Add NF attribution

CHANGELOG.md

@@ -27,7 +27,7 @@ All functions have been rewritten to follow the new API, errors when running pre
   - `viper` now correctly estimates shadow regulons when network weights are values other than -1 or +1
   - `wsum` and `wmean` are deprecated, instead now the method `waggr` allows to run both methods and any custom function. This makes it easier to quickly test new enrichment methods without having to deal with `decoupler`'s implementation
 - Databases from Omnipath can now be accessed through the new `op` module
-  - Use `decoupler.op.<resource_name>` to access a database  
+  - Use `decoupler.op.<resource_name>` to access a database
   - Removed the `omnipath` package as a dependancy
   - Fixed `collectri` to the publication version instead of the OmniPath one
   - Made `progeny` only return significant genes by default instead of the top N genes per pathway
@@ -63,7 +63,7 @@ All functions have been rewritten to follow the new API, errors when running pre
   - Added `obsbar` to plot size of metadata columns in `anndata.AnnData.obs`
   - Added `order` to plot sources or features along a continous process such as a trajectory
   - Added `order_targets` to plot the targets of a given source along a continous process
-  - 
+  -
 - New preprocessing functions in the `pp` module
   - Added two functions to format networks, `adjmat` to return an adjacency matrix, and `idxmax` to return a list of sets
   - Added `filter_samples` to filter pseudobulk profiles after running `pseudobulk`

README.md

@@ -1,6 +1,6 @@
 # decoupler - Ensemble of methods to infer enrichment scores
 <img src="https://github.com/saezlab/decoupleR/blob/master/inst/figures/logo.svg?raw=1" align="right" width="120" class="no-scaled-link" />
-   
+
 
 [![Tests][badge-tests]][tests]
 [![Documentation][badge-docs]][documentation]
@@ -27,13 +27,24 @@
 [badge-adown]: https://static.pepy.tech/badge/decoupler
 [badge-stars]: https://img.shields.io/github/stars/scverse/decoupler?style=flat&logo=github&color=yellow
 
-`decoupler` is a python package containing different enrichment statistical
-methods to extract biologically driven scores
-from omics data within a unified framework. This is its faster and memory efficient Python implementation,
+`decoupler` is a scverse core package containing different enrichment statistical
+methods to extract biologically driven scores from omics data within a unified framework.
+This is its faster and memory efficient Python implementation,
 a deprecated version in R can be found [here](https://github.com/saezlab/decoupler).
 
-It is a package from the [scverse][] ecosystem {cite:p}`scverse`,
-designed for easy interoperability with `anndata`, `scanpy` {cite:p}`scanpy` and other related packages.
+[//]: # (numfocus-fiscal-sponsor-attribution)
+
+decoupler is part of the scverse® project ([website](https://scverse.org), [governance](https://scverse.org/about/roles)) and is fiscally sponsored by [NumFOCUS](https://numfocus.org/).
+If you like scverse® and want to support our mission, please consider making a tax-deductible [donation](https://numfocus.org/donate-to-scverse) to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.
+
+<div align="center">
+<a href="https://numfocus.org/project/scverse">
+  <img
+    src="https://raw.githubusercontent.com/numfocus/templates/master/images/numfocus-logo.png"
+    width="200"
+  >
+</a>
+</div>
 
 ## Getting started
 

docs/_static/css/custom.css

@@ -5,10 +5,10 @@ div.cell_output table.dataframe {
 
 /* Adjust the logo size */
 .logo img {
-    width: 50%;   /* or any percentage you want */
+    width: 50%; /* or any percentage you want */
     height: auto; /* maintain aspect ratio */
 }
 
 img.no-scaled-link {
     background: transparent !important;
-}
+}

docs/api/index.md

@@ -21,4 +21,4 @@ pp
 
 pl
 tl
-```
+```

docs/api/mt.md

@@ -30,4 +30,4 @@
 
     mt.decouple
     mt.consensus
-```
+```

docs/api/op.md

@@ -25,4 +25,4 @@
     op.show_resources
     op.show_organisms
     op.translate
-```
+```

docs/api/pl.md

@@ -20,4 +20,4 @@
     pl.order
     pl.source_targets
     pl.volcano
-```
+```

docs/conf.py

@@ -97,7 +97,7 @@
     "scanpy": ("https://scanpy.readthedocs.io/en/stable/", None),
     "numpy": ("https://numpy.org/doc/stable/", None),
     "matplotlib": ("https://matplotlib.org/stable/", None),
-    'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
 }
 
