https://github.com/midnighter/conifer-analysis

Post-process conifer output for downstream statistical analysis.
https://github.com/midnighter/conifer-analysis

Last synced: 11 days ago
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Post-process conifer output for downstream statistical analysis.

• Host: GitHub
• URL: https://github.com/midnighter/conifer-analysis
• Owner: Midnighter
• License: apache-2.0
• Created: 2022-03-04T20:19:56.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-03-04T21:05:17.000Z (over 2 years ago)
• Last Synced: 2024-10-31T16:51:05.221Z (20 days ago)
• Language: Python
• Size: 35.2 KB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • Contributing: .github/CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
=============================

Conifer Analysis

=============================

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.. summary-start

Post-process conifer output for downstream statistical analysis.

``conifer-analysis`` uses `dask `_ in order to analyze

`conifer `_ results in a distributed and

out-of-memory fashion. This can be helpful when processing many such results.

Example

=======

Say that you have a bunch of ``conifer`` results in a directory. You can

generate a histogram of the confidence values per file (sample) and per taxa

using the provided pipeline ``confidence_hist``. Even when you work locally, it

can be helpful to explicitly create a distributed client controlling the number

of workers.

.. code-block:: python

    from dask.distributed import Client

    from conifer_analysis import confidence_hist

    client = Client(n_workers=8)

You can then visit the `default dashboard `_ in

your browser to observe tasks live.  Next, we run the pipeline which returns a

``pandas.DataFrame``.

.. code-block:: python

    hist = confidence_hist("data/*.tsv")

    hist.info()

As an example of the returned shape:

.. code-block:: console

    

    RangeIndex: 7700 entries, 0 to 7699

    Data columns (total 8 columns):

     #   Column       Non-Null Count  Dtype

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

     0   path         7700 non-null   category

     1   name         7700 non-null   category

     2   taxonomy_id  7700 non-null   category

     3   bin          7700 non-null   interval[float64, right]

     4   midpoints    7700 non-null   float64

     5   read1_hist   7700 non-null   int64

     6   read2_hist   7700 non-null   int64

     7   avg_hist     7700 non-null   int64

    dtypes: category(3), float64(1), int64(3), interval(1)

    memory usage: 385.3 KB

Install

=======

It's as simple as:

.. code-block:: console

    pip install conifer-analysis

If you want to observe tasks in the dask dashboard, you will need additional

dependencies.

.. code-block:: console

    pip install conifer-analysis[dashboard]

Copyright

=========

* Copyright © 2022, Moritz E. Beber.

* Free software distributed under the `Apache Software License 2.0

  `_.

.. summary-end