {adata.n_obs + 1}",
+        ):
+            ligrec(adata, _CK, interactions=interactions)
+
+    def test_invalid_cluster_key(self, adata: AnnData, interactions: Interactions_t):
+        with pytest.raises(KeyError, match=r"Cluster key `foobar` not found"):
+            ligrec(adata, cluster_key="foobar", interactions=interactions)
+
+    def test_cluster_key_is_not_categorical(
+        self, adata: AnnData, interactions: Interactions_t
+    ):
+        adata.obs[_CK] = adata.obs[_CK].astype("string")
+        with pytest.raises(
+            TypeError, match=rf"Expected `adata.obs\[{_CK!r}\]` to be `categorical`"
+        ):
+            ligrec(adata, _CK, interactions=interactions)
+
+    def test_only_1_cluster(self, adata: AnnData, interactions: Interactions_t):
+        adata.obs["foo"] = 1
+        adata.obs["foo"] = adata.obs["foo"].astype("category")
+        with pytest.raises(
+            ValueError, match=rf"Expected at least `2` clusters, found `1`."
+        ):
+            ligrec(adata, "foo", interactions=interactions)
+
+    def test_invalid_complex_policy(self, adata: AnnData, interactions: Interactions_t):
+        with pytest.raises(
+            ValueError, match=rf"Invalid option `'foobar'` for `ComplexPolicy`."
+        ):
+            ligrec(adata, _CK, interactions=interactions, complex_policy="foobar")
+
+    def test_invalid_fdr_axis(self, adata: AnnData, interactions: Interactions_t):
+        with pytest.raises(
+            ValueError, match=r"Invalid option `foobar` for `CorrAxis`."
+        ):
+            ligrec(
+                adata,
+                _CK,
+                interactions=interactions,
+                corr_axis="foobar",
+                corr_method="fdr_bh",
+            )
+
+    def test_too_few_permutations(self, adata: AnnData, interactions: Interactions_t):
+        with pytest.raises(ValueError, match=r"Expected `n_perms` to be positive"):
+            ligrec(adata, _CK, interactions=interactions, n_perms=0)
+
+    def test_invalid_interactions_type(self, adata: AnnData):
+        with pytest.raises(TypeError, match=r"Expected either a `pandas.DataFrame`"):
+            ligrec(adata, _CK, interactions=42)
+
+    def test_invalid_interactions_dict(self, adata: AnnData):
+        with pytest.raises(KeyError, match=r"Column .* is not in `interactions`."):
+            ligrec(adata, _CK, interactions={"foo": ["foo"], "target": ["bar"]})
+        with pytest.raises(KeyError, match=r"Column .* is not in `interactions`."):
+            ligrec(adata, _CK, interactions={"source": ["foo"], "bar": ["bar"]})
+
+    def test_invalid_interactions_dataframe(
+        self, adata: AnnData, interactions: Interactions_t
+    ):
+        df = pd.DataFrame(interactions, columns=["foo", "target"])
+        with pytest.raises(KeyError, match=r"Column .* is not in `interactions`."):
+            ligrec(adata, _CK, interactions=df)
+
+        df = pd.DataFrame(interactions, columns=["source", "bar"])
+        with pytest.raises(KeyError, match=r"Column .* is not in `interactions`."):
+            ligrec(adata, _CK, interactions=df)
+
+    def test_interactions_invalid_sequence(
+        self, adata: AnnData, interactions: Interactions_t
+    ):
+        interactions += ("foo", "bar", "bar")  # type: ignore
+        with pytest.raises(
+            ValueError, match=r"Not all interactions are of length `2`."
+        ):
+            ligrec(adata, _CK, interactions=interactions)
+
+    def test_interactions_only_invalid_names(self, adata: AnnData):
+        with pytest.raises(ValueError, match=r"After filtering by genes"):
+            ligrec(adata, _CK, interactions=["foo", "bar", "baz"])
+
+    def test_invalid_clusters(self, adata: AnnData, interactions: Interactions_t):
+        with pytest.raises(ValueError, match=r"Invalid cluster `'foo'`."):
+            ligrec(adata, _CK, interactions=interactions, clusters=["foo"])
+
+    def test_invalid_clusters_mix(self, adata: AnnData, interactions: Interactions_t):
+        with pytest.raises(
+            ValueError, match=r"Expected a `tuple` of length `2`, found `3`."
+        ):
+            ligrec(
+                adata, _CK, interactions=interactions, clusters=["foo", ("bar", "baz")]
+            )
diff --git a/rapids_singlecell/tests/test_normalization.py b/rapids_singlecell/tests/test_normalization.py
new file mode 100644
index 00000000..a54d71f5
--- /dev/null
+++ b/rapids_singlecell/tests/test_normalization.py
@@ -0,0 +1,81 @@
+import numpy as np
+import cupy as cp
+from anndata import AnnData
+import rapids_singlecell as rsc
+from rapids_singlecell.cunnData import cunnData
+import pytest
+from scipy.sparse import csr_matrix
+
+X_total = np.array([[1, 0], [3, 0], [5, 6]])
+
+
+def test_normalize_total():
+    adata = AnnData(X_total, dtype=np.float32)
+    cudata = cunnData(adata)
+    rsc.pp.normalize_total(cudata, target_sum=1)
+    cp.testing.assert_allclose(
+        cp.ravel(cudata.X.sum(axis=1)), np.ones(cudata.shape[0], dtype=np.float32)
+    )
+
+
+def test_normalize_total_layers():
+    adata = AnnData(X_total)
+    cudata = cunnData(adata)
+    cudata.layers["layer"] = cudata.X.copy()
+
+    rsc.pp.normalize_total(cudata, target_sum=1, layer="layer")
+    assert np.allclose(
+        cudata.layers["layer"].sum(axis=1), np.ones(cudata.shape[0], dtype=np.float32)
+    )
+
+
+@pytest.mark.parametrize(
+    "sparsity_func", [cp.array, csr_matrix], ids=lambda x: x.__name__
+)
+@pytest.mark.parametrize("dtype", ["float32"])
+@pytest.mark.parametrize("theta", [0.01, 1.0, 100, np.Inf])
+@pytest.mark.parametrize("clip", [None, 1.0, np.Inf])
+def test_normalize_pearson_residuals_values(sparsity_func, dtype, theta, clip):
+    # toy data
+    X = np.array([[3, 6], [2, 4], [1, 0]])
+    ns = np.sum(X, axis=1)
+    ps = np.sum(X, axis=0) / np.sum(X)
+    mu = np.outer(ns, ps)
+
+    # compute reference residuals
+    if np.isinf(theta):
+        # Poisson case
+        residuals_reference = (X - mu) / np.sqrt(mu)
+    else:
+        # NB case
+        residuals_reference = (X - mu) / np.sqrt(mu + mu**2 / theta)
+
+    # compute output to test
+    cudata = cunnData(X=sparsity_func(X, dtype=cp.float32))
+    output_X = rsc.pp.normalize_pearson_residuals(
+        cudata, theta=theta, clip=clip, inplace=False
+    )
+
+    rsc.pp.normalize_pearson_residuals(cudata, theta=theta, clip=clip, inplace=True)
+    assert np.all(np.isin(["pearson_residuals_normalization"], list(cudata.uns.keys())))
+    assert np.all(
+        np.isin(
+            ["theta", "clip", "computed_on"],
+            list(cudata.uns["pearson_residuals_normalization"].keys()),
+        )
+    )
+
+    # test against inplace
+    cp.testing.assert_array_equal(cudata.X, output_X)
+    if clip is None:
+        # default clipping: compare to sqrt(n) threshold
+        clipping_threshold = np.sqrt(cudata.shape[0]).astype(np.float32)
+        assert np.max(output_X) <= clipping_threshold
+        assert np.min(output_X) >= -clipping_threshold
+    elif np.isinf(clip):
+        # no clipping: compare to raw residuals
+        assert np.allclose(output_X, residuals_reference)
+    else:
+        # custom clipping: compare to custom threshold
+        assert np.max(output_X) <= clip
+        assert np.min(output_X) >= -clip
diff --git a/rapids_singlecell/tests/test_pca.py b/rapids_singlecell/tests/test_pca.py
new file mode 100644
index 00000000..7554f774
--- /dev/null
+++ b/rapids_singlecell/tests/test_pca.py
