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Merge branch 'master' into diff_input_processing_recoding
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‎docs/source/conf.py‎

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intersphinx_mapping = {
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'python': ('https://docs.python.org/3', None),
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'networkx': ('https://networkx.github.io/documentation/stable', None),
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'sqlalchemy': ('https://docs.sqlalchemy.org/en/13/', None),
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'pybel': ('https://pybel.readthedocs.io/en/latest/', None),
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}
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autodoc_member_order = 'bysource'

‎docs/source/intro.rst‎

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@@ -14,6 +14,7 @@ You can submit your dataset in any of the following formats:
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Please ensure that the dataset minimally has a column 'Node' containing node IDs. You can also optionally add the
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following columns to your dataset:
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- NodeType
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- LogFC [*]_
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- p-value
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| D |
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+------------+
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2. You can also choose to provide a dataset with a column 'Node' containing node IDs as well as a column 'logFC' with
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their abs(LogFC).
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2. You can also provide a dataset with a column 'Node' containing node IDs as well as a column 'NodeType', indicating
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the entity type of the node to run diffusion by entity type.
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+------------+--------------+
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| Node | NodeType |
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+============+==============+
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| A | Gene |
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+------------+--------------+
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| B | Gene |
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+------------+--------------+
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| C | Metabolite |
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+------------+--------------+
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| D | Gene |
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+------------+--------------+
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3. You can also choose to provide a dataset with a column 'Node' containing node IDs as well as a column 'logFC' with
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their logFC. You may also add a 'NodeType' column to run diffusion by entity type.
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+--------------+------------+
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| Node | LogFC |
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+==============+============+
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| Gene A | 4 |
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| A | 4 |
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+--------------+------------+
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| Gene B | -1 |
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| B | -1 |
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+--------------+------------+
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| Metabolite C | 1.5 |
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| C | 1.5 |
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+--------------+------------+
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| Gene D | 3 |
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| D | 3 |
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+--------------+------------+
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3. Finally, you can provide a dataset with a column 'Node' containing node IDs, a column 'logFC' with their abs(LogFC)
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and a column 'p-value' with adjusted p-values.
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4. Finally, you can provide a dataset with a column 'Node' containing node IDs, a column 'logFC' with their logFC and a
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column 'p-value' with adjusted p-values. You may also add a 'NodeType' column to run diffusion by entity type.
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+--------------+------------+---------+
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| Node | LogFC | p-value |
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+==============+============+=========+
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| Gene A | 4 | 0.03 |
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| A | 4 | 0.03 |
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+--------------+------------+---------+
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| Gene B | -1 | 0.05 |
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| B | -1 | 0.05 |
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+--------------+------------+---------+
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| Metabolite C | 1.5 | 0.001 |
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| C | 1.5 | 0.001 |
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+--------------+------------+---------+
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| Gene D | 3 | 0.07 |
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| D | 3 | 0.07 |
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+--------------+------------+---------+
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You can also take a look at our `sample datasets <https://github.com/multipaths/DiffuPy/tree/master/examples/datasets>`_
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folder for some examples files.
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See the `sample datasets <https://github.com/multipaths/DiffuPy/tree/master/examples/datasets>`_ directory for example
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files.
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Networks
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--------
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~~~~~~~~~~~~~~~~~~~~~~
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+-----------+--------------+-------------+
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| Source | Target | Relation |
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| Source | Target | Relation |
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+===========+==============+=============+
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| Gene A | Gene B | Increase |
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| A | B | Increase |
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+-----------+--------------+-------------+
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| Gene B | Metabolite C | Association |
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| B | C | Association |
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+-----------+--------------+-------------+
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| Gene A | Pathology D | Association |
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| A | D | Association |
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+-----------+--------------+-------------+
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You can also take a look at our `sample networks <https://github.com/multipaths/DiffuPy/tree/master/examples/networks>`_

