- Changed dependency: arboretum package was renamed to arboreto.

- Set version of loompy package to 2.0.2 because setup.py in more recent versions is not compatible with python 3.5.

README.rst

@@ -14,13 +14,13 @@ in no time. The latter is achieved via the dask_ framework for distributed compu
 
 The pipeline has three steps:
 
-1. First transcription factors (TFs) and their target genes, together defining a regulon, are derived using gene inference methods which solely rely on correlations between expression of genes across cells. The arboretum_ package is used for this step.
+1. First transcription factors (TFs) and their target genes, together defining a regulon, are derived using gene inference methods which solely rely on correlations between expression of genes across cells. The arboreto_ package is used for this step.
 2. These regulons are refined by pruning targets that do not have an enrichment for a corresponding motif of the TF effectively separating direct from indirect targets based on the presence of cis-regulatory footprints.
 3. Finally, the original cells are differentiated and clustered on the activity of these discovered regulons.
 
 
 .. note::
-    The most impactfull speed improvement is introduced by the arboretum_ package in step 1. This package provides an alternative to GENIE3 [3]_ called GRNBoost2. This package can be controlled from within pySCENIC.
+    The most impactfull speed improvement is introduced by the arboreto_ package in step 1. This package provides an alternative to GENIE3 [3]_ called GRNBoost2. This package can be controlled from within pySCENIC.
 
 
 .. sidebar:: **Quick Start**
@@ -103,8 +103,8 @@ First we import the necessary modules and declare some constants:
 
     from dask.diagnostics import ProgressBar
 
-    from arboretum.utils import load_tf_names
-    from arboretum.algo import grnboost2
+    from arboreto.utils import load_tf_names
+    from arboreto.algo import grnboost2
 
     from pyscenic.rnkdb import FeatherRankingDatabase as RankingDatabase
     from pyscenic.utils import modules_from_adjacencies, load_motifs
@@ -172,10 +172,10 @@ Phase I: Inference of co-expression modules
 In the initial phase of the pySCENIC pipeline the single cell expression profiles are used to infer 
 co-expression modules from.
 
-Run GENIE3 or GRNBoost from arboretum_ to infer co-expression modules
+Run GENIE3 or GRNBoost from arboreto_ to infer co-expression modules
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The arboretum package is used for this phase of the pipeline. For this notebook only a sample of 1,000 cells is used
+The arboreto package is used for this phase of the pipeline. For this notebook only a sample of 1,000 cells is used
 for the co-expression module inference is used.
 
 .. code-block:: python
@@ -321,7 +321,7 @@ References
 .. _distributed: https://distributed.readthedocs.io/en/latest/
 .. _LCB: https://aertslab.org
 .. _feather: https://github.com/wesm/feather
-.. _arboretum: https://arboretum.readthedocs.io
+.. _arboreto: https://arboreto.readthedocs.io
 .. _notebooks: https://github.com/aertslab/pySCENIC/tree/master/notebooks
 .. _issue: https://github.com/aertslab/pySCENIC/issues/new
 .. _SCENIC: http://scenic.aertslab.org

docs/index.rst

@@ -14,12 +14,12 @@ in no time. The latter is achieved via the dask_ framework for distributed compu
 
 The pipeline has three steps:
 
-1. First transcription factors (TFs) and their target genes, together defining a regulon, are derived using gene inference methods which solely rely on correlations between expression of genes across cells. The arboretum_ package is used for this step.
+1. First transcription factors (TFs) and their target genes, together defining a regulon, are derived using gene inference methods which solely rely on correlations between expression of genes across cells. The arboreto_ package is used for this step.
 2. These regulons are refined by pruning targets that do not have an enrichment for a corresponding motif of the TF effectively separating direct from indirect targets based on the presence of cis-regulatory footprints.
 3. Finally, the original cells are differentiated and clustered on the activity of these discovered regulons.
 
 .. note::
-    The most impactfull speed improvement is introduced by the arboretum_ package in step 1. This package provides an alternative to GENIE3 [3]_ called GRNBoost2. This package can be controlled from within pySCENIC.
+    The most impactfull speed improvement is introduced by the arboreto_ package in step 1. This package provides an alternative to GENIE3 [3]_ called GRNBoost2. This package can be controlled from within pySCENIC.
 