@@ -0,0 +1,106 @@
+import rapids_singlecell as rsc
+import numpy as np
+import pytest
+from anndata import AnnData
+from scanpy.datasets import pbmc3k_processed
+
+A_list = [
+    [0, 0, 7, 0, 0],
+    [8, 5, 0, 2, 0],
+    [6, 0, 0, 2, 5],
+    [0, 0, 0, 1, 0],
+    [8, 8, 2, 1, 0],
+    [0, 0, 0, 4, 5],
+]
+
+A_pca = np.array(
+    [
+        [-4.4783009, 5.55508466, 1.73111572, -0.06029139, 0.17292555],
+        [5.4855141, -0.42651191, -0.74776055, -0.74532146, 0.74633582],
+        [0.01161428, -4.0156662, 2.37252748, -1.33122372, -0.29044446],
+        [-3.61934397, 0.48525412, -2.96861931, -1.16312545, -0.33230607],
+        [7.14050048, 1.86330409, -0.05786325, 1.25045782, -0.50213107],
+        [-4.53998399, -3.46146476, -0.32940009, 2.04950419, 0.20562023],
+    ]
+)
+
+A_svd = np.array(
+    [
+        [-0.77034038, -2.00750922, 6.64603489, -0.39669256, -0.22212097],
+        [-9.47135856, -0.6326006, -1.33787112, -0.24894361, -1.02044665],
+        [-5.90007339, 4.99658727, 0.70712592, -2.15188849, 0.30430008],
+        [-0.19132409, 0.42172251, 0.11169531, 0.50977966, -0.71637566],
+        [-11.1286238, -2.73045559, 0.08040596, 1.06850585, 0.74173764],
+        [-1.50180389, 5.56886849, 1.64034442, 2.24476032, -0.05109001],
+    ]
+)
+
+
+def test_pca_transform():
+    A = np.array(A_list).astype("float32")
+    A_pca_abs = np.abs(A_pca)
+    A_svd_abs = np.abs(A_svd)
+
+    adata = AnnData(A)
+
+    rsc.pp.pca(adata, n_comps=4, zero_center=True)
+
+    assert np.linalg.norm(A_pca_abs[:, :4] - np.abs(adata.obsm["X_pca"])) < 2e-05
+
+    rsc.pp.pca(adata, n_comps=4, zero_center=False)
+    assert np.linalg.norm(A_svd_abs[:, :4] - np.abs(adata.obsm["X_pca"])) < 2e-05
+
+
+def test_pca_shapes():
+    adata = AnnData(np.random.randn(30, 20))
+    rsc.pp.pca(adata)
+    assert adata.obsm["X_pca"].shape == (30, 19)
+
+    adata = AnnData(np.random.randn(20, 30))
+    rsc.pp.pca(adata)
+    assert adata.obsm["X_pca"].shape == (20, 19)
+    with pytest.raises(ValueError):
+        rsc.pp.pca(adata, n_comps=100)
+
+
+def test_pca_chunked():
+    chunked = pbmc3k_processed()
+    default = pbmc3k_processed()
+    chunked.X = chunked.X.astype(np.float64)
+    default.X = default.X.astype(np.float64)
+    rsc.pp.pca(chunked, chunked=True, chunk_size=chunked.shape[0])
+    rsc.pp.pca(default)
+
+    np.testing.assert_allclose(
+        np.abs(chunked.obsm["X_pca"]),
+        np.abs(default.obsm["X_pca"]),
+        rtol=1e-7,
+        atol=1e-6,
+    )
+
+    np.testing.assert_allclose(
+        np.abs(chunked.varm["PCs"]), np.abs(default.varm["PCs"]), rtol=1e-7, atol=1e-6
+    )
+    np.testing.assert_allclose(
+        np.abs(chunked.uns["pca"]["variance"]), np.abs(default.uns["pca"]["variance"])
+    )
+
+    np.testing.assert_allclose(
+        np.abs(chunked.uns["pca"]["variance_ratio"]),
+        np.abs(default.uns["pca"]["variance_ratio"]),
+    )
+
+
+def test_pca_reproducible():
+    pbmc = pbmc3k_processed()
+    pbmc.X = pbmc.X.astype(np.float32)
+    rsc.tl.pca(pbmc)
+    a = pbmc.obsm["X_pca"]
+    rsc.tl.pca(pbmc)
+    b = pbmc.obsm["X_pca"]
+    np.array_equal(a, b)
+    cpbmc = rsc.cunnData.cunnData(pbmc)
+    cpbmc.X = cpbmc.X.toarray()
+    rsc.tl.pca(cpbmc)
+    c = pbmc.obsm["X_pca"]
+    np.array_equal(a, c)
diff --git a/rapids_singlecell/tests/test_preprocessing.py b/rapids_singlecell/tests/test_preprocessing.py
