### Check Arboreto Installation Details

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Verify the installation of the Arboreto package and display its metadata, including version, dependencies, and installation location. This is useful for confirming a successful installation.

```bash
$ pip show arboreto

```

--------------------------------

### Install and show Arboreto

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Install the latest version of Arboreto using pip and display its package information.

```bash
$ pip install arboreto

$ pip show arboreto

Name: arboreto
Version: 0.1.5
Summary: Scalable gene regulatory network inference using tree-based ensemble regressors
Home-page: https://github.com/tmoerman/arboreto
Author: Thomas Moerman
Author-email: thomas.moerman@gmail.com
License: BSD 3-Clause License
Location: /vsc-hard-mounts/leuven-data/software/biomed/Anaconda/5-Python-3.6/lib/python3.6/site-packages
Requires: scikit-learn, dask, numpy, scipy, distributed, pandas

```

--------------------------------

### Install Arboreto from Built Source

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Install the Arboreto package after building it from source using pip. This command installs the package from the local distribution files.

```bash
$ pip install dist/*

```

--------------------------------

### Build Arboreto from Source

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Build the Arboreto package from the cloned source code using the provided build script. This prepares the package for installation.

```bash
$ ./pypi_build.sh

```

--------------------------------

### Start Jupyter Lab Server

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Launches a Jupyter Lab server on a specified port. Use this command in your terminal to begin a Jupyter session.

```bash
$ jupyter lab --port 9999 --no-browser

[I 12:16:08.725 LabApp] JupyterLab alpha preview extension loaded from /data/leuven/software/biAnaconda/5-Python-3.6/lib/python3.6/site-packages/jupyterlab
JupyterLab v0.27.0
Known labextensions:
[I 12:16:08.739 LabApp] Running the core application with no additional extensions or settings
[I 12:16:08.766 LabApp] Serving notebooks from local directory: /ddn1/vol1/staging/leuven/stg_00002/lcb/tmoerman/nb
[I 12:16:08.766 LabApp] 0 active kernels
[I 12:16:08.766 LabApp] The Jupyter Notebook is running at:
[I 12:16:08.766 LabApp] http://localhost:9999/?token=2dca6ce946265895846795c4983191c9f76ba954f414efdf
[I 12:16:08.766 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 12:16:08.767 LabApp] 

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:9999/?token=2dca6ce946265895846795c4983191c9f76ba954f414efdf

```

--------------------------------

### Install Arboreto using Pip

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Install the Arboreto package from PyPI using the pip package manager. This is a straightforward method for installing Python packages.

```bash
$ pip install arboreto

```

--------------------------------

### Start Dask scheduler

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Launch the Dask scheduler on a VSC front node. This command also starts the Dask diagnostics dashboard.

```bash
$ dask-scheduler

distributed.scheduler - INFO - -----------------------------------------------
distributed.scheduler - INFO -   Scheduler at: tcp://10.118.224.134:8786
distributed.scheduler - INFO -       bokeh at:                    :35874
distributed.scheduler - INFO - Local Directory:    /tmp/scheduler-wu5odlrh
distributed.scheduler - INFO - -----------------------------------------------
distributed.worker - INFO -         Registered to:  tcp://10.118.224.134:8786
distributed.worker - INFO - -------------------------------------------------
distributed.worker - INFO -         Registered to:  tcp://10.118.224.134:8786
distributed.worker - INFO - -------------------------------------------------

```

--------------------------------

### Install Arboreto using Conda

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Install the Arboreto package from the bioconda channel using the conda package manager. This is the recommended installation method.

```bash
$ conda install -c bioconda arboreto

```

--------------------------------

### Create and Activate Conda Environment for Arboreto

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Create a new conda environment named 'arboreto-env', activate it, and then install Arboreto within this isolated environment. This ensures dependency management.

```bash
# create the conda environment named "arboreto-env"
$ conda create --name arboreto-env

# activate the conda environment we just created
$ source activate arboreto-env

# note: your terminal will indicate which environment is active on the left
(arboreto-env) $ ...

# install arboreto into the "arboreto-env" environment (hit Y to proceed)
(arboreto-env) $ conda install -c bioconda arboreto

```

--------------------------------

### Connect to Dask Client in Jupyter

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Initializes a Dask client connection to a specified scheduler address within a Jupyter notebook. This code is used to verify the Dask cluster setup.

```python
from distributed import Client

client = Client('tcp://10.118.224.134:8786')

client

