Merge pull request #9 from ArpiarSaundersLab/dev

Filtered slashes in in gene names. Resolves #8

Author: kennypavan

README.md

@@ -127,9 +127,7 @@ asql.plot_umap(color_by="leiden_clusters", annotate=True)
 
 
 ## Reference
-AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources<br />
-Kenny Pavan, Arpiar Saunders<br />
-bioRxiv 2024.11.02.621676; [doi: https://doi.org/10.1101/2024.11.02.621676](https://www.biorxiv.org/content/10.1101/2024.11.02.621676)
-
+Kenny Pavan, Arpiar Saunders, AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources<br>
+Bioinformatics Advances, 2025; vbaf105, [https://doi.org/10.1093/bioadv/vbaf105](https://doi.org/10.1093/bioadv/vbaf105)
 <br>
 <br>

docs/cite.md

@@ -1,5 +1,4 @@
 # Citation
 
-AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources<br />
-Kenny Pavan, Arpiar Saunders<br />
-bioRxiv 2024.11.02.621676; [doi: https://doi.org/10.1101/2024.11.02.621676](https://www.biorxiv.org/content/10.1101/2024.11.02.621676)
+Kenny Pavan, Arpiar Saunders, AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources<br>
+Bioinformatics Advances, 2025; vbaf105, [https://doi.org/10.1093/bioadv/vbaf105](https://doi.org/10.1093/bioadv/vbaf105)

docs/index.md

@@ -78,9 +78,8 @@ There are two key reasons to use **AnnSQL**: (1) if you prefer SQL's expressive
 
 
 ## Citation
-AnnSQL: A Python SQL-based package for large-scale single-cell genomics analysis on a laptop<br />
-Kenny Pavan, Arpiar Saunders<br />
-bioRxiv 2024.11.02.621676; [doi: https://doi.org/10.1101/2024.11.02.621676](https://www.biorxiv.org/content/10.1101/2024.11.02.621676)
+Kenny Pavan, Arpiar Saunders, AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources<br>
+Bioinformatics Advances, 2025; vbaf105, [https://doi.org/10.1093/bioadv/vbaf105](https://doi.org/10.1093/bioadv/vbaf105)
 
 <br>
 <br>

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
 	name='AnnSQL',
-	version='v1.0.2',
+	version='v1.0.3',
 	author="Kenny Pavan",
 	author_email="pavan@ohsu.edu",
 	description="A Python SQL tool for converting Anndata objects to a relational DuckDb database. Methods are included for querying and basic single-cell preprocessing (experimental). ",

src/AnnSQL.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
-Metadata-Version: 2.2
+Metadata-Version: 2.4
 Name: AnnSQL
-Version: 1.0.1
+Version: 1.0.3
 Summary: A Python SQL tool for converting Anndata objects to a relational DuckDb database. Methods are included for querying and basic single-cell preprocessing (experimental). 
 Home-page: https://github.com/ArpiarSaundersLab/annsql
 Author: Kenny Pavan
@@ -12,7 +12,7 @@ Requires-Python: >=3.12
 Description-Content-Type: text/markdown
 License-File: LICENSE
 Requires-Dist: scanpy>=1.10.3
-Requires-Dist: duckdb>=1.1.2
+Requires-Dist: duckdb>=1.2.2
 Requires-Dist: memory-profiler>=0.61.0
 Requires-Dist: psutil>=6.0.0
 Requires-Dist: pyarrow>=17.0.0
@@ -23,12 +23,25 @@ Dynamic: classifier
 Dynamic: description
 Dynamic: description-content-type
 Dynamic: home-page
+Dynamic: license-file
 Dynamic: requires-dist
 Dynamic: requires-python
 Dynamic: summary
 