new file mode 100644
index 00000000..7ec68b29
--- /dev/null
+++ b/rapids_singlecell/tests/test_preprocessing.py
@@ -0,0 +1,21 @@
+import scanpy as sc
+import rapids_singlecell as rsc
+import cupy as cp
+
+
+def test_scale():
+    adata = sc.datasets.pbmc68k_reduced()
+    adata.X = adata.raw.X
+
+    v = adata[:, 0 : adata.shape[1] // 2]
+    cudata = rsc.cunnData.cunnData(v)
+    cudata.X = cudata.X.toarray().astype(cp.float64)
+
+    rsc.pp.scale(cudata)
+
+    cp.testing.assert_allclose(
+        cudata.X.var(axis=0), cp.ones(cudata.shape[1]), atol=0.01
+    )
+    cp.testing.assert_allclose(
+        cudata.X.mean(axis=0), cp.zeros(cudata.shape[1]), atol=0.00001
+    )
diff --git a/rapids_singlecell/tests/test_qc_metrics.py b/rapids_singlecell/tests/test_qc_metrics.py
new file mode 100644
index 00000000..1e882014
--- /dev/null
+++ b/rapids_singlecell/tests/test_qc_metrics.py
@@ -0,0 +1,89 @@
+import scanpy as sc
+import rapids_singlecell as rsc
+from scipy import sparse
+import numpy as np
+import cupy as cp
+from anndata import AnnData
+from cupyx.scipy import sparse as sparse_gpu
+import pytest
+import pandas as pd
+
+
+def test_qc_metrics():
+    cudata = rsc.cunnData.cunnData(
+        X=sparse.csr_matrix(np.random.binomial(100, 0.005, (1000, 1000)))
+    )
+    cudata.var["mito"] = np.concatenate(
+        (np.ones(100, dtype=bool), np.zeros(900, dtype=bool))
+    )
+    cudata.var["negative"] = False
+    rsc.pp.calculate_qc_metrics(cudata, qc_vars=["mito", "negative"])
+    assert (cudata.obs["n_genes_by_counts"] < cudata.shape[1]).all()
+    assert (
+        cudata.obs["n_genes_by_counts"] >= cudata.obs["log1p_n_genes_by_counts"]
+    ).all()
+    assert (cudata.obs["total_counts"] == cp.ravel(cudata.X.sum(axis=1)).get()).all()
+    assert (cudata.obs["total_counts"] >= cudata.obs["log1p_total_counts"]).all()
+    assert (
+        cudata.obs["total_counts_mito"] >= cudata.obs["log1p_total_counts_mito"]
+    ).all()
+    assert (cudata.obs["total_counts_negative"] == 0).all()
+
+    for col in filter(lambda x: "negative" not in x, cudata.obs.columns):
+        assert (cudata.obs[col] >= 0).all()  # Values should be positive or zero
+        assert (cudata.obs[col] != 0).any().all()  # Nothing should be all zeros
+        if col.startswith("pct_counts_in_top"):
+            assert (cudata.obs[col] <= 100).all()
+            assert (cudata.obs[col] >= 0).all()
+    for col in cudata.var.columns:
+        assert (cudata.var[col] >= 0).all()
+    assert (
+        cudata.var["mean_counts"] < cp.ravel(cudata.X.max(axis=0).toarray()).get()
+    ).all()
+    assert (cudata.var["mean_counts"] >= cudata.var["log1p_mean_counts"]).all()
+    assert (cudata.var["total_counts"] >= cudata.var["log1p_total_counts"]).all()
+    # Should return the same thing if run again
+    old_obs, old_var = cudata.obs.copy(), cudata.var.copy()
+    rsc.pp.calculate_qc_metrics(cudata, qc_vars=["mito", "negative"])
+    assert set(cudata.obs.columns) == set(old_obs.columns)
+    assert set(cudata.var.columns) == set(old_var.columns)
+    for col in cudata.obs:
+        assert np.allclose(cudata.obs[col], old_obs[col])
+    for col in cudata.var:
+        assert np.allclose(cudata.var[col], old_var[col])
+    # with log1p=False
+    cudata = rsc.cunnData.cunnData(