 # List of patterns, relative to source directory, that match files and
@@ -115,8 +115,8 @@
 html_static_path = ["_static"]
 html_css_files = ["css/custom.css"]
 html_title = project_name
-html_logo = '_static/images/logo.png'
-html_favicon = '_static/images/logo.png'
+html_logo = "_static/images/logo.png"
+html_favicon = "_static/images/logo.png"
 
 html_theme_options = {
     "repository_url": repository_url,

docs/notebooks/bench/rna.ipynb

@@ -408,16 +408,11 @@
     "def random(x, w):\n",
     "    return np.random.rand(1)[0]\n",
     "\n",
+    "\n",
     "df = dc.bm.benchmark(\n",
     "    adata=adata,\n",
     "    net=net,\n",
-    "    kws_decouple={\n",
-    "        'cons': True,\n",
-    "        'tmin': 3,\n",
-    "        'args':{\n",
-    "            'waggr': {'fun': random}\n",
-    "        }\n",
-    "    },\n",
+    "    kws_decouple={\"cons\": True, \"tmin\": 3, \"args\": {\"waggr\": {\"fun\": random}}},\n",
     ")"
    ]
   },
@@ -578,7 +573,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.auc(df, hue='method')"
+    "dc.bm.pl.auc(df, hue=\"method\")"
    ]
   },
   {
@@ -614,7 +609,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.fscore(df, hue='method')"
+    "dc.bm.pl.fscore(df, hue=\"method\")"
    ]
   },
   {
@@ -650,7 +645,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.qrank(df, hue='method')"
+    "dc.bm.pl.qrank(df, hue=\"method\")"
    ]
   },
   {
@@ -968,9 +963,9 @@
     }
    ],
    "source": [
-    "dc.bm.pl.bar(df=hdf, x='H(auroc, auprc)', y='method', hue='method')\n",
-    "dc.bm.pl.bar(df=hdf, x='F-score', y='method', hue='method')\n",
-    "dc.bm.pl.bar(df=hdf, x='H(1-qrank, -log10(pval))', y='method', hue='method')"
+    "dc.bm.pl.bar(df=hdf, x=\"H(auroc, auprc)\", y=\"method\", hue=\"method\")\n",
+    "dc.bm.pl.bar(df=hdf, x=\"F-score\", y=\"method\", hue=\"method\")\n",
+    "dc.bm.pl.bar(df=hdf, x=\"H(1-qrank, -log10(pval))\", y=\"method\", hue=\"method\")"
    ]
   },
   {
@@ -1000,7 +995,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.summary(df=hdf, y='method', figsize=(6, 3))"
+    "dc.bm.pl.summary(df=hdf, y=\"method\", figsize=(6, 3))"
    ]
   },
   {
@@ -1644,8 +1639,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "unw_ct = ct.drop(columns='weight')\n",
-    "unw_do = do.drop(columns='weight')"
+    "unw_ct = ct.drop(columns=\"weight\")\n",
+    "unw_do = do.drop(columns=\"weight\")"
    ]
   },
   {
@@ -1667,15 +1662,15 @@
     "df = dc.bm.benchmark(\n",
     "    adata=adata,\n",
     "    net={\n",
-    "        'collectri': ct,\n",
-    "        'dorothea': do,\n",
-    "        'unw_collectri': unw_ct,\n",
-    "        'unw_dorothea': unw_do,\n",
-    "        'r_collectri': rct,\n",
-    "        'r_dorothea': rdo,\n",
+    "        \"collectri\": ct,\n",
+    "        \"dorothea\": do,\n",
+    "        \"unw_collectri\": unw_ct,\n",
+    "        \"unw_dorothea\": unw_do,\n",
+    "        \"r_collectri\": rct,\n",
+    "        \"r_dorothea\": rdo,\n",
     "    },\n",
     "    kws_decouple={\n",
-    "        'methods': 'ulm',\n",
+    "        \"methods\": \"ulm\",\n",
     "    },\n",
     ")"
    ]
@@ -1897,7 +1892,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.auc(df, hue='net')"
+    "dc.bm.pl.auc(df, hue=\"net\")"
    ]
   },
   {
@@ -1926,7 +1921,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.fscore(df, hue='net')"
+    "dc.bm.pl.fscore(df, hue=\"net\")"
    ]
   },
   {
@@ -1955,7 +1950,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.qrank(df, hue='net')"
+    "dc.bm.pl.qrank(df, hue=\"net\")"
    ]
   },
   {
@@ -2172,9 +2167,9 @@
     }
    ],
    "source": [
-    "dc.bm.pl.bar(df=hdf, x='H(auroc, auprc)', y='net', hue='net')\n",
-    "dc.bm.pl.bar(df=hdf, x='F-score', y='net', hue='net')\n",
-    "dc.bm.pl.bar(df=hdf, x='H(1-qrank, -log10(pval))', y='net', hue='net')"
+    "dc.bm.pl.bar(df=hdf, x=\"H(auroc, auprc)\", y=\"net\", hue=\"net\")\n",
+    "dc.bm.pl.bar(df=hdf, x=\"F-score\", y=\"net\", hue=\"net\")\n",
+    "dc.bm.pl.bar(df=hdf, x=\"H(1-qrank, -log10(pval))\", y=\"net\", hue=\"net\")"
    ]
   },
   {
@@ -2204,7 +2199,7 @@
     }
    ],
    "source": [
-    "dc.bm.pl.summary(df=hdf, y='net', figsize=(7, 3))"
+    "dc.bm.pl.summary(df=hdf, y=\"net\", figsize=(7, 3))"
    ]
   },
   {