 .. sidebar:: **Quick Start**
 
@@ -97,8 +97,8 @@ First we import the necessary modules and declare some constants:
 
     from dask.diagnostics import ProgressBar
 
-    from arboretum.utils import load_tf_names
-    from arboretum.algo import grnboost2
+    from arboreto.utils import load_tf_names
+    from arboreto.algo import grnboost2
 
     from pyscenic.rnkdb import FeatherRankingDatabase as RankingDatabase
     from pyscenic.utils import modules_from_adjacencies, load_motifs
@@ -166,10 +166,10 @@ Phase I: Inference of co-expression modules
 In the initial phase of the pySCENIC pipeline the single cell expression profiles are used to infer
 co-expression modules from.
 
-Run GENIE3 or GRNBoost from arboretum_ to infer co-expression modules
+Run GENIE3 or GRNBoost from arboreto_ to infer co-expression modules
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The arboretum package is used for this phase of the pipeline. For this notebook only a sample of 1,000 cells is used
+The arboreto package is used for this phase of the pipeline. For this notebook only a sample of 1,000 cells is used
 for the co-expression module inference is used.
 
 .. code-block:: python
@@ -315,7 +315,7 @@ References
 .. _distributed: https://distributed.readthedocs.io/en/latest/
 .. _LCB: https://aertslab.org
 .. _feather: https://github.com/wesm/feather
-.. _arboretum: https://arboretum.readthedocs.io
+.. _arboreto: https://arboreto.readthedocs.io
 .. _notebooks: https://github.com/aertslab/pySCENIC/tree/master/notebooks
 .. _issue: https://github.com/aertslab/pySCENIC/issues/new
 .. _SCENIC: http://scenic.aertslab.org

notebooks/pySCENIC - Full pipeline.ipynb

@@ -26,8 +26,8 @@
     "import os, glob\n",
     "import pickle\n",
     "\n",
-    "from arboretum.utils import load_tf_names\n",
-    "from arboretum.algo import grnboost2\n",
+    "from arboreto.utils import load_tf_names\n",
+    "from arboreto.algo import grnboost2\n",
     "\n",
     "from pyscenic.rnkdb import FeatherRankingDatabase as RankingDatabase\n",
     "from pyscenic.utils import modules_from_adjacencies\n",

requirements.dev.txt

@@ -1,5 +1,5 @@
 cytoolz
-arboretum
+arboreto
 multiprocessing_on_dill
 numba==0.37.0
 llvmlite==0.22.0

requirements.txt

@@ -9,16 +9,15 @@ pandas>=0.20.1
 cloudpickle
 dask>=0.17.2
 distributed>=1.21.6
-arboretum
 pyarrow==0.8.0
 feather-format
-arboretum
+arboreto
 boltons
 setuptools
 pyyaml
 tqdm
 interlap
 umap-learn
-loompy
+loompy==2.0.2
 networkx
 matplotlib

scripts/hpc-grnboost.py

@@ -4,8 +4,8 @@
 import os, sys, glob
 import datetime
 from configparser import ConfigParser
-from arboretum.algo import grnboost2
-from arboretum.utils import load_tf_names
+from arboreto.algo import grnboost2
+from arboreto.utils import load_tf_names
 from dask.distributed import LocalCluster, Client
 #from cytoolz import mapcat
 import logging

src/pyscenic/cli/pyscenic.py

@@ -5,8 +5,8 @@
 import logging
 from dask.diagnostics import ProgressBar
 from multiprocessing import cpu_count
-from arboretum.algo import grnboost2
-from arboretum.utils import load_tf_names
+from arboreto.algo import grnboost2
+from arboreto.utils import load_tf_names
 
 from pyscenic.utils import load_from_yaml, modules_from_adjacencies
 from pyscenic.rnkdb import opendb, RankingDatabase

src/pyscenic/prune.py

@@ -248,6 +248,7 @@ def wrap(data):
             #def memoize(db: Type[RankingDatabase]) -> Type[RankingDatabase]:
             #    return MemoryDecorator(db)
             #delayed_or_future_dbs = list(map(wrap, map(memoize, rnkdbs)))
+            # Check also latest Stackoverflow message: https://stackoverflow.com/questions/50795901/dask-scatter-broadcast-a-list
             delayed_or_future_dbs = list(map(wrap, rnkdbs))
             # 3. The gene signatures: these signatures become large when chunking them, therefore chunking is overruled
             # when using dask.distributed.