diff --git a/setup.cfg b/setup.cfg
index 03d6d32..8eda853 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -49,8 +49,8 @@ package_dir =
 # For more information, check out https://semver.org/.
 install_requires =
     importlib-metadata; python_version<"3.8"
-    genomicranges>=0.3.6
-    scipy
+    genomicranges>=0.3.7
+    biocgenerics
 
 [options.packages.find]
 where = src
diff --git a/src/summarizedexperiment/BaseSE.py b/src/summarizedexperiment/BaseSE.py
index 99c0cba..404beb9 100644
--- a/src/summarizedexperiment/BaseSE.py
+++ b/src/summarizedexperiment/BaseSE.py
@@ -1,23 +1,21 @@
-import warnings
 from collections import OrderedDict
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
+from warnings import warn
 
-from biocframe import BiocFrame
+from biocframe import BiocFrame, from_pandas
+from biocgenerics import colnames, rownames, set_colnames, set_rownames
+from biocutils import is_list_of_type
 from genomicranges import GenomicRanges
 from pandas import DataFrame
 
-from .dispatchers.colnames import get_colnames, set_colnames
-from .dispatchers.rownames import get_rownames, set_rownames
 from .type_checks import (
     is_bioc_or_pandas_frame,
     is_list_of_subclass,
-    is_list_of_type,
     is_matrix_like,
+    is_pandas,
 )
 from .types import (
-    BiocOrPandasFrame,
     MatrixSlicerTypes,
-    MatrixTypes,
     SlicerArgTypes,
     SlicerResult,
 )
@@ -28,7 +26,6 @@
 )
 from .utils.slicer import get_indexes_from_bools, get_indexes_from_names
 
-
 __author__ = "jkanche, keviny2"
 __copyright__ = "jkanche"
 __license__ = "MIT"
@@ -42,7 +39,7 @@ class BaseSE:
     features in ``row_data``, sample data in ``col_data``, and any other relevant ``metadata``.
 
     Attributes:
-        assays (Dict[str, MatrixTypes]): A dictionary containing matrices, with assay names as keys
+        assays (Dict[str, Any]): A dictionary containing matrices, with assay names as keys
             and 2-dimensional matrices represented as either
             :py:class:`~numpy.ndarray` or :py:class:`~scipy.sparse.spmatrix`.
 
@@ -52,11 +49,11 @@ class BaseSE:
             All matrices in assays must be 2-dimensional and have the same shape
             (number of rows, number of columns).
 
-        row_data (BiocOrPandasFrame, optional): Features, which must be of the same length as the rows of
+        row_data (BiocFrame, optional): Features, must be the same length as the numner of rows of
             the matrices in assays. Features can be either a :py:class:`~pandas.DataFrame` or
             :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
-        col_data (BiocOrPandasFrame, optional): Sample data, which must be of the same length as the
+        col_data (BiocFrame, optional): Sample data, which be the same length as the number of
             columns of the matrices in assays. Sample Information can be either a :py:class:`~pandas.DataFrame`
             or :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
@@ -65,15 +62,15 @@ class BaseSE:
 
     def __init__(
         self,
-        assays: Dict[str, MatrixTypes],
-        rows: Optional[BiocOrPandasFrame] = None,
-        cols: Optional[BiocOrPandasFrame] = None,
-        metadata: Optional[Dict] = None,
+        assays: Dict[str, Any],
+        rows: Optional[BiocFrame] = None,
+        cols: Optional[BiocFrame] = None,
+        metadata: Optional[dict] = None,
     ) -> None:
         """Initialize an instance of `BaseSE`.
 
         Args:
-            assays (Dict[str, MatrixTypes]): A dictionary containing matrices, with assay names as keys
+            assays (Dict[str, Any]): A dictionary containing matrices, with assay names as keys
                 and 2-dimensional matrices represented as either
                 :py:class:`~numpy.ndarray` or :py:class:`~scipy.sparse.spmatrix`.
 
@@ -83,22 +80,22 @@ def __init__(
                 All matrices in assays must be 2-dimensional and have the same shape
                 (number of rows, number of columns).
 
-            row_data (BiocOrPandasFrame, optional): Features, which must be of the same length as the rows of
+            row_data (BiocFrame, optional): Features, must be the same length as the numner of rows of
                 the matrices in assays. Features can be either a :py:class:`~pandas.DataFrame` or
                 :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
-            col_data (BiocOrPandasFrame, optional): Sample data, which must be of the same length as the
+            col_data (BiocFrame, optional): Sample data, which be the same length as the number of
                 columns of the matrices in assays. Sample Information can be either a :py:class:`~pandas.DataFrame`
                 or :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
-            metadata (Dict, optional): Additional experimental metadata describing the methods. Defaults to None.
+            metadata (dict, optional): Additional experimental metadata describing the methods. Defaults to None.
         """
 
         self._shape: Optional[Tuple] = None
 
         if assays is None or not isinstance(assays, dict) or len(assays.keys()) == 0:
             raise Exception(
-                "`assays` must be a dictionary and contain at least one matrix."
+                "`assays` must be a dictionary and contain atleast one 2-dimensional matrix."
             )
 
         self._validate_assays(assays)
@@ -106,31 +103,31 @@ def __init__(
 
         # should have _shape by now
         if self._shape is None:
-            raise TypeError("This should not happen! `assays` is not consistent.")
+            raise RuntimeError("Cannot extract shape from assays!")
 