-<center><img src="examples/images/logo.png" width=500></center>
-<br />
+<p align="center">
+	<img src="examples/images/logo.png" width=500>
+</p>
+
+<br>
+
+<p align="center">
+	<a href="https://github.com/ArpiarSaundersLab/annsql/tree/main/tests"><img src="https://img.shields.io/badge/build-passing-brightgreen"></a>
+	<a href="https://img.shields.io/github/v/release/ArpiarSaundersLab/annsql"><img src="https://img.shields.io/github/v/release/ArpiarSaundersLab/annsql"></a>
+	<a href="https://static.pepy.tech/badge/annsql/month"><img src="https://static.pepy.tech/badge/annsql/month"></a>
+	<a href="https://static.pepy.tech/badge/annsql"><img src="https://static.pepy.tech/badge/annsql"></a>
+</p>
+
+<br>
 
 # Query AnnData Objects with SQL
 The Python based AnnSQL package enables SQL-based queries on [AnnData](https://anndata.readthedocs.io/en/latest/) objects, returning results as either a [Pandas](https://pandas.pydata.org/) DataFrame, an AnnData object, or a [Parquet](https://parquet.apache.org/) file that can easily be imported into a variety of data analysis tools. Behind the scenes, AnnSQL converts the layers of an AnnData object into a relational [DuckDB](https://duckdb.org/) database. Each layer is stored as an individual table, allowing for simple or complex SQL queries, including table joins.
@@ -112,16 +125,39 @@ asql.query("SELECT SUM(COLUMNS(*)) FROM (SELECT * EXCLUDE (cell_id) FROM X)")
 
 #taking the correlation of genes ITGB2 and SSU72 in dendritic cells that express either gene > 0
 asql.query("SELECT corr(ITGB2,SSU72) as correlation FROM adata WHERE bulk_labels = 'Dendritic' AND (ITGB2 > 0 OR SSU72 >0)")
+
+############################################################################
+# Extended AnnSQL methods (See: https://docs.annsql.com/preprocessing)
+# These methods are either SQL based or Python/SQL hybrid implementations.
+############################################################################
+
+#basic QC on the dataset
+asql.calculate_total_counts()
+asql.filter_by_cell_counts(min_cell_count=1000, max_cell_count=50000)
+asql.filter_by_gene_counts(min_gene_counts=100, max_gene_counts=10000)
+
+#normalize & log umi counts
+asql.expression_normalize(total_counts_per_cell=10000)
+asql.expression_log(log_type="LN")
+
+#select highly variable genes
+asql.calculate_variable_genes(save_var_names=True, top_variable_genes=1000)
+
+#run pca
+asql.calculate_pca(n_pcs=50, top_variable_genes=1000, zero_center=False)
+
+#umap, cluster, then and plot.
+asql.calculate_umap()
+asql.calculate_leiden_clusters(resolution=0.25, n_neighbors=5)
+asql.plot_umap(color_by="leiden_clusters", annotate=True)
 ```
 
 <br>
 <br>
 
 
 ## Reference
-AnnSQL: A Python SQL-based package for large-scale single-cell genomics analysis on a laptop<br />
-Kenny Pavan, Arpiar Saunders<br />
-bioRxiv 2024.11.02.621676; [doi: https://doi.org/10.1101/2024.11.02.621676](https://www.biorxiv.org/content/10.1101/2024.11.02.621676)
-
+Kenny Pavan, Arpiar Saunders, AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources<br>
+Bioinformatics Advances, 2025; vbaf105, [https://doi.org/10.1093/bioadv/vbaf105](https://doi.org/10.1093/bioadv/vbaf105)
 <br>
 <br>

src/AnnSQL.egg-info/requires.txt

@@ -1,5 +1,5 @@
 scanpy>=1.10.3
-duckdb>=1.1.2
+duckdb>=1.2.2
 memory-profiler>=0.61.0
 psutil>=6.0.0
 pyarrow>=17.0.0

src/AnnSQL/BuildDb.py

@@ -390,7 +390,7 @@ def replace_special_chars(self, string):
 		if string[0].isdigit():
 			return 'n'+string.replace("-", "_").replace(".", "_")
 		else:
-			return string.replace("-", "_").replace(".", "_").replace("(", "_").replace(")", "_").replace(",", "_").replace(" ", "_")
+			return string.replace("-", "_").replace(".", "_").replace("(", "_").replace(")", "_").replace(",", "_").replace(" ", "_").replace("/", "_")
 
 	def determine_buffer_status(self):
 		"""