‎examples/README.rst‎

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@@ -11,6 +11,7 @@ You can submit your dataset in any of the following formats:
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Please ensure that the dataset minimally has a column 'Node' containing node IDs. You can also optionally add the
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following columns to your dataset:
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- NodeType
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- LogFC [*]_
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- p-value
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| D |
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+------------+
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2. You can also choose to provide a dataset with a column 'Node' containing node IDs as well as a column 'logFC' with
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their | logFC |.
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2. You can also provide a dataset with a column 'Node' containing node IDs as well as a column 'NodeType', indicating
44+
the entity type of the node to run diffusion by entity type.
45+
46+
+------------+--------------+
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| Node | NodeType |
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+============+==============+
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| A | Gene |
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+------------+--------------+
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| B | Gene |
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+------------+--------------+
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| C | Metabolite |
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+------------+--------------+
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| D | Gene |
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+------------+--------------+
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3. You can also choose to provide a dataset with a column 'Node' containing node IDs as well as a column 'logFC' with
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their logFC. You may also add a 'NodeType' column to run diffusion by entity type.
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+--------------+------------+
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| Node | LogFC |
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+==============+============+
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| Gene A | 4 |
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| A | 4 |
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+--------------+------------+
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| Gene B | -1 |
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| B | -1 |
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+--------------+------------+
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| Metabolite C | 1.5 |
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| C | 1.5 |
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+--------------+------------+
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| Gene D | 3 |
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| D | 3 |
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+--------------+------------+
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57-
.. | logFC | replace:: Log\ :sub:`2`\ FC
58-
59-
3. Finally, you can provide a dataset with a column 'Node' containing node IDs, a column 'logFC' with their | logFC | and
60-
a column 'p-value' with adjusted p-values.
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4. Finally, you can provide a dataset with a column 'Node' containing node IDs, a column 'logFC' with their logFC and a
74+
column 'p-value' with adjusted p-values. You may also add a 'NodeType' column to run diffusion by entity type.
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+--------------+------------+---------+
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| Node | LogFC | p-value |
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+==============+============+=========+
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| Gene A | 4 | 0.03 |
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| A | 4 | 0.03 |
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+--------------+------------+---------+
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| Gene B | -1 | 0.05 |
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| B | -1 | 0.05 |
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+--------------+------------+---------+
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| Metabolite C | 1.5 | 0.001 |
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| C | 1.5 | 0.001 |
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+--------------+------------+---------+
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| Gene D | 3 | 0.07 |
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| D | 3 | 0.07 |
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+--------------+------------+---------+
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See the `sample datasets <https://github.com/multipaths/DiffuPy/tree/master/examples/datasets>`_ directory for example
@@ -118,11 +132,11 @@ Custom-network example
118132
+-----------+--------------+-------------+
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| Source | Target | Relation |
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+===========+==============+=============+
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| Gene A | Gene B | Increase |
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| A | B | Increase |
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+-----------+--------------+-------------+
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| Gene B | Metabolite C | Association |
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| B | C | Association |
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+-----------+--------------+-------------+
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| Gene A | Pathology D | Association |
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| A | D | Association |
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+-----------+--------------+-------------+
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See the `sample networks <https://github.com/multipaths/DiffuPy/tree/master/examples/networks>`_ directory for some

‎examples/datasets/node_type.csv‎

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Node,NodeType
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A,Gene
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B,Gene
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C,Metabolite
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D,Gene
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E,Metabolite
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F,Gene
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G,Pathology

‎examples/networks/sample_network.csv‎

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Source,Target,Relation
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Gene A,Gene B,Increase
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Gene B,Metabolite C,Association
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Gene A,Pathology D,Association
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A, B, Increase
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B, C, Association
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A, D, Association
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‎setup.cfg‎

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description = Compute diffusion scores over networks
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long_description = file: README.rst
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10-
# URLs associated with PyBEL
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# URLs associated with DiffuPy
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url = https://github.com/multipaths/DiffuPy
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download_url = https://github.com/multipaths/DiffuPy
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project_urls =

‎src/diffupy/cli.py‎

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import time
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import click
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from diffupath.constants import OUTPUT_DIR
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from diffupy.process_network import get_kernel_from_network_path
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from .process_network import get_kernel_from_network_path
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1515
from .constants import OUTPUT, METHODS, EMOJI, RAW, CSV, JSON
1616
from .diffuse import diffuse as run_diffusion
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9393
'-o', '--output',
9494
type=click.File('w'),
9595
help="Output file",
96-
default=OUTPUT_DIR,
96+
default=OUTPUT,
9797
)
9898
@click.option(
9999
'-m', '--method',
@@ -141,7 +141,7 @@ def kernel(
141141
def diffuse(
142142
input: str,
143143
network: str,
144-
output: str = OUTPUT_DIR,
144+
output: str = OUTPUT,
145145
method: str = RAW,
146146
binarize: bool = False,
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threshold: float = None,

‎src/diffupy/process_input.py‎

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266266
threshold
267267
)
268268

269+
# Standardize the title of the node column labeling column to 'Label', for later processing.
270+
if LABEL not in df.columns:
271+
for l in list(df.columns):
272+
if l in NODE_LABELING:
273+
df = df.rename(columns={l: LABEL})
274+
break
275+
276+
# If node type provided in a column, classify in a dictionary the input codification by its node type.
277+
if NODE_TYPE in df.columns:
278+
279+
node_types = list(set(df[NODE_TYPE])) # Get the node types list set.
280+
codified_by_type_dict = {}
281+
282+
for node_type in node_types:
283+
# Filter the nodes by the iterable type.
284+
df_by_type = df.loc[df[NODE_TYPE] == node_type]
285+
286+
# Codify the nodes for the iterable type.
287+
codified_by_type_dict[node_type] = _codify_method_check(df_by_type,
288+
method,
289+
binning,
290+
absolute_value,
291+
p_value,
292+
threshold
293+
)
294+
return codified_by_type_dict
269295

270296
def _codify_method_check(
271297
df: pd.DataFrame,
@@ -587,6 +613,7 @@ def mapping_statistics(
587613

588614
total_mapping.update(mapping)
589615

616+
590617
if subtotals:
591618
statistics_dict['total_mapping'] = total_mapping
592619
statistics_dict['total_input'] = total_input
@@ -755,7 +782,7 @@ def _map_label_dict(
755782
label_bck = _check_label_to_background_labels(label, background_labels, check_substrings)
756783
if label_bck is not None:
757784
mapped_dict[label_bck] = v
758-
785+
759786
elif isinstance(label, set) or isinstance(label, tuple) or isinstance(label, list):
760787
for sublabel in set(label):
761788
label_bck = _check_label_to_background_labels(sublabel, background_labels, check_substrings)

‎src/diffupy/utils.py‎

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2424
"""Matrix/graph handling utils."""
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2626

27+
2728
def get_laplacian(graph: Graph, normalized: bool = False) -> np.ndarray:
2829
"""Return Laplacian matrix."""
2930
if nx.is_directed(graph):
@@ -137,18 +138,16 @@ def print_dict_dimensions(entities_db, title='Title', message=''):
137138
"""Print dimension of the dictionary."""
138139
total = 0
139140
m = f'{title}\n'
140-
141141
for k1, v1 in entities_db.items():
142142
m += f'\n{message}{k1}:\n'
143143
if isinstance(v1, dict):
144144
for k2, v2 in v1.items():
145145
m += f'{k2} ({v2})\n'
146146
else:
147147
m += f'{v1}'
148-
148+
149149
print(f'{m}\n\n')
150150

151-
152151
def log_dict(dict_to_print: dict, message: str = ''):
153152
"""Print dictionary as list with a message."""
154153
for k1, v1 in dict_to_print.items():
@@ -301,6 +300,12 @@ def munge_cell(cell):
301300
else:
302301
raise TypeError(f'The cell "{cell}" could not be processed.')
303302

303+
def parse_xls_sheet_to_df(sheet: opxl.workbook,
304+
min_row: Optional[int] = 1,
305+
relevant_cols: Optional[list] = None,
306+
irrelevant_cols: Optional[list] = None) -> pd.DataFrame:
307+
"""Process/format excel sheets to DataFrame."""
308+
parsed_sheet_dict = {}
304309

305310
def parse_xls_sheet_to_df(sheet: opxl.workbook,
306311
min_row: Optional[int] = 1,

‎tests/resources/datasets/node_type_col.csv‎

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NodeType,Node
2-
Gene,A
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Gene,B
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Metabolite,C
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Gene,D
6-
Gene,E
1+
Node,NodeType
2+
A,Gene
3+
B,Gene
4+
C,Metabolite
5+
D,Gene
6+
E,Metabolite
7+
F,Gene
8+
G,Pathology

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