Skip to main content

A lite version of the cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.

Project description


cimcb lite

cimcb_lite is a lite version of the cimcb package containing the necessary tools for the statistical analysis of untargeted and targeted metabolomics data.



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


For futher detail on the usage refer to the docstring.


  • 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.


  • 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.


  • kfold: Exhaustitive search over param_dict calculating binary metrics.


  • 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.


  • 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.


cimcb_lite is licensed under the MIT license.



Professor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
cimcb_lite-1.0.1-py3-none-any.whl (46.8 kB) Copy SHA256 hash SHA256 Wheel py3
cimcb_lite-1.0.1.tar.gz (33.6 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page