Merge branch 'dev'

Author: cflerin

README.rst

@@ -3,21 +3,35 @@ pySCENIC
 
 |buildstatus|_ |pypipackage|_ |docstatus|_
 
+
 pySCENIC is a lightning-fast python implementation of the SCENIC_ pipeline (Single-Cell rEgulatory Network Inference and
 Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from
 single-cell RNA-seq data.
 
 The pioneering work was done in R and results were published in Nature Methods [1]_.
-A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols [5]_ (`see here <https://doi.org/10.1038/s41596-020-0336-2>`_).
+A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols [4]_.
 
 pySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells
 in no time. The latter is achieved via the dask_ framework for distributed computing [2]_.
 
-**Full documentation** is available on `Read the Docs <https://pyscenic.readthedocs.io/en/latest/>`_
+**Full documentation** for pySCENIC is available on `Read the Docs <https://pyscenic.readthedocs.io/en/latest/>`_
+
+----
+
+pySCENIC is part of the SCENIC Suite of tools! 
+See the main `SCENIC website <https://scenic.aertslab.org/>`_ for additional information and a full list of tools available.
+
+----
+
 
 News and releases
 -----------------
 
+0.11.2 | 2021-05-07
+^^^^^^^^^^^^^^^^^^^
+
+* Split some core cisTarget functions out into a separate repository, `ctxcore <https://github.com/aertslab/ctxcore>`_. This is now a required package for pySCENIC.
+
 0.11.1 | 2021-02-11
 ^^^^^^^^^^^^^^^^^^^
 
@@ -96,7 +110,9 @@ All the functionality of the original R implementation is available and in addit
 Additional resources
 --------------------
 
-For more information, please visit the main SCENIC_ website.
+For more information, please visit LCB_, 
+the main `SCENIC website <https://scenic.aertslab.org/>`_,
+or `SCENIC (R version) <https://github.com/aertslab/SCENIC>`_.
 There is a tutorial to `create new cisTarget databases <https://github.com/aertslab/create_cisTarget_databases>`_.
 The CLI to pySCENIC has also been streamlined into a pipeline that can be run with a single command, using the Nextflow workflow manager.
 There are two Nextflow implementations available:
@@ -114,11 +130,10 @@ We are grateful to all providers of TF-annotated position weight matrices, in pa
 References
 ----------
 
-.. [1] Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017).
+.. [1] Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017). `doi:10.1038/nmeth.4463 <https://doi.org/10.1038/nmeth.4463>`_
 .. [2] Rocklin, M. Dask: parallel computation with blocked algorithms and task scheduling. conference.scipy.org
-.. [3] Huynh-Thu, V. A. et al. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, (2010).
-.. [4] Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
-.. [5] Van de Sande B., Flerin C., et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. June 2020:1-30. doi:10.1038/s41596-020-0336-2
+.. [3] Huynh-Thu, V. A. et al. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, (2010). `doi:10.1371/journal.pone.0012776 <https://doi.org/10.1371/journal.pone.0012776>`_
+.. [4] Van de Sande B., Flerin C., et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. June 2020:1-30. `doi:10.1038/s41596-020-0336-2 <https://doi.org/10.1038/s41596-020-0336-2>`_
 
 .. |buildstatus| image:: https://travis-ci.org/aertslab/pySCENIC.svg?branch=master
 .. _buildstatus: https://travis-ci.org/aertslab/pySCENIC

docs/faq.rst

@@ -4,17 +4,8 @@ Frequently Asked Questions
 I am having problems with Dask
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The Arboreto package :code:`v0.1.5`, and some steps of the cisTarget step within pySCENIC, seem to depend on an older version of Dask/Distributed.
-Using a more recent version of Dask/Distributed can result in some cryptic errors.
-It is recommended to use the older version of Dask and Distributed for stability here:
-
-.. code-block:: bash
-
-    pip install dask==1.0.0 distributed'>=1.21.6,<2.0.0'
-
-
-But in many cases this still results in issues with the GRN step.
-An alternative is to use the multiprocessing implementation of Arboreto recently included in pySCENIC (`arboreto_with_multiprocessing.py <https://github.com/aertslab/pySCENIC/blob/master/src/pyscenic/cli/arboreto_with_multiprocessing.py>`_).
+An alternative is to use the multiprocessing implementation of Arboreto included in pySCENIC (`arboreto_with_multiprocessing.py <https://github.com/aertslab/pySCENIC/blob/master/src/pyscenic/cli/arboreto_with_multiprocessing.py>`_).
+This scrips is also available on the path once pySCENIC is installed.
 This script uses the Arboreto and pySCENIC codebase to run GRNBoost2 (or GENIE3) without Dask.
 The eliminates the possibility of running the GRN step across multiple nodes, but brings provides additional stability.
 The run time is generally equivalent to the Dask implementation using the same number of workers.
@@ -101,7 +92,7 @@ Yes you can. The code snippet below shows you how to create your own databases:
 
 .. code-block:: python
 
-    from pyscenic.rnkdb import DataFrameRankingDatabase as RankingDatabase
+    from ctxcore.rnkdb import DataFrameRankingDatabase as RankingDatabase
     import numpy as np
     import pandas as pd
 