         self._set_rows(rows)
         self._set_cols(cols)
 
         self._metadata = metadata
 
-    def _set_rows(self, rows: Optional[BiocOrPandasFrame]):
+    def _set_rows(self, rows: Optional[BiocFrame]):
         rows = (
             rows if rows is not None else BiocFrame({}, number_of_rows=self._shape[0])
         )
 
-        # if is_pandas(rows):
-        #     rows = from_pandas(rows)
+        if is_pandas(rows):
+            rows = from_pandas(rows)
 
         self._validate_rows(rows)
         self._rows = rows
 
-    def _set_cols(self, cols: Optional[BiocOrPandasFrame]):
+    def _set_cols(self, cols: Optional[BiocFrame]):
         cols = (
             cols if cols is not None else BiocFrame({}, number_of_rows=self._shape[1])
         )
 
-        # if is_pandas(cols):
-        #     cols = from_pandas(cols)
+        if is_pandas(cols):
+            cols = from_pandas(cols)
 
         self._validate_cols(cols)
         self._cols = cols
@@ -144,25 +141,23 @@ def _validate(self):
 
     def _validate_assays(
         self,
-        assays: Dict[str, MatrixTypes],
+        assays: Dict[str, Any],
     ):
         """Internal method to validate experiment data (assays).
 
         Args:
-            assays (Dict[str, MatrixTypes]): Experiment data.
+            assays (Dict[str, Any]): Experiment data.
 
         Raises:
             ValueError:
                 If ``assays`` contain more than 2 dimensions.
                 If all ``assays`` do not have the same dimensions.
-            TypeError: If ``assays`` includes an unsupported matrix representation.
+            TypeError: If ``assays`` contains an unsupported matrix representation.
         """
 
         for asy, mat in assays.items():
             if not is_matrix_like(mat):
-                raise TypeError(
-                    f"Assay: '{asy}' is not a supported matrix representation."
-                )
+                raise TypeError(f"Assay: '{asy}' is not a supported matrix type.")
 
             if len(mat.shape) > 2:
                 raise ValueError(
@@ -176,15 +171,15 @@ def _validate_assays(
 
             if mat.shape != self._shape:
                 raise ValueError(
-                    f"Assay: '{asy}' must be of shape '{self._shape}'"
+                    f"dimensions mismatch, '{asy}' must be of shape '{self._shape}'"
                     f" but provided '{mat.shape}'."
                 )
 
-    def _validate_rows(self, rows: BiocOrPandasFrame):
+    def _validate_rows(self, rows: BiocFrame):
         """Internal method to validate feature information (row_data).
 
         Args:
-            rows (BiocOrPandasFrame): Feature data frame to validate.
+            rows (BiocFrame): Features to validate.
                 Features may be either a :py:class:`~pandas.DataFrame` or
                 :py:class:`~biocframe.BiocFrame.BiocFrame`.
 
@@ -201,15 +196,15 @@ def _validate_rows(self, rows: BiocOrPandasFrame):
 
         if rows.shape[0] != self._shape[0]:
             raise ValueError(
-                f"`Features` and `assays` do not match. must be '{self._shape[0]}'"
+                f"`Features` and `assays` mismatch. Must be '{self._shape[0]}'"
                 f" but provided '{rows.shape[0]}'."
             )
 
-    def _validate_cols(self, cols: BiocOrPandasFrame):
+    def _validate_cols(self, cols: BiocFrame):
         """Internal method to validate sample information (col_data).
 
         Args:
-            cols (BiocOrPandasFrame): Sample information (col_data).
+            cols (BiocFrame): Sample information (col_data).
                 Sample may be either a :py:class:`~pandas.DataFrame` or
                 :py:class:`~biocframe.BiocFrame.BiocFrame`.
 
@@ -226,18 +221,18 @@ def _validate_cols(self, cols: BiocOrPandasFrame):
 
         if cols.shape[0] != self._shape[1]:
             raise ValueError(
-                f"`Sample` data and `assays` do not match. must be '{self._shape[1]}'"
+                f"`Sample` data and `assays` mismatch. Must be '{self._shape[1]}'"
                 f" but provided '{cols.shape[0]}'."
             )
 
     @property
     def assays(
         self,
-    ) -> Dict[str, MatrixTypes]:
-        """Retrieve all assays.
+    ) -> Dict[str, Any]:
+        """Retrieve all experiment data (assays).
 
         Returns:
-            Dict[str, MatrixTypes]: A dictionary where experiment names serve as keys,
+            Dict[str, Any]: A dictionary with experiment names as keys,
             and matrices as corresponding values.
         """
         return self._assays
@@ -245,12 +240,12 @@ def assays(
     @assays.setter
     def assays(
         self,
-        assays: Dict[str, MatrixTypes],
+        assays: Dict[str, Any],
     ):
         """Set new experiment data (assays).
 
         Args:
-            assays (Dict[str, MatrixTypes]): New assays.
+            assays (Dict[str, Any]): New assays.
         """
         self._validate_assays(assays)
         self._assays = assays
@@ -265,52 +260,55 @@ def row_data(self) -> BiocFrame:
         return self._rows
 
     @row_data.setter
-    def row_data(self, rows: Optional[BiocOrPandasFrame]):
+    def row_data(self, rows: Optional[BiocFrame]):
         """Set features.
 