+        X=sparse.csr_matrix(np.random.binomial(100, 0.005, (1000, 1000)))
+    )
+    cudata.var["mito"] = np.concatenate(
+        (np.ones(100, dtype=bool), np.zeros(900, dtype=bool))
+    )
+    cudata.var["negative"] = False
+    rsc.pp.calculate_qc_metrics(cudata, qc_vars=["mito", "negative"], log1p=False)
+    assert not np.any(cudata.obs.columns.str.startswith("log1p_"))
+    assert not np.any(cudata.var.columns.str.startswith("log1p_"))
+
+
+def adata_mito():
+    a = np.random.binomial(100, 0.005, (1000, 1000))
+    init_var = pd.DataFrame(
+        dict(mito=np.concatenate((np.ones(100, dtype=bool), np.zeros(900, dtype=bool))))
+    )
+    adata_dense = AnnData(X=a, var=init_var.copy())
+    return adata_dense
+
+
+@pytest.mark.parametrize(
+    "cls", [cp.array, sparse_gpu.csc_matrix, sparse_gpu.csr_matrix]
+)
+def test_qc_metrics_format(cls):
+    adata_dense = adata_mito()
+    cudata_dense = rsc.cunnData.cunnData(adata_dense)
+    rsc.pp.calculate_qc_metrics(cudata_dense, qc_vars=["mito"])
+    cudata = rsc.cunnData.cunnData(adata_dense)
+    cudata.X = cudata.X
+
+    rsc.pp.calculate_qc_metrics(cudata, qc_vars=["mito"])
+    assert np.allclose(cudata.obs, cudata_dense.obs)
+    for col in cudata.var:  # np.allclose doesn't like mix of types
+        assert np.allclose(cudata.var[col], cudata_dense.var[col])
diff --git a/rapids_singlecell/tests/test_rank_genes_groups_logreg.py b/rapids_singlecell/tests/test_rank_genes_groups_logreg.py
new file mode 100644
index 00000000..6ddca046
--- /dev/null
+++ b/rapids_singlecell/tests/test_rank_genes_groups_logreg.py
@@ -0,0 +1,37 @@
+import numpy as np
+import scanpy as sc
+import rapids_singlecell as rsc
+
+
+def test_rank_genes_groups_with_renamed_categories():
+    adata = sc.datasets.blobs(n_variables=4, n_centers=3, n_observations=200)
+    assert np.allclose(adata.X[1], [9.214668, -2.6487126, 4.2020774, 0.51076424])
+    adata.obs.blobs = adata.obs.blobs.astype("category")
+    # for method in ['logreg', 't-test']:
+
+    rsc.tl.rank_genes_groups_logreg(adata, "blobs")
+    assert adata.uns["rank_genes_groups"]["names"].dtype.names == ("0", "1", "2")
+    assert adata.uns["rank_genes_groups"]["names"][0].tolist() == ("1", "3", "0")
+
+    adata.rename_categories("blobs", ["Zero", "One", "Two"])
+    assert adata.uns["rank_genes_groups"]["names"][0].tolist() == ("1", "3", "0")
+
+    rsc.tl.rank_genes_groups_logreg(adata, "blobs")
+    assert adata.uns["rank_genes_groups"]["names"][0].tolist() == ("1", "3", "0")
+    assert adata.uns["rank_genes_groups"]["names"].dtype.names == ("Zero", "One", "Two")
+
+
+def test_rank_genes_groups_with_renamed_categories_use_rep():
+    adata = sc.datasets.blobs(n_variables=4, n_centers=3, n_observations=200)
+    assert np.allclose(adata.X[1], [9.214668, -2.6487126, 4.2020774, 0.51076424])
+    adata.obs.blobs = adata.obs.blobs.astype("category")
+
+    adata.layers["to_test"] = adata.X.copy()
+    adata.X = adata.X[::-1, :]
+
+    rsc.tl.rank_genes_groups_logreg(adata, "blobs", layer="to_test", use_raw=False)
+    assert adata.uns["rank_genes_groups"]["names"].dtype.names == ("0", "1", "2")
+    assert adata.uns["rank_genes_groups"]["names"][0].tolist() == ("1", "3", "0")
+
+    rsc.tl.rank_genes_groups_logreg(adata, "blobs")
+    assert not adata.uns["rank_genes_groups"]["names"][0].tolist() == ("3", "1", "0")