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This is a pre-release.

Project description

drawing

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:

Binder

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

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: ___

Project details


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