         Args:
-            rows (BiocOrPandasFrame, optional): New feature information.
+            rows (BiocFrame, optional): New feature information.
                 If ``rows`` is None, an empty :py:class:`~biocframe.BiocFrame.BiocFrame`
                 object is created.
         """
         self._set_rows(rows)
 
     @property
-    def col_data(self) -> BiocOrPandasFrame:
+    def col_data(self) -> BiocFrame:
         """Get sample data.
 
         Returns:
-            BiocOrPandasFrame: Sample information.
+            BiocFrame: Sample information.
         """
         return self._cols
 
     @col_data.setter
-    def col_data(self, cols: Optional[BiocOrPandasFrame]):
+    def col_data(self, cols: Optional[BiocFrame]):
         """Set sample data.
 
         Args:
-            cols (BiocOrPandasFrame, optional): New sample data.
+            cols (BiocFrame, optional): New sample data.
                 If ``cols`` is None, an empty :py:class:`~biocframe.BiocFrame.BiocFrame`
                 object is created.
         """
         self._set_cols(cols)
 
     @property
-    def metadata(self) -> Optional[Dict]:
+    def metadata(self) -> Optional[dict]:
         """Retrieve metadata.
 
         Returns:
-            Optional[Dict]: A metadata object, typically in the form of a dictionary.
+            Optional[dict]: A metadata object, typically in the form of a dictionary.
         """
         return self._metadata
 
     @metadata.setter
-    def metadata(self, metadata: Optional[Dict]):
+    def metadata(self, metadata: Optional[dict]):
         """Set metadata.
 
         Args:
-            metadata (Optional[Dict]): New metadata object.
+            metadata (Optional[dict]): New metadata object.
         """
+        if metadata is None:
+            metadata = {}
+
         self._metadata = metadata
 
     @property
@@ -356,7 +354,7 @@ def assay_names(self, names: List[str]):
         """
         current_names = self.assay_names
         if len(names) != len(current_names):
-            raise ValueError("Length of `names` do not match the number of `assays`.")
+            raise ValueError("Length of `names` does not match the number of `assays`.")
 
         new_assays = OrderedDict()
         for idx in range(len(names)):
@@ -365,16 +363,17 @@ def assay_names(self, names: List[str]):
         self._assays = new_assays
 
     def __repr__(self) -> str:
+        current_class_const = type(self)
         pattern = (
-            f"Class BaseSE with {self.shape[0]} features and {self.shape[1]} samples \n"
+            f"Class {current_class_const.__name__} with {self.shape[0]} features and {self.shape[1]} samples \n"
             f"  assays: {', '.join(list(self.assays.keys()))} \n"
             f"  features: {self.row_data.columns if self.row_data is not None else None} \n"
             f"  sample data: {self.col_data.columns if self.col_data is not None else None}"
         )
         return pattern
 
-    def assay(self, index_or_name: Union[int, str]) -> MatrixTypes:
-        """Convenience function to access an :py:attr:`~summarizedexperiment.BaseSE.BaseSE.assays` by name or index.
+    def assay(self, index_or_name: Union[int, str]) -> Any:
+        """Convenience method to access an :py:attr:`~summarizedexperiment.BaseSE.BaseSE.assays` by name or index.
 
         Args:
             name (Union[int, str]): Name or index position of the assay.
@@ -384,7 +383,7 @@ def assay(self, index_or_name: Union[int, str]) -> MatrixTypes:
             IndexError: If index is greater than the number of assays.
 
         Returns:
-            MatrixTypes: Experiment data.
+            Any: Experiment data.
         """
         if isinstance(index_or_name, int):
             if index_or_name < 0 or index_or_name > len(self.assay_names):
@@ -405,7 +404,7 @@ def subset_assays(
         self,
         row_indices: Optional[MatrixSlicerTypes] = None,
         col_indices: Optional[MatrixSlicerTypes] = None,
-    ) -> Dict[str, MatrixTypes]:
+    ) -> Dict[str, Any]:
         """Subset all assays using a slice defined by rows and columns.
 
         If both ``row_indices`` and ``col_indices`` are None, a copy of the
@@ -417,7 +416,7 @@ def subset_assays(
                 ``row_indices`` may be a list of integer indices to subset.
 
                 Alternatively ``row_indices`` may be a boolean vector specifying
-                `True` to keep the index or `False` to remove. The length of the boolean
+                `True` to keep the row or `False` to remove. The length of the boolean
                 vector must match the number of rows in the experiment.
 
                 Alternatively, ``row_indices`` may be a :py:class:`~slice` object.
@@ -429,7 +428,7 @@ def subset_assays(
                 ``col_indices`` may be a list of integer indices to subset.
 
                 Alternatively ``col_indices`` may be a boolean vector specifying
-                `True` to keep the index or `False` to remove. The length of the boolean
+                `True` to keep the column or `False` to remove. The length of the boolean
                 vector must match the number of columns in the experiment.
 
                 Alternatively, ``col_indices`` may be a :py:class:`~slice` object.
@@ -440,11 +439,11 @@ def subset_assays(
             warning: If ``row_indices`` and ``col_indices`` are both None.
 
         Returns:
-            Dict[str, MatrixTypes]: Sliced experiment data.
+            Dict[str, Any]: Sliced experimental data.
         """
 
         if row_indices is None and col_indices is None:
-            warnings.warn("No slice is provided, this returns a copy of all assays!")
+            warn("No slice is provided, this returns a copy of all assays!")
             return self.assays.copy()
 
         new_assays = OrderedDict()
@@ -469,13 +468,13 @@ def _slice(
             args (SlicerArgTypes): Indices or names to slice. The tuple contains
                 slices along dimensions (rows, cols).
 
-                Each element in the tuple may be either a integer vector (integer positions),
+                Each element in the tuple may be either an list of indices,
                 boolean vector or :py:class:`~slice` object.
 
                 Defaults to None.
 
         Raises:
-            ValueError: If too many or too few slices provided.
+            ValueError: If too many or too few slices are provided.
 
         Returns:
             SlicerResult: The sliced tuple.
@@ -505,7 +504,7 @@ def _slice(
         if row_indices is not None and self.row_data is not None:
             if is_list_of_type(row_indices, str):
                 row_indices = get_indexes_from_names(
-                    get_rownames(self.row_data), row_indices
+                    rownames(self.row_data), row_indices
                 )
             elif is_list_of_type(row_indices, bool):
                 if len(row_indices) != self.shape[0]:
@@ -521,12 +520,12 @@ def _slice(
                 else:
                     new_rows = new_rows[row_indices, :]
             else:
-                raise TypeError("`row_indices` is not supported!")
+                raise TypeError("`row_indices` is not a supported type!")
 
         if col_indices is not None and self.col_data is not None:
             if is_list_of_type(col_indices, str):
                 col_indices = get_indexes_from_names(
-                    get_rownames(self.col_data), col_indices
+                    rownames(self.col_data), col_indices
                 )
             elif is_list_of_type(col_indices, bool):
                 if len(col_indices) != self.shape[1]:
@@ -542,7 +541,7 @@ def _slice(
                 else:
                     new_cols = new_cols[col_indices, :]
             else:
-                raise TypeError("`col_indices` not supported!")
+                raise TypeError("`col_indices` not a supported type!")
 
         new_assays = self.subset_assays(
             row_indices=row_indices, col_indices=col_indices
@@ -557,7 +556,7 @@ def row_names(self) -> List[str]:
         Returns:
             List[str]: List of row names.
         """
-        return get_rownames(self.row_data)
+        return rownames(self.row_data)
 
     @row_names.setter
     def row_names(self, names: List[str]):
@@ -581,7 +580,7 @@ def colnames(self) -> List[str]:
         Returns:
             List[str]: List of sample names.
         """
-        return get_colnames(self.col_data)
+        return colnames(self.col_data)
 
     @colnames.setter
     def colnames(self, names: List[str]):
@@ -599,7 +598,7 @@ def colnames(self, names: List[str]):
         self._cols = set_colnames(self.col_data, names)
 
     def to_anndata(self):
-        """Transform :py:class:`summarizedexperiment.BaseSE`-like into a :py:class:`~anndata.AnnData` representation.
+        """Coerce :py:class:`summarizedexperiment.BaseSE`-like into an :py:class:`~anndata.AnnData` representation.
 
         Returns:
             AnnData: An `AnnData` representation of the experiment.
diff --git a/src/summarizedexperiment/RangedSummarizedExperiment.py b/src/summarizedexperiment/RangedSummarizedExperiment.py
index a1dfe46..e1bca66 100644
--- a/src/summarizedexperiment/RangedSummarizedExperiment.py
+++ b/src/summarizedexperiment/RangedSummarizedExperiment.py
@@ -1,10 +1,11 @@
-from typing import Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 import numpy as np
+from biocframe import BiocFrame
 from genomicranges import GenomicRanges, GenomicRangesList, SeqInfo
 
 from .SummarizedExperiment import SummarizedExperiment
-from .types import BiocOrPandasFrame, MatrixTypes, SlicerArgTypes
+from .types import SlicerArgTypes
 
 __author__ = "jkanche"
 __copyright__ = "jkanche"
@@ -57,14 +58,14 @@ class RangedSummarizedExperiment(SummarizedExperiment):
 
     The key difference between this and `SummarizedExperiment` is enforcing type for
     feature information (`row_ranges`), must be a `GenomicRanges` object. This allows us to
-    provides new methods, to perform genomic range based operations over experimental data.
+    implement methods to perform genomic range based operations over experimental data.
 
     Note: If ``row_ranges`` is empty, None or not a
     :py:class:`genomicranges.GenomicRanges.GenomicRanges` object, use a
     :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` instead.
 
     Attributes:
-        assays (Dict[str, MatrixTypes]): A dictionary containing matrices, with assay names as keys
+        assays (Dict[str, Any]): A dictionary containing matrices, with assay names as keys
             and 2-dimensional matrices represented as either
             :py:class:`~numpy.ndarray` or :py:class:`~scipy.sparse.spmatrix`.
 
@@ -77,11 +78,11 @@ class RangedSummarizedExperiment(SummarizedExperiment):
         row_ranges (GRangesOrGRangesList, optional): Genomic features, must be the same length as
             rows of the matrices in assays.
 