```

--------------------------------

### Read Transposed Gene Expression Matrix with Pandas

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

This example demonstrates how to read a transposed gene expression matrix using Pandas. The .T attribute transposes the DataFrame, aligning genes with columns and observations with rows, as expected by scikit-learn.

```python
df = pd.read_csv(<ex_path>, index_col=0, sep='\t').T
```

--------------------------------

### Instantiate Dask Client with Local Directory

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

Use this snippet to specify a local directory for Dask client operations, which can help resolve worker connection issues. Ensure the specified directory has sufficient space.

```python
>>> from dask.distributed import Client, LocalCluster
>>> worker_kwargs = {'local_dir': '/tmp'}
>>> cluster = LocalCluster(**worker_kwargs)
>>> client = Client(cluster)
>>> client

<Client: scheduler='tcp://127.0.0.1:41803' processes=28 cores=28>
```

--------------------------------

### Initiate Arboreto GRN Inference with Dask

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Demonstrates how to specify a Dask client or address when calling the `genie3` function for distributed GRN inference. This enables parallel processing of the inference task.

```python
network_df = genie3(expression_data=ex_matrix,
                    tf_names=tf_names,
                    client_or_address=client)

```

--------------------------------

### Load Anaconda module

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Load the Anaconda module on VSC front nodes to prepare the environment for Python and Arboreto.

```bash
$ module load Anaconda/5-Python-3.6

```

--------------------------------

### Load tmux module

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Load the tmux module on VSC front nodes to manage multiple SSH sessions.

```bash
$ module load tmux/2.5-foss-2014a

```

--------------------------------

### Enable Dask Diagnostics Dashboard with Custom Client

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

To enable the Dask diagnostics dashboard, create a LocalCluster without specifying 'diagnostics_port'. A custom Client connected to this cluster can then be passed to Arboreto's GRN inference algorithms.

```python
local_cluster = LocalCluster()  # diagnostics dashboard is enabled
custom_client = Client(local_cluster)

network = grnboost2(expression_data=ex_matrix,
                    tf_names=tf_names,
                    client=custom_client)  # specify the custom client
```

--------------------------------

### Clone Arboreto Repository

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Clone the Arboreto GitHub repository locally using the git tool. This step is necessary before building from source.

```bash
$ git clone https://github.com/tmoerman/arboreto.git
$ cd arboreto

```

--------------------------------

### Launch Dask Workers

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Launches 24 worker processes with 1 thread each, connected to the specified scheduler address. Use `--nthreads 1` for Arboreto. The `nice` command is optional for lower process priority.

```bash
$ nice -n 10 dask-worker tcp://10.118.224.134:8786 --nprocs 24 --nthreads 1

```

```bash
$ nice -n 10 dask-worker tcp://10.118.224.134:8786 --nprocs 24 --nthreads 1

```

```bash
$ nice -n 10 dask-worker tcp://10.118.224.134:8786 --nprocs 24 --nthreads 1

```

```bash
$ nice -n 10 dask-worker tcp://10.118.224.134:8786 --nprocs 24 --nthreads 1

```

```bash
$ nice -n 10 dask-worker tcp://10.118.224.134:8786 --nprocs 24 --nthreads 1

```

--------------------------------

### Run GRNBoost2 with Python Script

Source: https://arboreto.readthedocs.io/en/latest/examples.html

This script demonstrates how to run the GRNBoost2 algorithm using a Python script. It loads expression data and transcription factor names, computes the gene regulatory network, and saves the output. Requires pandas and dask libraries.

```python
import pandas as pd

from distributed import Client, LocalCluster
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2

if __name__ == '__main__':

    in_file  = 'net1_expression_data.tsv'
    tf_file  = 'net1_transcription_factors.tsv'
    out_file = 'net1_grn_output.tsv'

    # ex_matrix is a DataFrame with gene names as column names
    ex_matrix = pd.read_csv(in_file, sep='\t')

    # tf_names is read using a utility function included in Arboreto
    tf_names = load_tf_names(tf_file)

    # instantiate a custom Dask distributed Client
    client = Client(LocalCluster())

    # compute the GRN
    network = grnboost2(expression_data=ex_matrix,
                        tf_names=tf_names,
                        client_or_address=client)

    # write the GRN to file
    network.to_csv(out_file, sep='\t', index=False, header=False)

