cimcb 1.0.1

This is a pre-release.

<img src=”cimcb_logo.png” alt=”drawing” width=”400”/>

# 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: `console conda install -c cimcb cimcb ` or pip: `console pip install cimcb ` Alternatively, to install directly from github: `console pip install https://github.com/KevinMMendez/cimcb/archive/master.zip `

### Tutorial Open with Binders:

[](https://mybinder.org/v2/gh/KevinMMendez/BinderTutorial_Workflow/master?filepath=BinderTutorial_Workflow.ipynb)

### API For futher detail on the usage refer to the docstring.

#### cimcb.model - [PLS_SIMPLS](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PLS_SIMPLS.py#L14-L36): Partial least-squares regression using the SIMPLS algorithm. - [PCR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCR.py#L8-L29): Principal component regression. - [PCLR](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/PCLR.py#L8-L29): Principal component logistic regression. - [RF](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RF.py#L8-L9): Random forest. - [SVM](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/SVM.py#L8-L9): Support Vector Machine. - [RBF_NN](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/RBF_NN.py#L8-L9): Radial basis function neural network. - [NN_LinearLinear](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLinear.py#L7-L8): 2 Layer linear-linear neural network. - [NN_LinearLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LinearLogit.py#L7-L8): 2 Layer linear-logistic neural network. - [NN_LogitLogit](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/model/NN_LogitLogit.py#L7-L8): 2 Layer logistic-logistic neural network.

#### cimcb.plot - [boxplot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/boxplot.py#L8-L18): Creates a boxplot using Bokeh. - [distribution](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/distribution.py#L6-L16): Creates a distribution plot using Bokeh. - [pca](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/pca.py#L10-L17): Creates a PCA scores and loadings plot using Bokeh. - [permutation_test](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/permutation_test.py#L13-L27): Creates permutation test plots using Bokeh. - [roc_plot](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/roc.py#L11-L24): Creates a rocplot using Bokeh. - [scatter](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatter.py#L6-L16): Creates a scatterplot using Bokeh. - [scatterCI](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/plot/scatterCI.py#L7-L14): Creates a scatterCI plot using Bokeh.

#### cimcb.cross_val - [kfold](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/cross_val/kfold.py#L14-L42): Exhaustitive search over param_dict calculating binary metrics.

#### cimcb.bootstrap - [Perc](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/Perc.py#L6-L35): Returns bootstrap confidence intervals using the percentile boostrap interval. - [BC](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BC.py#L8-L37): Returns bootstrap confidence intervals using the bias-corrected boostrap interval. - [BCA](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/bootstrap/BCA.py#L8-L36): Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.

#### cimcb.utils - [binary_metrics](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/binary_metrics.py#L5-L23): Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score. - [ci95_ellipse](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/ci95_ellipse.py#L6-L28): Construct a 95% confidence ellipse using PCA. - [knnimpute](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/knnimpute.py#L7-L22): kNN missing value imputation using Euclidean distance. - [load_dataXL](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/load_dataXL.py#L7-L29): Loads and validates the DataFile and PeakFile from an excel file. - [nested_getattr](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/nested_getattr.py#L4-L5): getattr for nested attributes. - [scale](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/scale.py#L4-L42): Scales x (which can include nans) with method: ‘auto’, ‘pareto’, ‘vast’, or ‘level’. - [table_check](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/table_check.py#L4-L17): Error checking for DataTable and PeakTable (used in load_dataXL). - [univariate_2class](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/univariate_2class.py#L8-L35): Creates a table of univariate statistics (2 class). - [wmean](https://github.com/KevinMMendez/cimcb/blob/master/cimcb/utils/wmean.py#L4-L19): Returns Weighted Mean. Ignores NaNs and handles infinite weights.

### License cimcb is licensed under the ___ license.

### Authors - Kevin Mendez - [David Broadhurst](https://scholar.google.ca/citations?user=M3_zZwUAAAAJ&hl=en)

### Correspondence Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University. E-mail: [e-mail]

### Citation If you would cite cimcb in a scientific publication, you can use the following: ___