-        row_data (BiocOrPandasFrame, optional): Features, which must be of the same length as the rows of
+        row_data (BiocFrame, optional): Features, which must be of the same length as the rows of
             the matrices in assays. Features can be either a :py:class:`~pandas.DataFrame` or
             :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
-        col_data (BiocOrPandasFrame, optional): Sample data, which must be of the same length as the
+        col_data (BiocFrame, optional): Sample data, which must be of the same length as the
             columns of the matrices in assays. Sample Information can be either a :py:class:`~pandas.DataFrame`
             or :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
@@ -90,16 +91,16 @@ class RangedSummarizedExperiment(SummarizedExperiment):
 
     def __init__(
         self,
-        assays: Dict[str, MatrixTypes],
+        assays: Dict[str, Any],
         row_ranges: Optional[GRangesOrGRangesList] = None,
-        row_data: Optional[BiocOrPandasFrame] = None,
-        col_data: Optional[BiocOrPandasFrame] = None,
+        row_data: Optional[BiocFrame] = None,
+        col_data: Optional[BiocFrame] = None,
         metadata: Optional[Dict] = None,
     ) -> None:
         """Initialize a `RangedSummarizedExperiment` (RSE) object.
 
         Args:
-            assays (Dict[str, MatrixTypes]): Dictionary
+            assays (Dict[str, Any]): Dictionary
                 of matrices, with assay names as keys and 2-dimensional matrices represented as
                 :py:class:`~numpy.ndarray` or :py:class:`scipy.sparse.spmatrix` matrices.
 
@@ -112,14 +113,14 @@ def __init__(
             row_ranges (GRangesOrGRangesList, optional): Genomic features, must be the same length as
                 rows of the matrices in assays.
 
-            row_data (BiocOrPandasFrame, optional): Features, must be the same length as
+            row_data (BiocFrame, optional): Features, must be the same length as
                 rows of the matrices in assays.
 
                 Features may be either a :py:class:`~pandas.DataFrame` or
                 :py:class:`~biocframe.BiocFrame.BiocFrame`.
 
                 Defaults to None.
-            col_data (BiocOrPandasFrame, optional): Sample data, must be
+            col_data (BiocFrame, optional): Sample data, must be
                 the same length as columns of the matrices in assays.
 
                 Sample Information may be either a :py:class:`~pandas.DataFrame` or
@@ -148,10 +149,7 @@ def _validate_row_ranges(self, row_ranges: GRangesOrGRangesList):
             `row_ranges` & `assays`.
             TypeError: If `row_ranges` is not a `GenomicRanges` or `GenomicRangesList`.
         """
-        if not (
-            isinstance(row_ranges, GenomicRanges)
-            or isinstance(row_ranges, GenomicRangesList)
-        ):
+        if not isinstance(row_ranges, (GenomicRanges, GenomicRangesList)):
             raise TypeError(
                 "`row_ranges` must be a `GenomicRanges` or `GenomicRangesList`"
                 f" , provided {type(row_ranges)}."
@@ -245,11 +243,9 @@ def __getitem__(
             args (SlicerArgTypes): Indices or names to slice. The tuple contains
                 slices along dimensions (rows, cols).
 
-                Each element in the tuple, might be either a integer vector (integer positions),
+                Each element in the tuple may be either an list of indices,
                 boolean vector or :py:class:`~slice` object.
 
-                Defaults to None.
-
         Raises:
             ValueError: If too many or too few slices are provided.
 
@@ -262,7 +258,8 @@ def __getitem__(
         if sliced_objs.row_indices is not None and self.row_ranges is not None:
             new_row_ranges = self.row_ranges[sliced_objs.row_indices]
 
-        return RangedSummarizedExperiment(
+        current_class_const = type(self)
+        return current_class_const(
             assays=sliced_objs.assays,
             row_ranges=new_row_ranges,
             row_data=sliced_objs.row_data,
@@ -271,11 +268,13 @@ def __getitem__(
         )
 
     def __repr__(self) -> str:
+        current_class_const = type(self)
         pattern = (
-            f"Class RangedSummarizedExperiment with {self.shape[0]} features and {self.shape[1]} "
+            f"Class {current_class_const.__name__} with {self.shape[0]} features and {self.shape[1]} "
             "samples \n"
             f"  assays: {list(self.assays.keys())} \n"
             f"  row_data: {self.row_data.columns if self.row_data is not None else None} \n"
+            f"  row_ranges: {self.row_ranges.columns if self.row_ranges is not None else None} \n"
             f"  col_data: {self.col_data.columns if self.col_data is not None else None}"
         )
         return pattern
@@ -315,8 +314,7 @@ def nearest(
                 Defaults to False.
 
         Raises:
-            TypeError: If ``query`` is not a ``RangedSummarizedExperiment``
-                or ``GenomicRanges``.
+            TypeError: If query is neither `RangedSummarizedExperiment` nor `GenomicRanges`.
 
         Returns:
             (List[Optional[int]], optional): List of possible `hit` indices
@@ -345,8 +343,7 @@ def precede(
             ignore_strand (bool, optional): Whether to ignore strand. Defaults to False.
 
         Raises:
-            TypeError: If ``query`` is not a ``RangedSummarizedExperiment`` or
-            ``GenomicRanges``.
+            TypeError: If query is neither `RangedSummarizedExperiment` nor `GenomicRanges`.
 
         Returns:
             (List[Optional[int]], optional): List of possible hit indices
@@ -629,7 +626,7 @@ def find_overlaps(
             ignore_strand (bool, optional): Whether to ignore strand.. Defaults to False.
 
         Raises:
-            TypeError: If query is not a `RangedSummarizedExperiment` or `GenomicRanges`.
+            TypeError: If query is neither `RangedSummarizedExperiment` nor `GenomicRanges`.
 
         Returns:
             ("RangedSummarizedExperiment", optional): A `RangedSummarizedExperiment` object
@@ -676,7 +673,7 @@ def subset_by_overlaps(
             ignore_strand (bool, optional): Whether to ignore strand.. Defaults to False.
 
         Raises:
-            TypeError: If query is not a `RangedSummarizedExperiment` or `GenomicRanges`.
+            TypeError: If query is neither `RangedSummarizedExperiment` nor `GenomicRanges`.
 
         Returns:
             Optional["RangedSummarizedExperiment"]: A new `RangedSummarizedExperiment`
@@ -733,7 +730,7 @@ def sort(
         """
         order = self.row_ranges._generic_order(ignore_strand=ignore_strand)
 
-        if decreasing:
+        if decreasing is True:
             order = order[::-1]
 
         new_order = order.to_list()
diff --git a/src/summarizedexperiment/SummarizedExperiment.py b/src/summarizedexperiment/SummarizedExperiment.py
index ab205f9..698cdf2 100644
--- a/src/summarizedexperiment/SummarizedExperiment.py
+++ b/src/summarizedexperiment/SummarizedExperiment.py
@@ -12,14 +12,14 @@
 
 
 class SummarizedExperiment(BaseSE):
-    """Container to represents genomic experiment data (`assays`), features (`row_data`), sample data (`col_data`) and
+    """Container to represent genomic experimental data (`assays`), features (`row_data`), sample data (`col_data`) and
     any other `metadata`.
 
     SummarizedExperiment follows the R/Bioconductor specification; rows are features, columns
     are samples.
 
     Attributes:
-        assays (Dict[str, MatrixTypes]): A dictionary containing matrices, with assay names as keys
+        assays (Dict[str, Any]): A dictionary containing matrices, with assay names as keys
             and 2-dimensional matrices represented as either
             :py:class:`~numpy.ndarray` or :py:class:`~scipy.sparse.spmatrix`.
 
@@ -29,11 +29,11 @@ class SummarizedExperiment(BaseSE):
             All matrices in assays must be 2-dimensional and have the same shape
             (number of rows, number of columns).
 
-        row_data (BiocOrPandasFrame, optional): Features, which must be of the same length as the rows of
+        row_data (BiocFrame, optional): Features, must be the same length as the numner of rows of
             the matrices in assays. Features can be either a :py:class:`~pandas.DataFrame` or
             :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
-        col_data (BiocOrPandasFrame, optional): Sample data, which must be of the same length as the
+        col_data (BiocFrame, optional): Sample data, which be the same length as the number of
             columns of the matrices in assays. Sample Information can be either a :py:class:`~pandas.DataFrame`
             or :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
@@ -50,7 +50,7 @@ def __init__(
         """Initialize a Summarized Experiment (SE).
 
         Args:
-            assays (Dict[str, MatrixTypes]): A dictionary containing matrices, with assay names as keys
+            assays (Dict[str, Any]): A dictionary containing matrices, with assay names as keys
                 and 2-dimensional matrices represented as either
                 :py:class:`~numpy.ndarray` or :py:class:`~scipy.sparse.spmatrix`.
 
@@ -60,11 +60,11 @@ def __init__(
                 All matrices in assays must be 2-dimensional and have the same shape
                 (number of rows, number of columns).
 
-            row_data (BiocOrPandasFrame, optional): Features, which must be of the same length as the rows of
+            row_data (BiocFrame, optional): Features, must be the same length as the numner of rows of
                 the matrices in assays. Features can be either a :py:class:`~pandas.DataFrame` or
                 :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
-            col_data (BiocOrPandasFrame, optional): Sample data, which must be of the same length as the
+            col_data (BiocFrame, optional): Sample data, which be the same length as the number of
                 columns of the matrices in assays. Sample Information can be either a :py:class:`~pandas.DataFrame`
                 or :py:class:`~biocframe.BiocFrame.BiocFrame`. Defaults to None.
 
@@ -88,17 +88,19 @@ def __getitem__(
             args (SlicerArgTypes): Indices or names to slice. The tuple contains
                 slices along dimensions (rows, cols).
 
-                Each element in the tuple, might be either a integer vector (integer positions),
-                boolean vector or :py:class:`~slice` object. Defaults to None.
+                Each element in the tuple may be either an list of indices,
+                boolean vector or :py:class:`~slice` object.
 
         Raises:
-            ValueError: If too many or too few slices provided.
+            ValueError: If too many or too few slices are provided.
 
         Returns:
-            SummarizedExperiment: Sliced `SummarizedExperiment` object.
+            The same type as caller, with the sliced entries.
         """
         sliced_objs = self._slice(args)
-        return SummarizedExperiment(
+
+        current_class_const = type(self)
+        return current_class_const(
             assays=sliced_objs.assays,
             row_data=sliced_objs.row_data,
             col_data=sliced_objs.col_data,
@@ -106,8 +108,9 @@ def __getitem__(
         )
 
     def __repr__(self) -> str:
+        current_class_const = type(self)
         pattern = (
-            f"Class SummarizedExperiment with {self.shape[0]} features and {self.shape[1]} "
+            f"Class {current_class_const.__name__} with {self.shape[0]} features and {self.shape[1]} "
             "samples \n"
             f"  assays: {list(self.assays.keys())} \n"
             f"  row_data: {self.row_data.columns if self.row_data is not None else None} \n"
diff --git a/src/summarizedexperiment/dispatchers/__init__.py b/src/summarizedexperiment/dispatchers/__init__.py
deleted file mode 100644
index a38de3f..0000000
--- a/src/summarizedexperiment/dispatchers/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .colnames import get_colnames, set_colnames
-from .rownames import get_rownames, set_rownames
diff --git a/src/summarizedexperiment/dispatchers/colnames.py b/src/summarizedexperiment/dispatchers/colnames.py
deleted file mode 100644
index 6ffd8e3..0000000
--- a/src/summarizedexperiment/dispatchers/colnames.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from functools import singledispatch
-from typing import List
-
-from biocframe import BiocFrame
-from pandas import DataFrame
-
-__author__ = "jkanche"
-__copyright__ = "jkanche"
-__license__ = "MIT"
-
-
-@singledispatch
-def get_colnames(x) -> List[str]:
-    """Access column names from various representations.
-
-    Args:
-        x: Any object.
-
-            ``x`` may be a :py:class:`~pandas.DataFrame`.
-
-            Alternatively, ``x`` may be a :py:class:`~biocframe.BiocFrame.BiocFrame` object.
-
-            Alternatively, ``x`` may also contain a property or attribute ``colnames`` for
-            custom representations.
-
-    Raises:
-        NotImplementedError: If ``x`` is not a supported type.
-
-    Returns:
-        List[str]: List of column names.
-    """
-    if hasattr(x, "colnames"):
-        return x.colnames
-
-    raise NotImplementedError(f"`colnames` is not supported for class: '{type(x)}'.")
-
-
-@get_colnames.register
-def _(x: DataFrame) -> List[str]:
-    return x.index.tolist()
-
-
-@get_colnames.register
-def _(x: BiocFrame) -> List[str]:
-    return x.row_names
-
-
-@singledispatch
-def set_colnames(x, names: List[str]):
-    """Set column names for various representations.
-
-    Args:
-        x: Any object.
-
-            ``x`` may be a :py:class:`~pandas.DataFrame`.
-
-            Alternatively, ``x`` may be a :py:class:`biocframe.BiocFrame.BiocFrame` object.
-
-            Alternatively, ``x`` may also contain a property or attribute ``colnames`` for
-            custom representations.
-
-        names (List[str]): New names.
-
-    Raises:
-        NotImplementedError: if type is not supported.
-
-    Returns:
-        An object with the same type as ``x``.
-    """
-    raise NotImplementedError(
-        f"`set_colnames` is not supported for class: '{type(x)}'."
-    )
-
-
-@set_colnames.register
-def _(x: DataFrame, names: List[str]) -> DataFrame:
-    x.index = names
-    return x
-
-
-@set_colnames.register
-def _(x: BiocFrame, names: List[str]) -> BiocFrame:
-    x.row_names = names
-    return x
diff --git a/src/summarizedexperiment/dispatchers/rownames.py b/src/summarizedexperiment/dispatchers/rownames.py
deleted file mode 100644
index 26ac083..0000000
--- a/src/summarizedexperiment/dispatchers/rownames.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from functools import singledispatch
-from typing import Any, List
-
-from biocframe import BiocFrame
-from pandas import DataFrame
-
-__author__ = "jkanche"
-__copyright__ = "jkanche"
-__license__ = "MIT"
-
-
-@singledispatch
-def get_rownames(x) -> List[str]:
-    """Access row names from various representations.
-
-    Args:
-        x: Any object.
-
-        ``x`` may be a :py:class:`~pandas.DataFrame`.
-
-        Alternatively, ``x`` may be a :py:class:`~biocframe.BiocFrame.BiocFrame` object.
-
-        Alternatively, ``x`` may also contain a property or attribute ``row_names`` for
-        custom representations.
-
-    Raises:
-        NotImplementedError: If ``x`` is not a supported type.
-
-    Returns:
-        List[str]: List of row names.
-    """
-    if hasattr(x, "row_names"):
-        return x.row_names
-
-    raise NotImplementedError(f"`row_names` do not exist for class: '{type(x)}'.")
-
-
-@get_rownames.register
-def _(x: DataFrame) -> List[str]:
-    return x.index.tolist()
-
-
-@get_rownames.register
-def _(x: BiocFrame) -> List[str]:
-    return x.row_names
-
-
-@singledispatch
-def set_rownames(x: Any, names: List[str]):
-    """Set row names for various representations.
-
-    Args:
-        x (Any): supported object.
-
-        ``x`` may be a :py:class:`~pandas.DataFrame`.
-
-        Alternatively, ``x`` may be a :py:class:`~biocframe.BiocFrame.BiocFrame` object.
-
-        Alternatively, ``x`` may also contain a property or attribute ``row_names`` for
-        custom representations.
-
-        names (List[str]): New names.
-
-    Raises:
-        NotImplementedError: If ``x`` is not a supported type.
-
-    Returns:
-        An object with the same type as ``x``.
-    """
-    raise NotImplementedError(f"Cannot set row_names for class: {type(x)}")
-
-
-@set_rownames.register
-def _(x: DataFrame, names: List[str]) -> List[str]:
-    x.index = names
-    return x
-
-
-@set_rownames.register
-def _(x: BiocFrame, names: List[str]) -> List[str]:
-    x.row_names = names
-    return x
diff --git a/src/summarizedexperiment/type_checks.py b/src/summarizedexperiment/type_checks.py
index f2f5feb..932fed0 100644
--- a/src/summarizedexperiment/type_checks.py
+++ b/src/summarizedexperiment/type_checks.py
@@ -21,21 +21,6 @@ def is_bioc_or_pandas_frame(x: Any) -> bool:
     return is_pandas(x) or isinstance(x, BiocFrame)
 