```

```bash
cd <arboreto repo>/resources/dream5/net1
python run_grnboost2

```

--------------------------------

### Running GRN Inference with Custom Dask Client

Source: https://arboreto.readthedocs.io/en/latest/userguide.html

This snippet demonstrates running GRN inference multiple times with different seed values using a single custom Dask client. Close the client and cluster after use.

```python
import pandas as pd
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2
from distributed import LocalCluster, Client

if __name__ == '__main__':
    # create custom LocalCluster and Client instances
    local_cluster = LocalCluster(n_workers=10,
                                 threads_per_worker=1,
                                 memory_limit=8e9)
    custom_client = Client(local_cluster)

    # load the data
    ex_matrix = pd.read_csv(<ex_path>, sep='\t')
    tf_names = load_tf_names(<tf_path>)

    # run GRN inference multiple times
    network_666 = grnboost2(expression_data=ex_matrix,
                            tf_names=tf_names,
                            client_or_address=custom_client,  # specify the custom client
                            seed=666)

    network_777 = grnboost2(expression_data=ex_matrix,
                            tf_names=tf_names,
                            client_or_address=custom_client,  # specify the custom client
                            seed=777)

    # close the Client and LocalCluster after use
    client.close()
    local_cluster.close()

    network_666.to_csv('output_666.tsv', sep='\t', index=False, header=False)
    network_777.to_csv('output_777.tsv', sep='\t', index=False, header=False)

```

--------------------------------

### Run GRNBoost2 with Dask Distributed Scheduler

Source: https://arboreto.readthedocs.io/en/latest/userguide.html

This snippet shows how to connect Arboreto's GRNBoost2 algorithm to a remote Dask distributed scheduler. Ensure a Dask scheduler and workers are running and accessible at the specified address.

```python
import pandas as pd
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2
from distributed import Client

if __name__ == '__main__':
    ex_matrix = pd.read_csv(<ex_path>, sep='\t')
    tf_names = load_tf_names(<tf_path>)

    scheduler_address = 'tcp://10.118.224.134:8786'  # example address of the remote scheduler
    cluster_client = Client(scheduler_address)       # create a custom Client

    network = grnboost2(expression_data=ex_matrix,
                        tf_names=tf_names,
                        client_or_address=cluster_client)  # specify Client connected to the remote scheduler

    network.to_csv('output.tsv', sep='\t', index=False, header=False)

```

--------------------------------

### Dask Scheduler Bokeh Error

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

This log output indicates a 'TypeError: __init__() got an unexpected keyword argument 'host'' when the Dask scheduler attempts to launch the Bokeh service. This is a known issue fixed in Dask.distributed version 0.20.0.

```bash
vsc12345@r6i0n5 ~ 12:00 $ dask-scheduler

distributed.scheduler - INFO - -----------------------------------------------
distributed.scheduler - INFO - Could not launch service: ('bokeh', 8787)
Traceback (most recent call last):
  File "/data/leuven/software/biomed/Anaconda/5-Python-3.6/lib/python3.6/site-packages/distributed/scheduler.py", line 430, in start_services
    service.listen((listen_ip, port))
  File "/data/leuven/software/biomed/Anaconda/5-Python-3.6/lib/python3.6/site-packages/distributed/bokeh/core.py", line 31, in listen
    **kwargs)
  File "/data/leuven/software/biomed/Anaconda/5-Python-3.6/lib/python3.6/site-packages/bokeh/server/server.py", line 371, in __init__
    tornado_app = BokehTornado(applications, extra_websocket_origins=extra_websocket_origins, prefix=self.prefix, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'host'
distributed.scheduler - INFO -   Scheduler at: tcp://10.118.224.134:8786
distributed.scheduler - INFO -        http at:                     :9786
distributed.scheduler - INFO - Local Directory:    /tmp/scheduler-y6b8mnih
distributed.scheduler - INFO - -----------------------------------------------
distributed.scheduler - INFO - Receive client connection: Client-7b476bf6-c6d8-11e7-b839-a0040220fe80
distributed.scheduler - INFO - End scheduler at 'tcp://:8786'
```

--------------------------------

### Run GRNBoost2 with Pandas DataFrame

Source: https://arboreto.readthedocs.io/en/latest/userguide.html

Launches GRNBoost2 inference using an expression matrix provided as a Pandas DataFrame. Ensure the DataFrame has gene names as column headers. The `if __name__ == '__main__':` block is necessary for Dask process management.

```python
import pandas as pd
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # ex_matrix is a DataFrame with gene names as column names
    ex_matrix = pd.read_csv(<ex_path>, sep='\t')

    # tf_names is read using a utility function included in Arboreto
    tf_names = load_tf_names(<tf_path>)

    network = grnboost2(expression_data=ex_matrix,
                        tf_names=tf_names)

    network.to_csv('output.tsv', sep='\t', index=False, header=False)


