v0.5.7

docs/changelog.rst

@@ -1,22 +1,38 @@
 Change Log
 ==========
 
+Inferelator v0.5.7 `September 29, 2021`
+---------------------------------------
+
+New Functionality:
+
+- Added support for numba acceleration of AMuSR with ``.set_run_parameters(use_numba=True)`` (PR #46)
+
+Code Refactoring:
+
+- Updated example scripts
+
+Bug Fixes:
+
+- Gene labels are included as the first column of the produced confidences TSV file by default
+- Matplotlib backend selection checks for non-interactive mode
+
 Inferelator v0.5.6 `August 16, 2021`
------------------------------------
+------------------------------------
 
 New Functionality:
 
 - Added code to randomly generate noise in prior with ``.set_shuffle_parameters(add_prior_noise=None)``
 - Added in-workflow benchmarks for CellOracle and pySCENIC
-- 
+  
 
 Code Refactoring:
 
 - Minor changes to matplotlib interface
 - Improved testing for multitask workflows
 - Improved error messaging around prior and gold standard
 - Switch from Travis.ci to GitHub Actions for continuous integration
-- 
+  
 
 Inferelator v0.5.5 `April 29, 2021`
 -----------------------------------

docs/conf.py

@@ -23,7 +23,7 @@
 author = 'Chris Jackson'
 
 # The full version, including alpha/beta/rc tags
-release = 'v0.5.6'
+release = 'v0.5.7'
 
 
 # -- General configuration ---------------------------------------------------

examples/Bsubtilis_Network_Inference.ipynb

@@ -144,7 +144,8 @@
     "worker.append_to_path('output_dir', 'final')\n",
     "worker.set_crossvalidation_parameters(split_gold_standard_for_crossvalidation=False, cv_split_ratio=None)\n",
     "worker.set_run_parameters(num_bootstraps=50, random_seed=100)\n",
-    "final_network = worker.run()"
+    "\n",
+    "final_network_results = worker.run()"
    ]
   },
   {
@@ -154,8 +155,35 @@
    "outputs": [],
    "source": [
     "# Visualize network results\n",
+    "# The workflow returns an InferelatorResults object\n",
+    "\n",
+    "# There is a dataframe with an edge table for the final network\n",
+    "final_network_results.network.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There is a list of dataframes with model coeffcients\n",
+    "# Each list element is a dataframe with the results from one bootstrap\n",
+    "# The dataframes are genes x TFs\n",
+    "\n",
+    "final_network_results.betas[0].iloc[0:5, 0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The confidence scores for each network edge are also accessible\n",
+    "# This dataframe is genes x TFs\n",
     "\n",
-    "final_network.head()"
+    "final_network_results.combined_confidences.iloc[0:5, 0:5]"
    ]
   }
  ],
@@ -175,7 +203,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,

examples/Yeast_Network_Inference.ipynb

@@ -209,7 +209,8 @@
     "worker.append_to_path('output_dir', 'final')\n",
     "worker.set_crossvalidation_parameters(split_gold_standard_for_crossvalidation=False, cv_split_ratio=None)\n",
     "worker.set_run_parameters(num_bootstraps=50, random_seed=100)\n",
-    "final_network = worker.run()"
+    "\n",
+    "final_network_results = worker.run()"
    ]
   },
   {
@@ -219,8 +220,35 @@
    "outputs": [],
    "source": [
     "# Visualize network results\n",
+    "# The workflow returns an InferelatorResults object\n",
+    "\n",
+    "# There is a dataframe with an edge table for the final network\n",
+    "final_network_results.network.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There is a list of dataframes with model coeffcients\n",
+    "# Each list element is a dataframe with the results from one bootstrap\n",
+    "# The dataframes are genes x TFs\n",
+    "\n",
+    "final_network_results.betas[0].iloc[0:5, 0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The confidence scores for each network edge are also accessible\n",
+    "# This dataframe is genes x TFs\n",
     "\n",
-    "final_network.head()"
+    "final_network_results.combined_confidences.iloc[0:5, 0:5]"
    ]
   }
  ],
@@ -240,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.10"
   }
  },
  "nbformat": 4,

requirements.txt

@@ -3,4 +3,4 @@ matplotlib >=1.5.1
 scipy >=0.9
 scikit-learn
 pandas >=0.24.0
-anndata
+anndata >=0.7.4

setup.py

@@ -2,7 +2,7 @@
 from setuptools import setup, find_packages
 
 # Current Inferelator Version Number
-version = "0.5.6"
+version = "0.5.7"
 
 
 # Description from README.md