diff --git a/README.md b/README.md
index 9bec1f4..822ec54 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,63 @@
-STATUS: Beta
-
 # pandasaurus_cxg
 
-Ontology enrichment tool for [CxG standard](https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/3.0.0/schema.md) [AnnData files](https://anndata.readthedocs.io/en/latest/).
+STATUS: early Beta
+
+A library for retreiving and leveraging the semantic context of ontogy annotation in [CxG standard](https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/3.0.0/schema.md) [AnnData files](https://anndata.readthedocs.io/en/latest/).
 
 Slide summarising intended functionality
 ![image](https://github.com/INCATools/pandasaurus_cxg/assets/112839/3082dcd2-dd2f-469d-9076-4eabcc83130d)
 
+## Installation
+
+Available on [PyPi](https://pypi.org/project/pandasaurus-cxg/0.1.1/)
+
+$ pip3 install pandasaurus_cxg
+
+## Usage
+
+The `AnndataEnricher` and `AnndataAnalyzer` classes can be used both individually and in conjunction with the `AnndataEnrichmentAnalyzer` wrapper class. The `AnndataEnrichmentAnalyzer` class serves as a convenient way to leverage the functionalities of both `AnndataEnricher` and `AnndataAnalyzer`.
+
+### Using AnndataEnricher and AnndataAnalyzer Individually
+
+You can use the `AnndataEnricher` and `AnndataAnalyzer` classes separately to perform specific tasks on your data. For instance, `AnndataEnricher` facilitates data enrichment, while `AnndataAnalyzer` provides various analysis tools for Anndata objects.
+
+```python
+from pandasaurus_cxg.anndata_enricher import AnndataEnricher
+ade = AnndataEnricher.from_file_path("test/data/modified_human_kidney.h5ad")
+ade.simple_enrichment()
+ade.minimal_slim_enrichment(["blood_and_immune_upper_slim"])
+```
+
+```python
+from pandasaurus_cxg.anndata_analyzer import AnndataAnalyzer
+ada = AnndataAnalyzer.from_file_path("./immune_example.h5ad", author_cell_type_list = ['subclass.full', 'subclass.l3', 'subclass.l2', 'subclass.l1', 'class', 'author_cell_type'])
+ada.co_annotation_report()
+```
+
+### Using AnndataEnrichmentAnalyzer Wrapper
+
+The AnndataEnrichmentAnalyzer class wraps the functionality of both AnndataEnricher and AnndataAnalyzer, offering a seamless way to perform enrichment and analysis in one go.
+
+```python
+from pandasaurus_cxg.enrichment_analysis import AnndataEnrichmentAnalyzer
+from pandasaurus_cxg.graph_generator.graph_generator import GraphGenerator
+aea = AnndataEnrichmentAnalyzer("test/data/modified_human_kidney.h5ad")
+aea.contextual_slim_enrichment()
+aea.co_annotation_report()
+gg = GraphGenerator(aea)
+gg.generate_rdf_graph()
+gg.set_label_adding_priority(["class", "cell_type", "subclass.l1", "subclass.l1", "subclass.full", "subclass.l2", "subclass.l3"])
+gg.add_label_to_terms()
+gg.enrich_rdf_graph()
+gg.save_rdf_graph(file_name="kidney_new", _format="ttl")
+```
+More examples and detailed explanation can be found in jupyter notebook given in [Snippets](#Snippets)
+
+## Snippets
+
+https://github.com/INCATools/pandasaurus_cxg/blob/roadmap/walkthrough.ipynb
+
+## Roadmap
+
+https://github.com/INCATools/pandasaurus_cxg/blob/roadmap/ROADMAP.md
+
diff --git a/ROADMAP.md b/ROADMAP.md
new file mode 100644
index 0000000..a4214fa
--- /dev/null
+++ b/ROADMAP.md
@@ -0,0 +1,52 @@
+## Pandasaurus_cxg Roadmap
+
+* Generate & release integrated doc from PyDoc - including links to Tutorial notebooks
+
+    (potential framework - Sphinx)
+
+* Testing:
+   * Test against a range of datasets on CxG to find bugs and performance issues
+   * User testing - recruit friendly bioinformaticians to give feedback on functionality and usability
+
+* Extend basic enrichment methods to include number of hops from term.
+
+* Add support for CxG schema validation (via dependency on official lib)
+
+  This may not be needed for files downloaded from CxG, but aim is in part to promote the standard more generally so aims to be ready for files from other sources.
+
+* Add semantic context queries
+  
+  (Dependency - add abstracted most-specific subject/object queries to pandasaurus)
+  * CL-Pro
+  * CL-GO & GO-CL
+  * HPO-CL & MP-CL
+  * MONDO-CL
+  * OBA-CL
+ 
+* Add interface to QuickGO to pull gene associations.
+  
+  Can we use an existing lib for this?
+
+* Add interface to Monarch API to pull gene associations for Mondo, HP, MP, OBA.
+  
+  Can we use an existing lib for this or collaborate with Monarch on one?
+
+* Add support for queries for gene sets and general classes from disease metadata term.