@@ -114,6 +105,10 @@ Yes you can. The code snippet below shows you how to create your own databases:
             dtype=np.int32)
     RankingDatabase(df, 'custom').save('custom.db')
 
+Please also see
+`create_cisTarget_databases <https://github.com/aertslab/create_cisTarget_databases>`_
+for more detailed and flexible methods to create custom cisTarget databases.
+
 
 Can I draw the distribution of AUC values for a regulon across cells?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

docs/installation.rst

@@ -44,7 +44,7 @@ You can also install the bleeding edge (i.e. less stable) version of the package
 
 
 Containers
-~~~~~~~~~
+~~~~~~~~~~
 
 **pySCENIC containers** are also available for download and immediate use. In this case, no compiling or installation is required, provided either Docker or Singularity software is installed on the user's system.  Images are available from `Docker Hub`_. Usage of the containers is shown below (`Docker and Singularity Images`_).
 To pull the docker images, for example:
@@ -194,7 +194,60 @@ Note that in this case, a bind needs to be specified.
 
 .. code-block:: bash
 
-    singularity exec -B /data:/data aertslab-pyscenic-0.10.0.sif ipython kernel -f {connection_file}
+    singularity exec -B /data:/data aertslab-pyscenic-latest.sif ipython kernel -f {connection_file}
+
+More generally, a local or remote kernel can be set up by using the following examples.
+These would go in a kernel file in ``~/.local/share/jupyter/kernels/pyscenic-latest/kernel.json`` (for example).
+
+**Remote singularity kernel:**
+
+.. code-block:: bash
+
+    {
+      "argv": [
+        "/software/jupyter/bin/python",
+        "-m",
+        "remote_ikernel",
+        "--interface",
+        "ssh",
+        "--host",
+        "r23i27n14",
+        "--workdir",
+        "~/",
+        "--kernel_cmd",
+        "singularity",
+        "exec",
+        "-B",
+        "/path/to/mounts",
+        "/path/to/aertslab-pyscenic-latest.sif",
+        "ipython",
+        "kernel",
+        "-f",
+        "{connection_file}"
+      ],
+      "display_name": "pySCENIC singularity remote",
+      "language": "Python"
+    }
+
+**Local singularity kernel:**
+
+.. code-block:: bash
+
+    {
+        "argv": [
+         "singularity",
+         "exec",
+         "-B",
+         "/path/to/mounts",
+         "/path/to/aertslab-pyscenic-latest.sif",
+         "ipython",
+         "kernel",
+         "-f",
+         "{connection_file}"
+        ],
+        "display_name": "pySCENIC singularity local",
+        "language": "python"
+    }
 
 
 Nextflow

docs/tutorial.rst

@@ -56,7 +56,7 @@ First we import the necessary modules and declare some constants:
     from arboreto.utils import load_tf_names
     from arboreto.algo import grnboost2
 
-    from pyscenic.rnkdb import FeatherRankingDatabase as RankingDatabase
+    from ctxcore.rnkdb import FeatherRankingDatabase as RankingDatabase
     from pyscenic.utils import modules_from_adjacencies, load_motifs
     from pyscenic.prune import prune2df, df2regulons
     from pyscenic.aucell import aucell

requirements.txt

@@ -1,3 +1,4 @@
+ctxcore
 cytoolz
 multiprocessing_on_dill
 llvmlite
@@ -9,7 +10,6 @@ pandas>=0.20.1
 cloudpickle
 dask
 distributed
-pyarrow>=0.11.1,<0.17.0
 arboreto>=0.1.6
 boltons
 setuptools

requirements_docker.txt

@@ -18,6 +18,7 @@ cffi==1.14.4
 chardet==3.0.4
 click==7.1.2
 cloudpickle==1.6.0
+ctxcore==0.1.1
 cycler==0.10.0
 Cython==0.29.21
 cytoolz==0.11.0

src/pyscenic/aucell.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 
 import pandas as pd
-from .recovery import enrichment4cells
+from ctxcore.recovery import enrichment4cells
 from tqdm import tqdm
 from typing import Sequence, Type
-from .genesig import GeneSignature
+from ctxcore.genesig import GeneSignature
 from multiprocessing import cpu_count, Process, Array
 from boltons.iterutils import chunked
 from multiprocessing.sharedctypes import RawArray

src/pyscenic/cli/db2feather.py

@@ -2,7 +2,7 @@
 
 import os
 import argparse
-from pyscenic.rnkdb import convert_sqlitedb_to_featherdb
+from ctxcore.rnkdb import convert_sqlitedb_to_featherdb
 
 
 def derive_db_name(fname: str) -> str:

src/pyscenic/cli/gmt2regions.py

@@ -3,7 +3,7 @@
 import os
 import argparse
 import sys
-from pyscenic.genesig import GeneSignature
+from ctxcore.genesig import GeneSignature
 from pyscenic.regions import RegionRankingDatabase, Delineation, convert
 
 

src/pyscenic/cli/invertdb.py

@@ -2,7 +2,7 @@
 
 import os
 import argparse
-from pyscenic.rnkdb import opendb, InvertedRankingDatabase
+from ctxcore.rnkdb import opendb, InvertedRankingDatabase
 
 
 def derive_db_name(fname: str) -> str: