cimcb lite
cimcb_lite is a lite version of the cimcb package containing a small number of basic tools for the statistical analysis of untargeted and targeted metabolomics data.
Installation
Dependencies
cimcb_lite requires:
- Python (>=3.5)
- Bokeh (>=1.0.0)
- NumPy
- SciPy
- scikit-learn
- Statsmodels
- tqdm
User installation
The recommend way to install cimcb_lite and dependencies is to using conda:
conda install -c cimcb cimcb_lite
or pip:
pip install cimcb_lite
Alternatively, to install directly from github:
pip install https://github.com/cimcb/cimcb_lite/archive/master.zip
API
For futher detail on the usage refer to the docstring.
cimcb_lite.model
- PLS_SIMPLS: Partial least-squares regression using the SIMPLS algorithm.
- train: Fit the PLS model, save additional stats (as attributes) and return Y predicted values.
- test: Calculate and return Y predicted value.
- evaluate: Plots a figure containing a Violin plot, Distribution plot, ROC plot and Binary Metrics statistics.
- calc_bootci: Calculates bootstrap confidence intervals based on bootlist.
- plot_featureimportance: Plots feature importance metrics.
- plot_permutation_test: Plots permutation test figures.
cimcb_lite.plot
- boxplot: Creates a boxplot using Bokeh.
- distribution: Creates a distribution plot using Bokeh.
- pca: Creates a PCA scores and loadings plot using Bokeh.
- permutation_test: Creates permutation test plots using Bokeh.
- roc_plot: Creates a rocplot using Bokeh.
- scatter: Creates a scatterplot using Bokeh.
- scatterCI: Creates a scatterCI plot using Bokeh.
cimcb_lite.cross_val
- kfold: Exhaustitive search over param_dict calculating binary metrics.
cimcb_lite.bootstrap
- Perc: Returns bootstrap confidence intervals using the percentile boostrap interval.
- BC: Returns bootstrap confidence intervals using the bias-corrected boostrap interval.
- BCA: Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.
cimcb_lite.utils
- binary_metrics: Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score.
- ci95_ellipse: Construct a 95% confidence ellipse using PCA.
- knnimpute: kNN missing value imputation using Euclidean distance.
- load_dataXL: Loads and validates the DataFile and PeakFile from an excel file.
- nested_getattr: getattr for nested attributes.
- scale: Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'.
- table_check: Error checking for DataTable and PeakTable (used in load_dataXL).
- univariate_2class: Creates a table of univariate statistics (2 class).
- wmean: Returns Weighted Mean. Ignores NaNs and handles infinite weights.
License
cimcb_lite is licensed under the MIT license.
Authors
Correspondence
Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.
1 maintainer
Metadata
1 maintainer
Release files for cimcb-lite 1.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cimcb_lite-1.0.2.tar.gz | 33.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cimcb_lite-1.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 81.3 kB
Release files / cimcb_lite-1.0.2.tar.gz
| Download URL | cimcb_lite-1.0.2.tar.gz |
|---|---|
| Size | 33.7 kB |
| Tags | Source |
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Release files / cimcb_lite-1.0.2-py3-none-any.whl
| Download URL | cimcb_lite-1.0.2-py3-none-any.whl |
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