lingress
========
The Lingress project is an initiative aimed at developing a streamlined
pipeline for the analysis of Nuclear Magnetic Resonance (NMR) datasets,
utilizing a univariate linear regression model. This package encompasses
the execution of linear regression analysis via the Ordinary Least
Squares (OLS) method and provides visual interpretations of the
resultant data. Notably, it includes the p-values of all NMR peaks in
its analytical scope.
Functionally, this program strives to fit a model of metabolic profiles
through the application of linear regression. Its design and
capabilities present a robust tool for in-depth and nuanced data
analysis in the realm of metabolic studies.
**How to install**
------------------
.. code:: bash
pip install lingress
**Example code**
----------------
You can use function unipair to prepare the data for linear regression
model. The function will return a pair-wise dataframe if you have more
than 2 groups to observe.
.. code:: python
from lingress import unipair
import pandas as pd
# Create a unipair object
test = unipair(dataset = df, column_name='Class')
test.get_dataset() # Get list of dataset of all pairs
.. code:: python
from lingress import lin_regression
import pandas as pd
# Create a lin_regression object
test = lin_regression(spectra_X, target=meta['Class'], label=meta['Class'], features = spectra_X.columns)
# Create dataset to do linear regression model
dataset = test.create_dataset()
# Fit model with linear regression
test.fit_model(dataset, method = "fdr_bh")
# Get report
test.report()
Note:
::
- `bonferroni` : one-step correction
- `sidak` : one-step correction
- `holm-sidak` : step down method using Sidak adjustments
- `holm` : step-down method using Bonferroni adjustments
- `simes-hochberg` : step-up method (independent)
- `hommel` : closed method based on Simes tests (non-negative)
- `fdr_bh` : Benjamini/Hochberg (non-negative)
- `fdr_by` : Benjamini/Yekutieli (negative)
- `fdr_tsbh` : two stage fdr correction (non-negative)
- `fdr_tsbky` : two stage fdr correction (non-negative)
Raw data
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"description": "lingress\n========\n\nThe Lingress project is an initiative aimed at developing a streamlined\npipeline for the analysis of Nuclear Magnetic Resonance (NMR) datasets,\nutilizing a univariate linear regression model. This package encompasses\nthe execution of linear regression analysis via the Ordinary Least\nSquares (OLS) method and provides visual interpretations of the\nresultant data. Notably, it includes the p-values of all NMR peaks in\nits analytical scope.\n\nFunctionally, this program strives to fit a model of metabolic profiles\nthrough the application of linear regression. Its design and\ncapabilities present a robust tool for in-depth and nuanced data\nanalysis in the realm of metabolic studies.\n\n**How to install**\n------------------\n\n.. code:: bash\n\n pip install lingress\n\n**Example code**\n----------------\n\nYou can use function unipair to prepare the data for linear regression\nmodel. The function will return a pair-wise dataframe if you have more\nthan 2 groups to observe.\n\n.. code:: python\n\n from lingress import unipair\n import pandas as pd\n\n # Create a unipair object\n test = unipair(dataset = df, column_name='Class')\n\n test.get_dataset() # Get list of dataset of all pairs\n\n.. code:: python\n\n from lingress import lin_regression\n import pandas as pd\n\n\n # Create a lin_regression object\n test = lin_regression(spectra_X, target=meta['Class'], label=meta['Class'], features = spectra_X.columns)\n\n # Create dataset to do linear regression model\n dataset = test.create_dataset()\n\n # Fit model with linear regression\n test.fit_model(dataset, method = \"fdr_bh\")\n\n # Get report\n test.report()\n\nNote:\n\n::\n\n - `bonferroni` : one-step correction\n - `sidak` : one-step correction\n - `holm-sidak` : step down method using Sidak adjustments \n - `holm` : step-down method using Bonferroni adjustments \n - `simes-hochberg` : step-up method (independent) \n - `hommel` : closed method based on Simes tests (non-negative)\n - `fdr_bh` : Benjamini/Hochberg (non-negative)\n - `fdr_by` : Benjamini/Yekutieli (negative)\n - `fdr_tsbh` : two stage fdr correction (non-negative)\n - `fdr_tsbky` : two stage fdr correction (non-negative)\n",
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