 
-# def is_gr_or_rse(x: Union[GenomicRanges, RangedSummarizedExperiment]):
-#     """Check if the object is either a `RangedSummarizedExperiment` or `GenomicRanges`.
-
-#     Args:
-#         x (Union[GenomicRanges, RangedSummarizedExperiment]): object to check.
-
-#     Raises:
-#         TypeError: object is not a `RangedSummarizedExperiment` or `GenomicRanges`.
-#     """
-#     if not (isinstance(x, RangedSummarizedExperiment) or isinstance(x, GenomicRanges)):
-#         raise TypeError(
-#             "object is not a `RangedSummarizedExperiment` or `GenomicRanges`"
-#         )
-
-
 # def _get_python_minor():
 #     return version_info[1] < 8
 # Expectations on Assays, these should be matrices or matrix-like objects
@@ -70,21 +55,6 @@ def is_matrix_like(x: Any) -> bool:
     return hasattr(x, "__getitem__") and hasattr(x, "shape")
 
 
-def is_list_of_type(x: Any, target_type: Callable) -> bool:
-    """Checks if ``x`` is a list or tuple and and whether all elements are of the same type.
-
-    Args:
-        x (Any): Any object.
-        target_type (callable): Type to check for, e.g. ``str``, ``int``.
-
-    Returns:
-        bool: True if ``x`` is :py:class:`~list` and all elements are of the same type.
-    """
-    return (isinstance(x, list) or isinstance(x, tuple)) and all(
-        isinstance(item, target_type) for item in x
-    )
-
-
 def is_list_of_subclass(x: Any, target_type: Callable) -> bool:
     """Checks if all provided objects subclass of ``target_type``.
 
@@ -95,7 +65,7 @@ def is_list_of_subclass(x: Any, target_type: Callable) -> bool:
     Returns:
         bool: True if ``x`` is :py:class:`~list` and all objects are derivatives of the same class.
     """
-    return (isinstance(x, list) or isinstance(x, tuple)) and all(
+    return (isinstance(x, (list, tuple))) and all(
         issubclass(type(item), target_type) for item in x
     )
 
diff --git a/src/summarizedexperiment/types.py b/src/summarizedexperiment/types.py
index f5a5978..9c904da 100644
--- a/src/summarizedexperiment/types.py
+++ b/src/summarizedexperiment/types.py
@@ -1,18 +1,10 @@
 from collections import namedtuple
 from typing import List, Tuple, Union
 
-import numpy as np
-import pandas as pd
-from biocframe import BiocFrame
-from scipy import sparse as sp
-
 __author__ = "jkanche"
 __copyright__ = "jkanche"
 __license__ = "MIT"
 
-MatrixTypes = Union[np.ndarray, sp.spmatrix]
-ArrayTypes = Union[np.ndarray, sp.lil_matrix]
-BiocOrPandasFrame = Union[pd.DataFrame, BiocFrame]
 MatrixSlicerTypes = Union[List[int], List[bool], slice]
 SlicerTypes = Union[List[int], List[bool], List[str], slice]
 SlicerArgTypes = Union[Tuple[SlicerTypes], List[SlicerTypes], slice]
diff --git a/src/summarizedexperiment/utils/combiners.py b/src/summarizedexperiment/utils/combiners.py
index 1116fc8..733fae3 100644
--- a/src/summarizedexperiment/utils/combiners.py
+++ b/src/summarizedexperiment/utils/combiners.py
@@ -1,11 +1,11 @@
 from typing import Dict, List, Literal, Tuple
 
 from biocframe import BiocFrame
+from biocgenerics import rownames
 from numpy import argwhere, find_common_type, ndarray
 from pandas import DataFrame, Index, concat
 from scipy.sparse import lil_matrix
 
-from ..dispatchers import get_rownames
 from ..types import ArrayTypes, BiocOrPandasFrame
 from .validators import validate_names, validate_shapes
 
@@ -97,8 +97,8 @@ def combine_frames(
 
     concat_df = concat(all_as_pandas, axis=axis)
 
-    if (use_names is False) and (get_rownames(x[0]) is not None):
-        concat_df.index = get_rownames(x[0])
+    if (use_names is False) and (rownames(x[0]) is not None):
+        concat_df.index = rownames(x[0])
 
     if remove_duplicate_columns:
         return _remove_duplicate_columns(concat_df)