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Functions for statistical analysis of treatment response curves in patient derived xenograph (PDX) models of cancer.

Project description


A Python package for statistical analysis and plotting of Patient Derived Xenographt (PDX) models of cancer.


The PyKuLGaP package provides three major classes associated with treatment response experiments. While the initial implementation of this package was specifically designed for PDX models, we have attempted to make classes general enough to accomodate other cancer models, such as cancer cell lines (CCLs) as well as to allow extension to other kinds of treatment response experiments in Cancer or otherwise.


This class contains all CancerModel objects for a given treatment response experiment. It is the highest level class in PyKuLGaP and stores the other two classes nested within it. This class provides a number of features to easily compute statistics aggregated over all CancerModels for each of the ExperimentalConditions, allowing a high level interface for summarizing the results of a given treatment response experiment, be that in PDX models, CLLs or other cancer model systems.


  • model_names: [list] The names of the CancerModel object contained within the object.
  • cancer_models: [list] The list of CancerModel object contained within the object.
    • Note: A TreatmentResponseExperiment (TRE) is iterable and returns a tuple of the model name and model object for each CancerModel in the object.
  • summary_stats_df: [DataFrame] Table containing summary statistics computed for all CancerModels in the TRE. Computes the statistics if they don't exist already.


  • experimental_condition_names: [list] Returns a list of names for all unique TreatmentConditon within the object.
  • to_dict: [dict] Returns the object as a dictionary
  • compute_all_statistics: [None] Computes all summary statistics and assigns them as a DataFrame to the summary_stats_df attribute.


  • Single Index Subsetting:
    • treatment_response_experiment['<cancer model name>'] returns the named CancerModel
      • e.g., treatment_response_experiment["P1"] returns the CancerModel assocaited with Patient 1.
    • treatment_response_experiment[<cancer model index>] also returns the CancerModel at that index
      • e.g., treatment_response_experiment[1] returns the CancerModel in the first index, in this case still Patient 1.
  • Multiple Index Subsetting:
    • treatment_response_experiment[[<cancer model 1>, <cancer model 2>, ..., ]]
      • e.g., treatment_response_experiment[["P1", "P2", "P3"]] returns a list of CancerModel objects.
  • Chained Subsetting:
    • treatment_response_experiment[][] returns the named ExperimentalCondition object from the name CancerModel.
    • treatment_response_experiment[<cancer_model_name>][][] returns the dose and response data for the specified replicate within the specified ExperimentalCondition and CancerModel

CancerModel Class

A CancerModel represents one or more samples with the same source. For example, in PDX models it would represent all tumour growth measurements for mice derived from a single patient. In cancer cell line (CCL) models it would represent all cellular viability measurements for cultures grown with a single cancer cell line.

ExperimentalCondition Class

The ExperimentalCondition class stores treatment response data for an experimental condition within a CancerModel. It stores all replicates for all variables of the experimental condition for a given cancer model system.

For example, in CancerModel Derived Xenograph (PDX) experiments it would store the tumour size measurements at each exposure time for all mouse models derived from a single patient.

In cancer cell lines (CCLs) it would store all viability measurements for each dose level for all cultures derived from a single cancer cell line and treated with a specific compound.

Thus the ExperimentalCondition class can be though of a storing data response data for a cancer model in two dimensions: replicates (e.g., a specific mouse or culture) variable condition levels (e.g., a specific time or dose).

Common experimental conditions:

  • Control, i.e. no treatment
  • Exposure to a specific drug or compound
  • Treatment with a specific type of ionizing radiation

It can have multiple replicates (ie. data for multiple growth curves)

Additional Features

This documentation is a work in progress, we will expand it further over the coming months.

In the mean time feel free to contact for questions/troubleshooting.

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