Git Product home page Git Product logo

cimcb's Introduction

drawing

cimcb

A 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)
  • Pandas
  • 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/cimcb/cimcb/archive/master.zip

API

For futher detail on the usage refer to the docstring.

cimcb.model

  • PLS_SIMPLS: Partial least-squares regression using the SIMPLS algorithm.
  • PLS_NIPALS: Partial least-squares regression using the NIPALS algorithm.
  • PCR: Principal component regression.
  • PCLR: Principal component logistic regression.
  • RF: Random forest.
  • SVM: Support vector machine.
  • NN_LinearLinear: 2 Layer linear-linear neural network.
  • NN_LinearSigmoid: 2 Layer linear-sigmoid neural network.
  • NN_SigmoidSigmoid: 2 Layer sigmoid-sigmoid 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 using k-fold cross validation.
  • holdout: Exhaustitive search over param_dict calculating binary metrics using hold-out set.

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.
  • dict_mean: Calculate mean for all keys in dictionary.
  • dict_median: Calculate median for all keys in dictionary.
  • dict_perc: Calculate confidence intervals (percentile) for all keys in dictionary.
  • dict_std: Calculate std for all keys in dictionary.
  • knnimpute: kNN missing value imputation using Euclidean distance.
  • load_dataCSV: Loads and validates the DataFile and PeakFile from CSV files.
  • load_dataXL: Loads and validates the DataFile and PeakFile from a 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 MIT license.

Authors

Correspondence

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

cimcb's People

Contributors

bottybotz avatar kevinmmendez avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.