+
+* Extend support for filtering on metadata before analysis
+  
+* Add library of author cell type fields for CxG hosted datasets where this has been curated
+
+* Add support for cell type annotation schema (CAP)
+
+
+## Potential future functionality
+
+Both of these are probably better served by workflows with existing libraries
+
+- Automatic Cross checking retrieved gene sets against cluster expression
+- interfacing with standard enrichment tools 
+
+
+
+
diff --git a/pandasaurus_cxg/anndata_analyzer.py b/pandasaurus_cxg/anndata_analyzer.py
index dde4321..9ece72c 100644
--- a/pandasaurus_cxg/anndata_analyzer.py
+++ b/pandasaurus_cxg/anndata_analyzer.py
@@ -192,8 +192,8 @@ def _remove_duplicates(data: List[List[str]]):
     @staticmethod
     def _assign_predicate_column(co_oc, field_name_1, field_name_2):
         # Group by field_name_2 and field_name_1 to create dictionaries
-        field_name_2_dict = co_oc.groupby(field_name_2)[field_name_1].apply(list).to_dict()
-        field_name_1_dict = co_oc.groupby(field_name_1)[field_name_2].apply(list).to_dict()
+        field_name_2_dict = co_oc.groupby(field_name_2, observed=True)[field_name_1].apply(list).to_dict()
+        field_name_1_dict = co_oc.groupby(field_name_1, observed=True)[field_name_2].apply(list).to_dict()
         # Assign the "predicate" column using self._assign_predicate method
         co_oc["predicate"] = co_oc.apply(
             AnndataAnalyzer._assign_predicate,
diff --git a/poetry.lock b/poetry.lock
index f351c28..14599f1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -357,6 +357,64 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
 [[package]]
 name = "cssselect"
 version = "1.2.0"
@@ -392,6 +450,17 @@ pandas = ["pandas"]
 rdflib = ["rdflib"]
 tests = ["coverage", "pytest"]
 
+[[package]]
+name = "cycler"
+version = "0.11.0"
+description = "Composable style cycles"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
 [[package]]
 name = "deprecated"
 version = "1.2.14"
@@ -566,6 +635,63 @@ isort = ">=5.0.0,<6"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "fonttools"
+version = "4.42.1"
+description = "Tools to manipulate font files"
+optional = false
+python-versions = ">=3.8"
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 [[package]]
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 version = "0.7.0"
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 [[package]]
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+    {file = "Pillow-10.0.0.tar.gz", hash = "sha256:9c82b5b3e043c7af0d95792d0d20ccf68f61a1fec6b3530e718b688422727396"},
+]
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+
 [[package]]
 name = "platformdirs"
 version = "3.10.0"
@@ -1985,13 +2378,13 @@ jsonasobj = ">=1.2.1"
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.2"
+version = "10.2.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pymdown_extensions-10.2-py3-none-any.whl", hash = "sha256:fbb86243db9a681602e3b869deef000211c55d0261015a5cc41d6f34d2afc57f"},
-    {file = "pymdown_extensions-10.2.tar.gz", hash = "sha256:06042274876eb4267f12a389daf505eabaebc38bdca26725560c9afda5867549"},
+    {file = "pymdown_extensions-10.2.1-py3-none-any.whl", hash = "sha256:bded105eb8d93f88f2f821f00108cb70cef1269db6a40128c09c5f48bfc60ea4"},
+    {file = "pymdown_extensions-10.2.1.tar.gz", hash = "sha256:d0c534b4a5725a4be7ccef25d65a4c97dba58b54ad7c813babf0eb5ba9c81591"},
 ]
 
 [package.dependencies]
@@ -2003,13 +2396,13 @@ extra = ["pygments (>=2.12)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.1"
+version = "3.0.9"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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+    {file = "pyparsing-3.0.9-py3-none-any.whl", hash = "sha256:5026bae9a10eeaefb61dab2f09052b9f4307d44aee4eda64b309723d8d206bbc"},
+    {file = "pyparsing-3.0.9.tar.gz", hash = "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb"},
 ]
 
 [package.extras]
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@@ -2165,8 +2559,15 @@ files = [
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+    {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
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+    {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
+    {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"},
     {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
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     {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
@@ -2183,6 +2584,7 @@ files = [
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     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
     {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
+    {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
     {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
     {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
     {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
@@ -2190,6 +2592,7 @@ files = [
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     {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
+    {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"},
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     {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
@@ -3139,4 +3542,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "1ab81f1070c248b5447e2a2e9394499425b1d9a4d5daedcc9f269c7e57b1db57"
+content-hash = "00211564ab95971db40640f6b956a157e8cf036c380fed757142e104557befe0"
diff --git a/pyproject.toml b/pyproject.toml
index ae0e274..2e6c63a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pandasaurus-cxg"
-version = "0.1.0"
+version = "0.1.2"
 description = "Ontology enrichment tool for CxG standard AnnData files."
 authors = ["Ugur Bayindir <ugur@ebi.ac.uk>"]
 license = "http://www.apache.org/licenses/LICENSE-2.0"
@@ -10,10 +10,11 @@ packages = [{include = "pandasaurus_cxg"}]
 [tool.poetry.dependencies]
 python = "^3.9"
 pandas = "^2.0.2"
-pandasaurus = "0.3.2"
 anndata = "^0.9.1"
 rdflib = "^6.3.2"
 oaklib = "^0.5.13"
+matplotlib = "^3.7.2"
+pandasaurus = "^0.3.3"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.3.1"
diff --git a/walkthrough.ipynb b/walkthrough.ipynb
index 64d708e..19adf08 100644
--- a/walkthrough.ipynb
+++ b/walkthrough.ipynb
@@ -653,48 +653,48 @@
        "      <td>CL:0000798</td>\n",
        "      <td>gamma-delta T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000842</td>\n",
-       "      <td>mononuclear cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>CL:0000798</td>\n",
        "      <td>gamma-delta T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0000842</td>\n",
+       "      <td>mononuclear cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>CL:0000809</td>\n",
        "      <td>double-positive, alpha-beta thymocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0002420</td>\n",
+       "      <td>immature T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>CL:0000809</td>\n",
        "      <td>double-positive, alpha-beta thymocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000789</td>\n",
-       "      <td>alpha-beta T cell</td>\n",
+       "      <td>CL:0000842</td>\n",
+       "      <td>mononuclear cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>CL:0000809</td>\n",
        "      <td>double-positive, alpha-beta thymocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000842</td>\n",
-       "      <td>mononuclear cell</td>\n",
+       "      <td>CL:0000789</td>\n",
+       "      <td>alpha-beta T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>CL:0000809</td>\n",
