Release v1.0.0 (#367)

* Version bumps

* commit changelog

* Add some notes and icons

* icon

* Update note about number of cells

Author: jimmymathews

CHANGELOG.md

@@ -1,3 +1,12 @@
+# v1.0.0
+- Includes comprehensive tutorial and reference documentation
+- Adds dataset "curation" or preprocessing details, with a complete example
+- A number of updates to the graph processing workflows
+- Improve UMAP functionality into an interactive plot
+- Fix some timing bugs in edge cases related to the feature value computation queue
+- Greatly improves handling of the Ripley statistic summaries
+- Adds support for S3 source files in Nextflow workflows that operate on these source files
+
 # v0.24.0
 Implements a major refactoring of on-demand metrics computation in which each worker container picks up a single sample's worth of feature computation at a time. This is organized with a simple PostgresQL table considered as a task queue, and database notifications. Now all computations for a given sample begin from a database query for the same compressed binary payload representing phenotype and location data for all cells. The TCP client/server model for dispatching specific feature computations to different services is deprecated.
 

README.md

@@ -65,7 +65,7 @@ The next page shows the cell population breakdown with respect to the phenotypes
 In the grid, each *pair* of phenotypes is shown with the fraction of cells expressing *both* phenotypes. For example, the fraction of cells that are both **CD3+ CD4+ FOXP3+** and **CD3+** is 16.53%, the same as the fraction of cells that are **CD3+ CD4+ FOXP3+**, as expected since **CD3+** is part of the signature of this phenotype (the T regulatory cells).
 
 > [!NOTE]
-> You could use this technique to make a standard heat map for assessment of clusters, by selecting all single-channel phenotypes. Depending on the size of the samples, since these metrics are computed live, this could take up to 1 minute per computed value, and sometimes longer.
+> :bar_chart: You could use this technique to make a standard heat map for assessment of clusters, by selecting all single-channel phenotypes. Depending on the size of the samples, since these metrics are computed live, this could take up to 1 minute per computed value, and sometimes longer.
 
 ![alt](docs/image_assets/f4.png)
 
@@ -95,13 +95,15 @@ The "virtual slide viewer" opens. Choose a few phenotypes, and the corresponding
 
 A UMAP dimensional reduction of the cell set across the whole data collection is available in this case. Click **UMAP**.
 
-You can zoom and pan the view using scroll and click-and-drag.
+> [!NOTE]
+> :mag: You can zoom and pan the view using scroll and click-and-drag.
 
 We spot a region that looks "saturated" with Tc cells. Select it by clicking and dragging the mouse while holding either the **Ctrl** key or (on Mac) **CMD**.
 
 The new cell count for each phenotype is now shown, together with the new percentage, relative to the selection. In this case the Tc fraction approximately doubled, to **6996** cells (shown in green). This increase is assessed using the Fisher test (the entire contingency table is also shown, for reference). The test verifies that the increase is highly statistically significant in this case, as expected.
 
-By careful use of the selection tool, noting enrichments in each virtual region, you can account for most of the cell types present and hone the focus of study.
+> [!NOTE]
+> :dna: By careful use of the selection tool, noting enrichments in each virtual region, you can account for most of the cell types present and hone the focus of study.
 
 ![alt](docs/image_assets/f10.png)
 
@@ -119,7 +121,8 @@ This metric is the average number of **Naive cytotoxic T cells** appearing withi
 
 ![alt](docs/image_assets/f12.png)
 
-You can **save results like this for later** by copying the URL in the address bar. In fact, this result is highlighted on the study summary page. Try reproducing it by following the first link as shown below.
+> [!NOTE]
+> :clipboard: You can **save results like this for later** by copying the URL in the address bar. In fact, this result is highlighted on the study summary page. Try reproducing it by following the first link as shown below.
 
 ![alt](docs/image_assets/f13.png)
 

spatialprofilingtoolbox/apiserver/__init__.py

@@ -1,2 +1,2 @@
 """API service"""
-__version__ = '0.27.0'
+__version__ = '1.0.0'

spatialprofilingtoolbox/apiserver/app/main.py

@@ -2,7 +2,6 @@
 from typing import cast
 from typing import Annotated
 from typing import Literal
-import json
 from io import BytesIO
 
 from fastapi import FastAPI
@@ -46,7 +45,7 @@
 from spatialprofilingtoolbox.graphs.config_reader import read_plot_importance_fractions_config
 from spatialprofilingtoolbox.graphs.importance_fractions import PlotGenerator
 
-VERSION = '0.26.0'
+VERSION = '1.0.0'
 
 TITLE = 'Single cell studies data API'
 
@@ -58,8 +57,8 @@
 
 The public portion of the database includes phenotype and slide position information for:
 
-* ~9 million cells
-* across about 1000 specimens
+* ~75 million cells
+* across about 1500 specimens
 * typically with around 30 protein targets quantified per cell
 * from cancers from several sites: breast, lung, urothelial cancer and melanoma
 * with a range of outcome assignments depending on the study design (often immunotherapy response)

spatialprofilingtoolbox/db/__init__.py

@@ -1,2 +1,2 @@
 """Database-related SPT functionality."""
-__version__ = '0.23.0'
+__version__ = '1.0.0'

spatialprofilingtoolbox/graphs/__init__.py

@@ -1,2 +1,2 @@
 """Cell-graph graph neural network functionality."""
-__version__ = '0.2.1'
+__version__ = '1.0.0'

spatialprofilingtoolbox/ondemand/__init__.py

@@ -1,2 +1,2 @@
 """The ondemand computations service."""
-__version__ = '0.27.0'
+__version__ = '1.0.0'

spatialprofilingtoolbox/workflow/__init__.py

@@ -18,7 +18,7 @@
 
 from spatialprofilingtoolbox.workflow.common.workflow_module_exporting import WorkflowModules
 
-__version__ = '0.23.0'
+__version__ = '1.0.0'
 
 workflow_names_and_subpackages = {
     'tabular import': 'tabular_import',

version.txt

@@ -1 +1 @@
-0.31.0
+1.0.0