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
The recommend way to install cimcb_lite and dependencies is to using conda:
conda install -c cimcb cimcb_liteor pip:
pip install cimcb_liteAlternatively, to install directly from github:
pip install https://github.com/cimcb/cimcb_lite/archive/master.zipFor 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.
E-mail: d.broadhurst@ecu.edu.au