cimcb
cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.
Installation
Dependencies
cimcb requires:
- Python (>=3.5)
- Bokeh (>=1.0.0)
- Keras
- NumPy (>=1.12)
- SciPy
- scikit-learn
- Statsmodels
- TensorFlow
- tqdm
User installation
The recommend way to install cimcb and dependencies is to using conda:
conda install -c cimcb cimcb
or pip:
pip install cimcb
Alternatively, to install directly from github:
pip install https://github.com/KevinMMendez/cimcb/archive/master.zip
Tutorial
Open with Binders:
API
For futher detail on the usage refer to the docstring.
cimcb.model
- PLS_SIMPLS: Partial least-squares regression using the SIMPLS algorithm.
- PCR: Principal component regression.
- PCLR: Principal component logistic regression.
- RF: Random forest.
- SVM: Support Vector Machine.
- RBF_NN: Radial basis function neural network.
- NN_LinearLinear: 2 Layer linear-linear neural network.
- NN_LinearLogit: 2 Layer linear-logistic neural network.
- NN_LogitLogit: 2 Layer logistic-logistic neural network.
cimcb.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.cross_val
- kfold: Exhaustitive search over param_dict calculating binary metrics.
cimcb.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.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 is licensed under the ___ license.
Authors
- Kevin Mendez
- David Broadhurst
Correspondence
Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University. E-mail: d.broadhurst@ecu.edu.au
Citation
If you would cite cimcb in a scientific publication, you can use the following: ___
1 maintainer
Metadata
1 maintainer
Release files for cimcb 1.1.0
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-1.1.0.tar.gz | 80.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cimcb-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 230.3 kB
Release files / cimcb-1.1.0.tar.gz
| Download URL | cimcb-1.1.0.tar.gz |
|---|---|
| Size | 80.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / cimcb-1.1.0-py3-none-any.whl
| Download URL | cimcb-1.1.0-py3-none-any.whl |
|---|---|
| Size | 150.2 kB |
| Tags | Python 3 |
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twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3
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