        "      <td>double-positive, alpha-beta thymocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002420</td>\n",
-       "      <td>immature T cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -733,80 +733,80 @@
        "      <td>CL:0000895</td>\n",
        "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000842</td>\n",
-       "      <td>mononuclear cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>CL:0000895</td>\n",
        "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0000789</td>\n",
+       "      <td>alpha-beta T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>CL:0000895</td>\n",
        "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000789</td>\n",
-       "      <td>alpha-beta T cell</td>\n",
+       "      <td>CL:0000842</td>\n",
+       "      <td>mononuclear cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>CL:0000897</td>\n",
        "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000842</td>\n",
-       "      <td>mononuclear cell</td>\n",
+       "      <td>CL:0000813</td>\n",
+       "      <td>memory T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>CL:0000897</td>\n",
        "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000813</td>\n",
-       "      <td>memory T cell</td>\n",
+       "      <td>CL:0000842</td>\n",
+       "      <td>mononuclear cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>CL:0000897</td>\n",
        "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0000789</td>\n",
+       "      <td>alpha-beta T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>CL:0000897</td>\n",
        "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000789</td>\n",
-       "      <td>alpha-beta T cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>CL:0000900</td>\n",
        "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0000789</td>\n",
+       "      <td>alpha-beta T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>CL:0000900</td>\n",
        "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000789</td>\n",
-       "      <td>alpha-beta T cell</td>\n",
+       "      <td>CL:0000842</td>\n",
+       "      <td>mononuclear cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>CL:0000900</td>\n",
        "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000842</td>\n",
-       "      <td>mononuclear cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
@@ -821,16 +821,16 @@
        "      <td>CL:0000909</td>\n",
        "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000842</td>\n",
-       "      <td>mononuclear cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>CL:0000909</td>\n",
        "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0000842</td>\n",
+       "      <td>mononuclear cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
@@ -845,8 +845,8 @@
        "      <td>CL:0000940</td>\n",
        "      <td>mucosal invariant T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0000842</td>\n",
+       "      <td>mononuclear cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
@@ -861,24 +861,24 @@
        "      <td>CL:0000940</td>\n",
        "      <td>mucosal invariant T cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000842</td>\n",
-       "      <td>mononuclear cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>CL:0000980</td>\n",
        "      <td>plasmablast</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000236</td>\n",
-       "      <td>B cell</td>\n",
+       "      <td>CL:0000145</td>\n",
+       "      <td>professional antigen presenting cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>CL:0000980</td>\n",
        "      <td>plasmablast</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000145</td>\n",
-       "      <td>professional antigen presenting cell</td>\n",
+       "      <td>CL:0000236</td>\n",
+       "      <td>B cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
@@ -901,16 +901,16 @@
        "      <td>CL:0002489</td>\n",
        "      <td>double negative thymocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002420</td>\n",
-       "      <td>immature T cell</td>\n",
+       "      <td>CL:0000084</td>\n",
+       "      <td>T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>CL:0002489</td>\n",
        "      <td>double negative thymocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000084</td>\n",
-       "      <td>T cell</td>\n",
+       "      <td>CL:0002420</td>\n",
+       "      <td>immature T cell</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -967,39 +967,39 @@
        "4   rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
        "5   rdfs:subClassOf  CL:0000145  professional antigen presenting cell  \n",
        "6   rdfs:subClassOf  CL:0000236                                B cell  \n",
-       "7   rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "8   rdfs:subClassOf  CL:0000084                                T cell  \n",
-       "9   rdfs:subClassOf  CL:0000084                                T cell  \n",
-       "10  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
-       "11  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "12  rdfs:subClassOf  CL:0002420                       immature T cell  \n",
+       "7   rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "8   rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
+       "9   rdfs:subClassOf  CL:0002420                       immature T cell  \n",
+       "10  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
+       "11  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
+       "12  rdfs:subClassOf  CL:0000084                                T cell  \n",
        "13  rdfs:subClassOf  CL:0000084                                T cell  \n",
        "14  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
        "15  rdfs:subClassOf  CL:0000084                                T cell  \n",
        "16  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "17  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "18  rdfs:subClassOf  CL:0000084                                T cell  \n",
-       "19  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
-       "20  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "21  rdfs:subClassOf  CL:0000813                         memory T cell  \n",
-       "22  rdfs:subClassOf  CL:0000084                                T cell  \n",
-       "23  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
-       "24  rdfs:subClassOf  CL:0000084                                T cell  \n",
-       "25  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
-       "26  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
+       "17  rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "18  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
+       "19  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
+       "20  rdfs:subClassOf  CL:0000813                         memory T cell  \n",
+       "21  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
+       "22  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
+       "23  rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "24  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
+       "25  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
+       "26  rdfs:subClassOf  CL:0000084                                T cell  \n",
        "27  rdfs:subClassOf  CL:0000813                         memory T cell  \n",
-       "28  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "29  rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "28  rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "29  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
        "30  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
-       "31  rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "31  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
        "32  rdfs:subClassOf  CL:0000789                     alpha-beta T cell  \n",
-       "33  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "34  rdfs:subClassOf  CL:0000236                                B cell  \n",
-       "35  rdfs:subClassOf  CL:0000145  professional antigen presenting cell  \n",
+       "33  rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "34  rdfs:subClassOf  CL:0000145  professional antigen presenting cell  \n",
+       "35  rdfs:subClassOf  CL:0000236                                B cell  \n",
        "36  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
        "37  rdfs:subClassOf  CL:0000842                      mononuclear cell  \n",
-       "38  rdfs:subClassOf  CL:0002420                       immature T cell  \n",
-       "39  rdfs:subClassOf  CL:0000084                                T cell  "
+       "38  rdfs:subClassOf  CL:0000084                                T cell  \n",
+       "39  rdfs:subClassOf  CL:0002420                       immature T cell  "
       ]
      },
      "execution_count": 6,
@@ -1103,8 +1103,8 @@
        "      <td>CL:0000787</td>\n",
        "      <td>memory B cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000236</td>\n",
-       "      <td>B cell</td>\n",
+       "      <td>CL:0000785</td>\n",
+       "      <td>mature B cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1119,8 +1119,8 @@
        "      <td>CL:0000787</td>\n",
        "      <td>memory B cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0000542</td>\n",
-       "      <td>lymphocyte</td>\n",
+       "      <td>CL:0001201</td>\n",
+       "      <td>B cell, CD19-positive</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1130,16 +1130,16 @@
        "            s        s_label                p           o  \\\n",
        "0  CL:0000084         T cell  rdfs:subClassOf  CL:0000842   \n",
        "1  CL:0000084         T cell  rdfs:subClassOf  CL:0000542   \n",
-       "2  CL:0000787  memory B cell  rdfs:subClassOf  CL:0000236   \n",
+       "2  CL:0000787  memory B cell  rdfs:subClassOf  CL:0000785   \n",
        "3  CL:0000787  memory B cell  rdfs:subClassOf  CL:0001201   \n",
-       "4  CL:0000787  memory B cell  rdfs:subClassOf  CL:0000542   \n",
+       "4  CL:0000787  memory B cell  rdfs:subClassOf  CL:0001201   \n",
        "\n",
        "                 o_label  \n",
        "0       mononuclear cell  \n",
        "1             lymphocyte  \n",
-       "2                 B cell  \n",
+       "2          mature B cell  \n",
        "3  B cell, CD19-positive  \n",
-       "4             lymphocyte  "
+       "4  B cell, CD19-positive  "
       ]
      },
      "execution_count": 8,
@@ -1218,160 +1218,160 @@
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000612</td>\n",
-       "      <td>kidney corpuscule cell</td>\n",
+       "      <td>CL:0002584</td>\n",
+       "      <td>renal cortical epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002681</td>\n",
-       "      <td>kidney cortical cell</td>\n",
+       "      <td>CL:1000510</td>\n",
+       "      <td>kidney glomerular epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002584</td>\n",
-       "      <td>renal cortical epithelial cell</td>\n",
+       "      <td>CL:1000450</td>\n",
+       "      <td>epithelial cell of glomerular capsule</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002681</td>\n",
-       "      <td>kidney cortical cell</td>\n",
+       "      <td>CL:1000450</td>\n",
+       "      <td>epithelial cell of glomerular capsule</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000746</td>\n",
-       "      <td>glomerular cell</td>\n",
+       "      <td>CL:1000449</td>\n",
+       "      <td>epithelial cell of nephron</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000612</td>\n",
-       "      <td>kidney corpuscule cell</td>\n",
+       "      <td>CL:0002681</td>\n",
+       "      <td>kidney cortical cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002518</td>\n",
-       "      <td>kidney epithelial cell</td>\n",
+       "      <td>CL:0002584</td>\n",
+       "      <td>renal cortical epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000510</td>\n",
-       "      <td>kidney glomerular epithelial cell</td>\n",
+       "      <td>CL:1000746</td>\n",
+       "      <td>glomerular cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000497</td>\n",
-       "      <td>kidney cell</td>\n",
+       "      <td>CL:1000612</td>\n",
+       "      <td>kidney corpuscule cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000746</td>\n",
-       "      <td>glomerular cell</td>\n",
+       "      <td>CL:1000497</td>\n",
+       "      <td>kidney cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000450</td>\n",
-       "      <td>epithelial cell of glomerular capsule</td>\n",
+       "      <td>CL:0002681</td>\n",
+       "      <td>kidney cortical cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002584</td>\n",
-       "      <td>renal cortical epithelial cell</td>\n",
+       "      <td>CL:0002518</td>\n",
+       "      <td>kidney epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000449</td>\n",
-       "      <td>epithelial cell of nephron</td>\n",
+       "      <td>CL:1000746</td>\n",
+       "      <td>glomerular cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000510</td>\n",
-       "      <td>kidney glomerular epithelial cell</td>\n",
+       "      <td>CL:1000612</td>\n",
+       "      <td>kidney corpuscule cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000450</td>\n",
-       "      <td>epithelial cell of glomerular capsule</td>\n",
+       "      <td>CL:1000510</td>\n",
+       "      <td>kidney glomerular epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>CL:0002306</td>\n",
        "      <td>epithelial cell of proximal tubule</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002681</td>\n",
-       "      <td>kidney cortical cell</td>\n",
+       "      <td>CL:0002584</td>\n",
+       "      <td>renal cortical epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>CL:0002306</td>\n",
        "      <td>epithelial cell of proximal tubule</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000497</td>\n",
-       "      <td>kidney cell</td>\n",
+       "      <td>CL:1000615</td>\n",
+       "      <td>kidney cortex tubule cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>CL:0002306</td>\n",
        "      <td>epithelial cell of proximal tubule</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002518</td>\n",
-       "      <td>kidney epithelial cell</td>\n",
+       "      <td>CL:0002681</td>\n",
+       "      <td>kidney cortical cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>CL:0002306</td>\n",
        "      <td>epithelial cell of proximal tubule</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002584</td>\n",
-       "      <td>renal cortical epithelial cell</td>\n",
+       "      <td>CL:0002681</td>\n",
+       "      <td>kidney cortical cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>CL:0002306</td>\n",
        "      <td>epithelial cell of proximal tubule</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002681</td>\n",
-       "      <td>kidney cortical cell</td>\n",
+       "      <td>CL:0002584</td>\n",
+       "      <td>renal cortical epithelial cell</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1401,26 +1401,26 @@
        "19  CL:0002306  epithelial cell of proximal tubule  rdfs:subClassOf   \n",
        "\n",
        "             o                                o_label  \n",
-       "0   CL:1000612                 kidney corpuscule cell  \n",
-       "1   CL:0002681                   kidney cortical cell  \n",
-       "2   CL:0002584         renal cortical epithelial cell  \n",
-       "3   CL:0002681                   kidney cortical cell  \n",
-       "4   CL:1000746                        glomerular cell  \n",
-       "5   CL:1000612                 kidney corpuscule cell  \n",
-       "6   CL:0002518                 kidney epithelial cell  \n",
-       "7   CL:1000510      kidney glomerular epithelial cell  \n",
-       "8   CL:1000497                            kidney cell  \n",
-       "9   CL:1000746                        glomerular cell  \n",
-       "10  CL:1000450  epithelial cell of glomerular capsule  \n",
-       "11  CL:0002584         renal cortical epithelial cell  \n",
-       "12  CL:1000449             epithelial cell of nephron  \n",
-       "13  CL:1000510      kidney glomerular epithelial cell  \n",
-       "14  CL:1000450  epithelial cell of glomerular capsule  \n",
-       "15  CL:0002681                   kidney cortical cell  \n",
-       "16  CL:1000497                            kidney cell  \n",
-       "17  CL:0002518                 kidney epithelial cell  \n",
-       "18  CL:0002584         renal cortical epithelial cell  \n",
-       "19  CL:0002681                   kidney cortical cell  "
+       "0   CL:0002584         renal cortical epithelial cell  \n",
+       "1   CL:1000510      kidney glomerular epithelial cell  \n",
+       "2   CL:1000450  epithelial cell of glomerular capsule  \n",
+       "3   CL:1000450  epithelial cell of glomerular capsule  \n",
+       "4   CL:1000449             epithelial cell of nephron  \n",
+       "5   CL:0002681                   kidney cortical cell  \n",
+       "6   CL:0002584         renal cortical epithelial cell  \n",
+       "7   CL:1000746                        glomerular cell  \n",
+       "8   CL:1000612                 kidney corpuscule cell  \n",
+       "9   CL:1000497                            kidney cell  \n",
+       "10  CL:0002681                   kidney cortical cell  \n",
+       "11  CL:0002518                 kidney epithelial cell  \n",
+       "12  CL:1000746                        glomerular cell  \n",
+       "13  CL:1000612                 kidney corpuscule cell  \n",
+       "14  CL:1000510      kidney glomerular epithelial cell  \n",
+       "15  CL:0002584         renal cortical epithelial cell  \n",
+       "16  CL:1000615              kidney cortex tubule cell  \n",
+       "17  CL:0002681                   kidney cortical cell  \n",
+       "18  CL:0002681                   kidney cortical cell  \n",
+       "19  CL:0002584         renal cortical epithelial cell  "
       ]
      },
      "execution_count": 10,
@@ -1470,7 +1470,7 @@
    "outputs": [],
    "source": [
     "# temporarily using a placeholder schema for free text cell types \n",
-    "ada = AnndataAnalyzer(\"./immune_example.h5ad\", \"pandasaurus_cxg/schema/schema.json\")"
+    "ada = AnndataAnalyzer.from_file_path(\"./immune_example.h5ad\", author_cell_type_list = ['subclass.full', 'subclass.l3', 'subclass.l2', 'subclass.l1', 'class', 'author_cell_type'])"
    ]
   },
   {
@@ -1524,6 +1524,118 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>naive B cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>naive B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>memory B cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>memory B cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>gamma-delta T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>plasmablast</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>plasmablast</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>regulatory T cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>regulatory T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>naive CD8+ T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>naive CD4+ T cell</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>mucosal invariant T cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>mucosal invariant T cell (MAIT)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>memory T cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>TissueResMemT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>double-positive, alpha-beta thymocyte</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>double-positive T cell (DPT)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>double negative thymocyte</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>double negative T cell (DNT)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>T cell</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>author_cell_type</td>\n",
+       "      <td>TCRVbeta13.1pos</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>naive B cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1531,7 +1643,7 @@
        "      <td>naive B cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>15</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>memory B cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1539,7 +1651,7 @@
        "      <td>memory B cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>16</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>gamma-delta T cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1547,7 +1659,7 @@
        "      <td>gamma-delta T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>17</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>plasmablast</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1555,7 +1667,7 @@
        "      <td>plasmablast</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>18</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>regulatory T cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1563,7 +1675,7 @@
        "      <td>regulatory T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>19</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1571,7 +1683,7 @@
        "      <td>CD4-positive, alpha-beta memory T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>20</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1579,7 +1691,7 @@
        "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>21</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>naive CD8+ T cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1587,7 +1699,7 @@
        "      <td>naive thymus-derived CD8-positive, alpha-beta ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>22</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>naive CD4+ T cell</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1595,7 +1707,7 @@
        "      <td>naive thymus-derived CD4-positive, alpha-beta ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>23</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>mucosal invariant T cell (MAIT)</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1603,7 +1715,7 @@
        "      <td>mucosal invariant T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>24</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>TissueResMemT</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1611,7 +1723,7 @@
        "      <td>memory T cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>25</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>double-positive T cell (DPT)</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1619,7 +1731,7 @@
        "      <td>double-positive, alpha-beta thymocyte</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>26</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>double negative T cell (DNT)</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1627,7 +1739,7 @@
        "      <td>double negative thymocyte</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>27</th>\n",
        "      <td>author_cell_type</td>\n",
        "      <td>TCRVbeta13.1pos</td>\n",
        "      <td>cluster_matches</td>\n",
@@ -1639,37 +1751,95 @@
        "</div>"
       ],
       "text/plain": [
-       "         field_name1                                  value1        predicate  \\\n",
-       "0   author_cell_type                            naive B cell  cluster_matches   \n",
-       "1   author_cell_type                           memory B cell  cluster_matches   \n",
-       "2   author_cell_type                      gamma-delta T cell  cluster_matches   \n",
-       "3   author_cell_type                             plasmablast  cluster_matches   \n",
-       "4   author_cell_type                       regulatory T cell  cluster_matches   \n",
-       "5   author_cell_type  CD4-positive, alpha-beta memory T cell  cluster_matches   \n",
-       "6   author_cell_type  CD8-positive, alpha-beta memory T cell  cluster_matches   \n",
-       "7   author_cell_type                       naive CD8+ T cell  cluster_matches   \n",
-       "8   author_cell_type                       naive CD4+ T cell  cluster_matches   \n",
-       "9   author_cell_type         mucosal invariant T cell (MAIT)  cluster_matches   \n",
-       "10  author_cell_type                           TissueResMemT  cluster_matches   \n",
-       "11  author_cell_type            double-positive T cell (DPT)  cluster_matches   \n",
-       "12  author_cell_type            double negative T cell (DNT)  cluster_matches   \n",
-       "13  author_cell_type                         TCRVbeta13.1pos  cluster_matches   \n",
+       "         field_name1                                             value1  \\\n",
+       "0          cell_type                                       naive B cell   \n",
+       "1          cell_type                                      memory B cell   \n",
+       "2          cell_type                                 gamma-delta T cell   \n",
+       "3          cell_type                                        plasmablast   \n",
+       "4          cell_type                                  regulatory T cell   \n",
+       "5          cell_type             CD4-positive, alpha-beta memory T cell   \n",
+       "6          cell_type             CD8-positive, alpha-beta memory T cell   \n",
+       "7          cell_type  naive thymus-derived CD8-positive, alpha-beta ...   \n",
+       "8          cell_type  naive thymus-derived CD4-positive, alpha-beta ...   \n",
+       "9          cell_type                           mucosal invariant T cell   \n",
+       "10         cell_type                                      memory T cell   \n",
+       "11         cell_type              double-positive, alpha-beta thymocyte   \n",
+       "12         cell_type                          double negative thymocyte   \n",
+       "13         cell_type                                             T cell   \n",
+       "14  author_cell_type                                       naive B cell   \n",
+       "15  author_cell_type                                      memory B cell   \n",
+       "16  author_cell_type                                 gamma-delta T cell   \n",
+       "17  author_cell_type                                        plasmablast   \n",
+       "18  author_cell_type                                  regulatory T cell   \n",
+       "19  author_cell_type             CD4-positive, alpha-beta memory T cell   \n",
+       "20  author_cell_type             CD8-positive, alpha-beta memory T cell   \n",
+       "21  author_cell_type                                  naive CD8+ T cell   \n",
+       "22  author_cell_type                                  naive CD4+ T cell   \n",
+       "23  author_cell_type                    mucosal invariant T cell (MAIT)   \n",
+       "24  author_cell_type                                      TissueResMemT   \n",
+       "25  author_cell_type                       double-positive T cell (DPT)   \n",
+       "26  author_cell_type                       double negative T cell (DNT)   \n",
+       "27  author_cell_type                                    TCRVbeta13.1pos   \n",
        "\n",
-       "   field_name2                                             value2  \n",
-       "0    cell_type                                       naive B cell  \n",
-       "1    cell_type                                      memory B cell  \n",
-       "2    cell_type                                 gamma-delta T cell  \n",
-       "3    cell_type                                        plasmablast  \n",
-       "4    cell_type                                  regulatory T cell  \n",
-       "5    cell_type             CD4-positive, alpha-beta memory T cell  \n",
-       "6    cell_type             CD8-positive, alpha-beta memory T cell  \n",
-       "7    cell_type  naive thymus-derived CD8-positive, alpha-beta ...  \n",
-       "8    cell_type  naive thymus-derived CD4-positive, alpha-beta ...  \n",
-       "9    cell_type                           mucosal invariant T cell  \n",
-       "10   cell_type                                      memory T cell  \n",
-       "11   cell_type              double-positive, alpha-beta thymocyte  \n",
-       "12   cell_type                          double negative thymocyte  \n",
-       "13   cell_type                                             T cell  "
+       "          predicate       field_name2  \\\n",
+       "0   cluster_matches  author_cell_type   \n",
+       "1   cluster_matches  author_cell_type   \n",
+       "2   cluster_matches  author_cell_type   \n",
+       "3   cluster_matches  author_cell_type   \n",
+       "4   cluster_matches  author_cell_type   \n",
+       "5   cluster_matches  author_cell_type   \n",
+       "6   cluster_matches  author_cell_type   \n",
+       "7   cluster_matches  author_cell_type   \n",
+       "8   cluster_matches  author_cell_type   \n",
+       "9   cluster_matches  author_cell_type   \n",
+       "10  cluster_matches  author_cell_type   \n",
+       "11  cluster_matches  author_cell_type   \n",
+       "12  cluster_matches  author_cell_type   \n",
+       "13  cluster_matches  author_cell_type   \n",
+       "14  cluster_matches         cell_type   \n",
+       "15  cluster_matches         cell_type   \n",
+       "16  cluster_matches         cell_type   \n",
+       "17  cluster_matches         cell_type   \n",
+       "18  cluster_matches         cell_type   \n",
+       "19  cluster_matches         cell_type   \n",
+       "20  cluster_matches         cell_type   \n",
+       "21  cluster_matches         cell_type   \n",
+       "22  cluster_matches         cell_type   \n",
+       "23  cluster_matches         cell_type   \n",
+       "24  cluster_matches         cell_type   \n",
+       "25  cluster_matches         cell_type   \n",
+       "26  cluster_matches         cell_type   \n",
+       "27  cluster_matches         cell_type   \n",
+       "\n",
+       "                                               value2  \n",
+       "0                                        naive B cell  \n",
+       "1                                       memory B cell  \n",
+       "2                                  gamma-delta T cell  \n",
+       "3                                         plasmablast  \n",
+       "4                                   regulatory T cell  \n",
+       "5              CD4-positive, alpha-beta memory T cell  \n",
+       "6              CD8-positive, alpha-beta memory T cell  \n",
+       "7                                   naive CD8+ T cell  \n",
+       "8                                   naive CD4+ T cell  \n",
+       "9                     mucosal invariant T cell (MAIT)  \n",
+       "10                                      TissueResMemT  \n",
+       "11                       double-positive T cell (DPT)  \n",
+       "12                       double negative T cell (DNT)  \n",
+       "13                                    TCRVbeta13.1pos  \n",
+       "14                                       naive B cell  \n",
+       "15                                      memory B cell  \n",
+       "16                                 gamma-delta T cell  \n",
+       "17                                        plasmablast  \n",
+       "18                                  regulatory T cell  \n",
+       "19             CD4-positive, alpha-beta memory T cell  \n",
+       "20             CD8-positive, alpha-beta memory T cell  \n",
+       "21  naive thymus-derived CD8-positive, alpha-beta ...  \n",
+       "22  naive thymus-derived CD4-positive, alpha-beta ...  \n",
+       "23                           mucosal invariant T cell  \n",
+       "24                                      memory T cell  \n",
+       "25              double-positive, alpha-beta thymocyte  \n",
+       "26                          double negative thymocyte  \n",
+       "27                                             T cell  "
       ]
      },
      "execution_count": 13,
@@ -1778,91 +1948,78 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>415</th>\n",
+       "      <th>1198</th>\n",
+       "      <td>subclass.l1</td>\n",
+       "      <td>PEC</td>\n",
+       "      <td>cluster_matches</td>\n",
        "      <td>cell_type</td>\n",
-       "      <td>kidney distal convoluted tubule epithelial cell</td>\n",
-       "      <td>subcluster_of</td>\n",
-       "      <td>class</td>\n",
-       "      <td>epithelial cells</td>\n",
+       "      <td>parietal epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>416</th>\n",
+       "      <th>1199</th>\n",
+       "      <td>subclass.l1</td>\n",
+       "      <td>PapE</td>\n",
+       "      <td>cluster_matches</td>\n",
        "      <td>cell_type</td>\n",
-       "      <td>renal interstitial pericyte</td>\n",
-       "      <td>subcluster_of</td>\n",
-       "      <td>class</td>\n",
-       "      <td>stroma cells</td>\n",
+       "      <td>papillary tips cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>417</th>\n",
-       "      <td>cell_type</td>\n",
-       "      <td>neural cell</td>\n",
-       "      <td>cluster_matches</td>\n",
+       "      <th>1200</th>\n",
        "      <td>class</td>\n",
-       "      <td>neural cells</td>\n",
+       "      <td>immune cells</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>leukocyte</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>418</th>\n",
-       "      <td>cell_type</td>\n",
-       "      <td>parietal epithelial cell</td>\n",
-       "      <td>subcluster_of</td>\n",
+       "      <th>1201</th>\n",
        "      <td>class</td>\n",
-       "      <td>epithelial cells</td>\n",
+       "      <td>endothelial cells</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>endothelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>419</th>\n",
-       "      <td>cell_type</td>\n",
-       "      <td>papillary tips cell</td>\n",
-       "      <td>subcluster_of</td>\n",
+       "      <th>1202</th>\n",
        "      <td>class</td>\n",
-       "      <td>epithelial cells</td>\n",
+       "      <td>neural cells</td>\n",
+       "      <td>cluster_matches</td>\n",
+       "      <td>cell_type</td>\n",
+       "      <td>neural cell</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>420 rows × 5 columns</p>\n",
+       "<p>1203 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     field_name1                                           value1  \\\n",
-       "0    subclass.l3                                              dPT   \n",
-       "1    subclass.l3                                              aPT   \n",
-       "2    subclass.l3                                            M-FIB   \n",
-       "3    subclass.l3                                               MD   \n",
-       "4    subclass.l3                                            NKC/T   \n",
-       "..           ...                                              ...   \n",
-       "415    cell_type  kidney distal convoluted tubule epithelial cell   \n",
-       "416    cell_type                      renal interstitial pericyte   \n",
-       "417    cell_type                                      neural cell   \n",
-       "418    cell_type                         parietal epithelial cell   \n",
-       "419    cell_type                              papillary tips cell   \n",
-       "\n",
-       "           predicate    field_name2  \\\n",
-       "0    cluster_matches  subclass.full   \n",
-       "1    cluster_matches  subclass.full   \n",
-       "2    cluster_matches  subclass.full   \n",
-       "3    cluster_matches  subclass.full   \n",
-       "4    cluster_matches  subclass.full   \n",
-       "..               ...            ...   \n",
-       "415    subcluster_of          class   \n",
-       "416    subcluster_of          class   \n",
-       "417  cluster_matches          class   \n",
-       "418    subcluster_of          class   \n",
-       "419    subcluster_of          class   \n",
+       "      field_name1             value1        predicate    field_name2  \\\n",
+       "0     subclass.l3                dPT  cluster_matches  subclass.full   \n",
+       "1     subclass.l3                aPT  cluster_matches  subclass.full   \n",
+       "2     subclass.l3              M-FIB  cluster_matches  subclass.full   \n",
+       "3     subclass.l3                 MD  cluster_matches  subclass.full   \n",
+       "4     subclass.l3              NKC/T  cluster_matches  subclass.full   \n",
+       "...           ...                ...              ...            ...   \n",
+       "1198  subclass.l1                PEC  cluster_matches      cell_type   \n",
+       "1199  subclass.l1               PapE  cluster_matches      cell_type   \n",
+       "1200        class       immune cells  cluster_matches      cell_type   \n",
+       "1201        class  endothelial cells  cluster_matches      cell_type   \n",
+       "1202        class       neural cells  cluster_matches      cell_type   \n",
        "\n",
-       "                                                value2  \n",
-       "0         Degenerative Proximal Tubule Epithelial Cell  \n",
-       "1    Adaptive / Maladaptive / Repairing Proximal Tu...  \n",
-       "2                                 Medullary Fibroblast  \n",
-       "3                                    Macula Densa Cell  \n",
-       "4          Natural Killer Cell / Natural Killer T Cell  \n",
-       "..                                                 ...  \n",