```

--------------------------------

### Dask Scheduler Worker Connection Logs

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Logs indicating that worker processes have successfully connected to the Dask scheduler.

```log
distributed.scheduler - INFO - Register tcp://10.118.224.134:43342
distributed.scheduler - INFO - Starting worker compute stream, tcp://10.118.224.134:43342

```

--------------------------------

### SSH port forwarding for Dask dashboard

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Use SSH port forwarding to access the Dask diagnostics dashboard running on a VSC front node from your local machine.

```bash
ssh -L 8787:localhost:35874 hpc2-big3

```

--------------------------------

### Stabilize GRNBoost2 Output with a Seed

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

GRNBoost2 uses stochastic algorithms, leading to potentially different network outputs across runs. Providing a 'seed' value initializes the random number generator, ensuring reproducible results.

```python
network_df = grnboost2(expression_data=ex_matrix,
                       tf_names=tf_names,
                       seed=777)
```

--------------------------------

### Infer Gene Regulatory Network with GRNBoost2

Source: https://arboreto.readthedocs.io/en/latest/index.html

This snippet shows how to load expression data and transcription factor names, then use the grnboost2 function to infer a gene regulatory network. Ensure you have pandas, arboreto.utils, and arboreto.algo imported. Replace <ex_path> and <tf_path> with your actual file paths.

```python
# import python modules
import pandas as pd
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # load the data
    ex_matrix = pd.read_csv(<ex_path>, sep='\t')
    tf_names = load_tf_names(<tf_path>)

    # infer the gene regulatory network
    network = grnboost2(expression_data=ex_matrix,
                        tf_names=tf_names)

    network.head()

```

--------------------------------

### SSH Port Forwarding for Jupyter

Source: https://arboreto.readthedocs.io/en/latest/lcb.html

Establishes an SSH tunnel to forward local port 9999 to the remote Jupyter server's port 9999. This is necessary to access the notebook server from your local machine.

```bash
$ ssh -L 9999:localhost:9999 hpc2-big2

```

--------------------------------

### Expression Matrix as NumPy ndarray

Source: https://arboreto.readthedocs.io/en/latest/userguide.html

Use this snippet when your expression data is a NumPy ndarray. Ensure gene names are provided in the correct order corresponding to the matrix columns.

```python
import numpy as np
from arboreto.utils import load_tf_names
from arboreto.algo import grnboost2

if __name__ == '__main__':
    # ex_matrix is a numpy ndarray, which has no notion of column names
    ex_matrix = np.genfromtxt(<ex_path>, delimiter='\t', skip_header=1)

    # we read the gene names from the first line of the file
    with open(<ex_path>) as file:
        gene_names = [gene.strip() for gene in file.readline().split('\t')]

    # sanity check to verify the ndarray's nr of columns equals the length of the gene_names list
    assert ex_matrix.shape[1] == len(gene_names)

    # tf_names is read using a utility function included in Arboreto
    tf_names = load_tf_names(<tf_path>)

    network = grnboost2(expression_data=ex_matrix,
                        gene_names=gene_names,  # specify the gene_names
                        tf_names=tf_names)

    network.to_csv('output.tsv', sep='\t', index=False, header=False)

```

--------------------------------

### Workaround for Dask Scheduler Bokeh Error

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

As a workaround for the Dask scheduler Bokeh error, you can launch the scheduler with the Bokeh dashboard disabled using the '--no-bokeh' flag.

```bash
dask-scheduler --no-bokeh
```

--------------------------------

### Disable Dask Diagnostics Dashboard

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

When Arboreto creates a LocalCluster instance, the diagnostics dashboard is disabled by default. This snippet shows how to explicitly disable it by setting 'diagnostics_port' to None.

```python
local_cluster = LocalCluster(diagnostics_port=None)
client = Client(local_cluster)
```

--------------------------------

### Check DataFrame Shape

Source: https://arboreto.readthedocs.io/en/latest/troubleshooting.html

After reading or processing a gene expression matrix, it's crucial to verify its dimensions. This snippet shows how to check the shape of a Pandas DataFrame to ensure it matches expected dimensions (observations x genes).

```python
df.shape
```

--------------------------------

### Deactivate Conda Environment

Source: https://arboreto.readthedocs.io/en/latest/installation.html

Deactivate the currently active conda environment to return to the base environment. This is done after finishing work within the specific environment.

```bash
# deactivate the current environment
(arboreto-env) $ source deactivate

# as you will see: the environment indication has disappeared.
$ ...

```

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