This repository is home of the framework TMEA (Thermodynamically Motivated Enrichment Analysis), which we created from the scripts we used in our 2020 Entropy paper

Find the authors on github: Kevin Schneider (1), Benedikt Venn (1), Timo Mühlhaus

• (1) : These authors contributed equally.

If you use this package in your research, please cite it. Citation formats are available at the original article page

alternatively, here is an example citation:

Schneider K, Venn B, Mühlhaus T. TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation. Entropy. 2020; 22(9):1030.

This package is in an early beta stage, there may be bugs. Issues and PRs are greatly appreciated!

• Introduction
• Installation
• Usage
• Plots
  • Functionally Annotated Set (FAS) weight distributions
  • Potential Time Course:
  • Free Energy Landscape:
  • Constraint importance:
  • Data recovery:
• TMEA.Dash
  • Usage
• License acknowlegments

Introduction

The objective of gene set enrichment analysis (GSEA) in modern biological studies is to identify functional profiles in huge sets of biomolecules generated by high throughput measurements of genes, transcripts, metabolites, and proteins. GSEA is based on a two-stage process using classical statistical analysis to score the input data and subsequent testing for overrepresentation of the enrichment score within a given functional coherent set. However, enrichment scores computed by different methods are merely statistically motivated and often elusive to direct biological interpretation.

Here, we propose a novel approach, called Thermodynamically Motivated Enrichment Analysis (TMEA), to account for the energy investment in biological relevant processes. Therefore, TMEA is based on surprisal analysis, that offers a thermodynamic-free energy-based representation of the biological steady state and of the biological change. The contribution of each biomolecule underlying the changes in free energy is used in a Monte Carlo resampling procedure resulting in a functional characterization directly coupled to the thermodynamic characterization of biological responses to system perturbations.

Installation

For instructions on how to install F#, please head here(Windows) , here(MAC) or here(Linux)

the package itself is available on nuget: https://www.nuget.org/packages/TMEA

alternatively, clone this repo and run fake.cmd or fake.sh (requires dotnet sdk >= 3.1.302)

Usage

1. Include the lapack folder to your PATH variable, either for the fsi session or on your systems level. The folder is located in the nuget package under ./Netlib_LAPACK

2. Reference this library and its dependencies.

3. We strongly recommend to register fsi printers for Deedle, the dataframe library we use in this project. There is a Deedle.fsx file located in the Deedle nuget package which will take care of that if you #load it.

4. A simple pipeline to perform TMEA on time series data looks like this:

   ```F# open TMEA open TMEA.SurprisalAnalysis open TMEA.MonteCarlo open TMEA.Frames open TMEA.Plots

   let annotationMap : Map = ... // We assume you have ontology annotations for your dataset

   let tmeaRes = IO.readDataFrame "TranscriptIdentifier" // The column of the data table that contains your entity identifiers "\t" // separator for the input file "path/to/your/raw/data.txt" |> Analysis.computeOfDataFrame Analysis.standardTMEAParameters //using custom parameters you can change verbosity, bootstrap iterations, and the annotation used for unannotated entities annotationMap ```

Plots

All plot functions have a generate* analog, which generates the Chart object without rendering it (in case you want to fine tune styles etc.). Currently, the following plots are provided by the package:

All charting functions are extension methods of the TMEAResult type. Given the example script above, you can visualize the results as:

Functionally Annotated Set (FAS) weight distributions

• plotFASWeightDistribution is an exploratory plot that visualizes the overall weight distributions of the given TMEA Characterizations, and adds detailed weight distributions of the FAS of interest on top of that. additionally, annotations on the respective subplots show useful information about the FAS characterization.

  F# tmeaRes |> TMEAResult.plotFASWeightDistribution true //use style presets 0.05 //significance threshold for (corrected!) p values [1;2;3] //constraints to plot "signalling.light" //name of the FAS

  

Potential Time Course:

• plotConstraintTimecourses plots the constraint potential time courses of the given TMEA result:

  F# tmeaRes |> TMEAResult.plotConstraintTimecourses true //true -> will use style presets

  

• plotPotentialHeatmap is a more visually pleasing version of above plot (it omits the baseline state per default):

  F# tmeaRes |> TMEAResult.plotPotentialHeatmap true

  

Free Energy Landscape:

• plotFreeEnergyLandscape plots the free energy landscape of the TMEA result:

  tmeaRes |> TMEAResult.plotFreeEnergyLandscape true

  

Constraint importance:

• plotConstraintImportance: given the TMEA result, plots the singular values of all constraints (except the baseline state) and the 'importance loss' between them.

  tmeaRes |> TMEAResult.plotConstraintImportance true

  

Data recovery:

• plotDataRecovery: given the TMEA result, plots the gradual reconstruction of the original data when using only n (in the example below, n = 3) constraints from the given TMEA result:

  tmeaRes |> TMEAResult.plotDataRecovery true 3

  

TMEA.Dash

TMEA.Dash is a guided analytics application for TMEA using Dash.NET.

Usage

• Clone this repository

• install dotnet sdk >= 3.1.302

• in a shell, navigate to src/TMEA.Dash

• use dotnet run to start the application. Open a browser and head to https://localhost:5001/

• you should see the following interface:

License acknowlegments

This library contains Netlib LAPACK binaries compiled from source, thanks to all the authors of it:

Anderson, E. and Bai, Z. and Bischof, C. and Blackford, S. and Demmel, J. and Dongarra, J. and
Du Croz, J. and Greenbaum, A. and Hammarling, S. and McKenney, A. and Sorensen, D.