-       "415                                   epithelial cells  \n",
-       "416                                       stroma cells  \n",
-       "417                                       neural cells  \n",
-       "418                                   epithelial cells  \n",
-       "419                                   epithelial cells  \n",
+       "                                                 value2  \n",
+       "0          Degenerative Proximal Tubule Epithelial Cell  \n",
+       "1     Adaptive / Maladaptive / Repairing Proximal Tu...  \n",
+       "2                                  Medullary Fibroblast  \n",
+       "3                                     Macula Densa Cell  \n",
+       "4           Natural Killer Cell / Natural Killer T Cell  \n",
+       "...                                                 ...  \n",
+       "1198                           parietal epithelial cell  \n",
+       "1199                                papillary tips cell  \n",
+       "1200                                          leukocyte  \n",
+       "1201                                   endothelial cell  \n",
+       "1202                                        neural cell  \n",
        "\n",
-       "[420 rows x 5 columns]"
+       "[1203 rows x 5 columns]"
       ]
      },
      "execution_count": 14,
@@ -1871,7 +2028,7 @@
     }
    ],
    "source": [
-    "ada = AnndataAnalyzer(\"./blue_lake_kidney.h5ad\", \"pandasaurus_cxg/schema/schema.json\")\n",
+    "ada = AnndataAnalyzer.from_file_path(\"./blue_lake_kidney.h5ad\", author_cell_type_list = ['subclass.full', 'subclass.l3', 'subclass.l2', 'subclass.l1', 'class', 'author_cell_type'])\n",
     "ada.co_annotation_report()"
    ]
   },
@@ -1919,7 +2076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1929,7 +2086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1949,16 +2106,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-08-16 09:28:12,570 - INFO - Transitive reduction has been applied on rdfs:subClassOf.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1993,40 +2143,40 @@
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002584</td>\n",
-       "      <td>renal cortical epithelial cell</td>\n",
+       "      <td>CL:1000449</td>\n",
+       "      <td>epithelial cell of nephron</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002681</td>\n",
-       "      <td>kidney cortical cell</td>\n",
+       "      <td>CL:1000510</td>\n",
+       "      <td>kidney glomerular epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000746</td>\n",
-       "      <td>glomerular cell</td>\n",
+       "      <td>CL:1000612</td>\n",
+       "      <td>kidney corpuscule cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000612</td>\n",
-       "      <td>kidney corpuscule cell</td>\n",
+       "      <td>CL:1000746</td>\n",
+       "      <td>glomerular cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>CL:0000653</td>\n",
        "      <td>podocyte</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0002518</td>\n",
-       "      <td>kidney epithelial cell</td>\n",
+       "      <td>CL:0002584</td>\n",
+       "      <td>renal cortical epithelial cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -2041,40 +2191,40 @@
        "      <td>CL:1001432</td>\n",
        "      <td>kidney collecting duct intercalated cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1001225</td>\n",
-       "      <td>kidney collecting duct cell</td>\n",
+       "      <td>CL:1000449</td>\n",
+       "      <td>epithelial cell of nephron</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>133</th>\n",
        "      <td>CL:1001432</td>\n",
        "      <td>kidney collecting duct intercalated cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000449</td>\n",
-       "      <td>epithelial cell of nephron</td>\n",
+       "      <td>CL:0005010</td>\n",
+       "      <td>renal intercalated cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>134</th>\n",
        "      <td>CL:1001432</td>\n",
        "      <td>kidney collecting duct intercalated cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:0005010</td>\n",
-       "      <td>renal intercalated cell</td>\n",
+       "      <td>CL:1000497</td>\n",
+       "      <td>kidney cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135</th>\n",
        "      <td>CL:1001432</td>\n",
        "      <td>kidney collecting duct intercalated cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000497</td>\n",
-       "      <td>kidney cell</td>\n",
+       "      <td>CL:1001225</td>\n",
+       "      <td>kidney collecting duct cell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>136</th>\n",
        "      <td>CL:1001432</td>\n",
        "      <td>kidney collecting duct intercalated cell</td>\n",
        "      <td>rdfs:subClassOf</td>\n",
-       "      <td>CL:1000454</td>\n",
-       "      <td>kidney collecting duct epithelial cell</td>\n",
+       "      <td>CL:0002518</td>\n",
+       "      <td>kidney epithelial cell</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2095,23 +2245,23 @@
        "135  CL:1001432  kidney collecting duct intercalated cell  rdfs:subClassOf   \n",
        "136  CL:1001432  kidney collecting duct intercalated cell  rdfs:subClassOf   \n",
        "\n",
-       "              o                                 o_label  \n",
-       "0    CL:0002584          renal cortical epithelial cell  \n",
-       "1    CL:0002681                    kidney cortical cell  \n",
-       "2    CL:1000746                         glomerular cell  \n",
-       "3    CL:1000612                  kidney corpuscule cell  \n",
-       "4    CL:0002518                  kidney epithelial cell  \n",
-       "..          ...                                     ...  \n",
-       "132  CL:1001225             kidney collecting duct cell  \n",
-       "133  CL:1000449              epithelial cell of nephron  \n",
-       "134  CL:0005010                 renal intercalated cell  \n",
-       "135  CL:1000497                             kidney cell  \n",
-       "136  CL:1000454  kidney collecting duct epithelial cell  \n",
+       "              o                            o_label  \n",
+       "0    CL:1000449         epithelial cell of nephron  \n",
+       "1    CL:1000510  kidney glomerular epithelial cell  \n",
+       "2    CL:1000612             kidney corpuscule cell  \n",
+       "3    CL:1000746                    glomerular cell  \n",
+       "4    CL:0002584     renal cortical epithelial cell  \n",
+       "..          ...                                ...  \n",
+       "132  CL:1000449         epithelial cell of nephron  \n",
+       "133  CL:0005010            renal intercalated cell  \n",
+       "134  CL:1000497                        kidney cell  \n",
+       "135  CL:1001225        kidney collecting duct cell  \n",
+       "136  CL:0002518             kidney epithelial cell  \n",
        "\n",
        "[137 rows x 5 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2130,7 +2280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2285,7 +2435,7 @@
        "[1203 rows x 5 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2312,7 +2462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2331,7 +2481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2347,7 +2497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2356,12 +2506,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -2379,13 +2529,6 @@
     "    file_path=\"kidney_new.ttl\",\n",
     ")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {