Table of contents


EvAM-Tools is an R package and Shiny web app that provides tools for evolutionary accumulation, or event accumulation, models. We use code from “Cancer Progression Models” (CPM) but these are not limited to cancer (the key idea is that events are gained one by one, but not lost). EvAM-Tools is also available as an R package (see https://github.com/rdiaz02/EvAM-Tools).

This web interface provides a GUI to the package and focuses on allowing fast construction, manipulation, and exploration of CPM models, and making it easy to gain an intuitive understanding of what these methods infer from different data sets as well as what kind of data are to be expected under these models. You can analyze your data, create cross-sectional data from scratch (by giving genotype frequencies), or generate synthetic data under different CPMs. You can compare results from different methods/models, as well as experiment and understand the consequences of changes in the input data on the returned inferences. You can also examine how a given method performs when data have been generated under another (or its own) model. Additional examples of use are discussed in https://github.com/rdiaz02/EvAM-Tools#some-examples-of-use and in the Additional documentation.


A two-paragraph summary about cross-sectional data and CPMs

In cross-sectional data a single sample is obtained from each subject or patient. That single sample represents the “observed genotype” of, for example, the tumor of that patient. Genotype can refer to single point mutations, insertions, deletions, or any other genetic modification. In this app, as is often done by CPM software, we store cross-sectional data in a matrix, where rows are patients or subjects, and columns are genes; the data is a 1 if the event was observed and 0 if it was not.

Cancer progression models (CPMs) or, more generally, event accumulation models, use these cross-sectional data to try to infer restrictions in the order of accumulation of events; for example, that a mutation on gene B is always preceded by a mutation in gene A (maybe because mutating B when A is not mutated). Some cancer progression models, such as MHN, instead of modeling deterministic restrictions, model facilitating/inhibiting interactions between genes, for example that having a mutation in gene A makes it very likely to gain a mutation in gene B. A longer explanation is provided in What CPMs are included in EvAM-Tools?, below, and many more details in the Additional documentation. Finally, note we have talked about “genotype” and “mutation”, but CPMs have been used with non-genetic data too, and thus our preference for the expression “event accumulation models”; as said above, the key idea is that events are gained one by one, but not lost, and that we can consider the different subjects/patients in the cross-sectional data as replicate evolutionary experiments or runs where all individuals are under the same constraints (e.g., genetic constraints if we are dealing with mutations).


How to use this web interface?

Web app: overview of workflow and use cases

The figure below provides an overview of the workflow with the web app:

Overview EvAM-Tools web app

The web app encompasses, thus, different major functionalities and use cases, mainly:

  1. Inference of CPMs from user data uploaded from a file.

  2. Exploration of the inferences that different CPM methods yield from manually constructed synthetic data.

  3. Construction of CPM models (DAGs with their rates/probabilities and MHN models) and simulation of synthetic data from them.

    3.1. Examination of the consequences of different CPM models and their parameters on the simulated data.

    3.2. Analysis of the data simulated under one model with methods that have different models (e.g., data simulated from CBN analyzed with OT and OncoBN).

    3.3. Analysis of the data simulated under one model after manual modification of specific genotype frequencies (e.g., data simulated under CBN but where, prior to analysis, we remove all observations with the WT genotype and the genotype with all loci mutated).

Furthermore, note that in all cases, when data are analyzed, in addition to returning the fitted models, the web app also returns the analysis of the CPMs in terms of their predictions such as predicted genotype frequencies and transition probabilities between genotypes.

The figure below highlights the different major functionalities and use cases, as numbered above, over-imposed on the previous figure: