Gene expression serves as one of the foundational elements in molecular biology, elucidating the functional state of a cell and providing insights into physiological and pathological processes. Variations in gene expression are most likely indicative of disease states and can be vital in understanding cellular responses, disease progression, and therapeutic susceptibilities. To effectively investigate these expression patterns, especially in large datasets, a robust and precise analysis tool is crucial. Comprehensive analyses of upregulated and downregulated genes can shed light on potential pathways and therapeutic targets. The field of genomics is currently undergoing a transformative phase, with gene expression analysis being a critical determinant of this transformation. The patterns in which genes are expressed can unveil significant insights into disease mechanisms, guide therapeutic responses, and help demystify intricate biological interactions.
We present THRESHOLD: a novel gene saturation analysis GUI. THRESHOLD analyzes transcriptomic data across large samples of patients to understand the cohesion of the most upregulated/downregulated genes in a given disease. This lends researchers knowledge to inform network topology analyses or to assess the relative amount of genes influencing a given disease. THRESHOLD offers several features to aid in analysis including user-inputted saturation type, restriction factors, and rank type. The tool outputs an interactive graph of saturation permitting the calculation of specific saturation thresholds and most saturated genes.
The overarching aim of "THRESHOLD" isn't only about gene identification; it seeks to deepen our understanding of the multifaceted genomic landscape. To that end, it plays a pivotal role in several key domains: (1) Disease Differentiation: Beyond simply classifying diseases according to observable symptoms, "Threshold" offers a molecular perspective, pinpointing unique genetic markers within different patient groups, facilitating a more refined and precise disease categorization. (2) Exploring Genetic Markers: In the quest for new therapeutic targets, "THRESHOLD" emerges as a potentially, spotlighting genes that consistently manifest high expression levels across diverse patients. This is particularly significant when those genes are intrinsically linked to disease pathways. (3) Early Detection: In conditions such as cancer, where prompt diagnosis is crucial, "THRESHOLD" offers valuable assistance. It helps researchers identify genes that are frequently overexpressed, suggesting their possible role as indicators of disease and aiding in the development of early detection methods. Enhancing its analytical capabilities, "THRESHOLD" includes two distinct features: "Incremental Saturation" and "Overall Saturation". The first feature focuses on unraveling the step-by-step changes in gene expression patterns, whereas the second offers a comprehensive view, encompassing the full spectrum of gene expression activity up to a specific threshold.
In summary, the "THRESHOLD" tool serves as a useful addition to genomics research. It provides insights into disease mechanisms and supports developments in personalized medicine, contributing positively to healthcare advancements.
To install THRESHOLD simply click the installation link below.
Open THRESHOLD in the development environment of your choice, although VS Code is recommended.
- To run
THRESHOLD, ensure you haveJava 8+andPython 3.8+installed. Ensure appropriate IDE specific extensions are also installed forJavaandPython. - Next navigate to your
THRESHOLDfolder that you have just downloaded and locate thesetup.shfile. - Right-click the file and click
Get Info. - Copy the file path from the
Wheresection. - Open your terminal and run the following command:
cd directory_path**Replace directory path with the file path copied from step 4.
- Next, run the following set up script to automatically download all required dependencies and compile the cleaning script:
bash setup.sh
- Follow the on-screen prompts. This includes clicking enter and security checks to confirm your device password and provide permissions to install necessary libraries. This includes NumPy, UltraJSON, SciPy, Matplotlib, pandas, and PyQt6.
Once you have provided ample time for this setup process to complete and you have received confirmation of Java file compilation, you are ready to get started.
Once you have downloaded the above folder, open the entire folder in a VScode IDE. Ensure the Python and Java extensions are added. When ready, simply navigate to the threshold.gui file and press run to begin. A window should appear, prompting the input of a file.
THRESHOLD requires the input of a file of patient transcriptomic data with gene expression data in the form of zscores or percentiles comparing expression against a control population, or ranked relatively within an individual patient’s expression.
The inputted .txt file must be formatted as such:
Hugo_Symbol {Blank} {Patient1ID} {Patient2ID}...
... ... zscores/percentile... zscores/percentile...
... ... zscores/percentile... zscores/percentile...
-
The Blank column is never actually used; it is a placeholder. Given space between the Hugo_Symbol and {Patient1ID} column, THRESHOLD will run.
-
Note the first heading MUST be called Hugo_Symbol
-
The columns must be separated by a tab
'\t'
An example file is available in the GitHub repository. It contains a normalized Pancreatic Adenocarcinoma dataset from TCGA (PanCancer Atlas), sourced from cBioPortal. This dataset is an abbreviated version, including only 10 patient samples for demonstration purposes.
To begin the analysis, simply upload a .txt file in the appropriate file format by clicking on the Open File button.
Once the button is clicked, about 5 seconds while the pseudogenes are removed and you will automatically move to the next page.
To view info and documentation of the tool, press the appropriate icons in the GUI toolbar.
Confirm your file is uploaded with the dialog below the Generate button.
Input the parameters according to your desired analyses. Clicking on the ⓘ icons will provide a detailed explanation of the parameter's domains and purpose. Note that regardless of the saturation type selected to be visualized, both data sets can be downloaded together in the succeeding interface. See below for details.
Once satisfied with your inputs, press Generate. If the inputted file is formatted correctly, and the inputs accurate, an In progress... text dialog will appear. For a dataset of about 400 patients, you will need to wait about 2 minutes for the calculations to be made.
If you would like to restart your analyses with a different file, press the restart button in the top right corner.
A saturation curve will appear indicating saturation type by nth ranked gene. See documentation for methods.
Hover over the graph to find specific saturation values. Alternatively, use the Find Threshold input to find when a certain saturation level is reached and the Specific Value input to evaluate the saturation of a specific nth gene. Clicking on the ⓘ icons will provide a detailed explanation of the parameter's domains and purpose.
Additionally, you can find the most saturated genes with the Top x Saturated Genes button. Simply enter the number of top genes you want to find in the x: parameter and press the button.
To export a .png file of the Graph, a .txt file of the saturation Data or a .txt file of removed pseudogenes, press the corresponding buttons above the graph.
To begin a new analysis with the same file, press the back button. If you would like to restart your analysis entirely, press the restart button.
THRESHOLD facilitates the statistical comparison between two saturation data sets to assess whether there are statistically significant differences between samples to provide additional insights in user analyses.
Upload the two saturation files you would like to compare by clicking on the black fields.
Ensure the files are in the proper, standard format (.txt) as exported by the THRESHOLD tool. The files should have three columns, “Nth Gene Included,” “Incremental Saturation,” and “Overall Saturation.” The file should be the same size; ie, the same number of rows.
To run the test, simply press the Run Unpaired-T-Test button.
To navigate back home, simply press restart in the top right corner.
This page simply outlines the result of your analyses, describing whether the differences between the saturation types in each data set yielded statistically significant differences.
This result is outlined in the orange box. Additional relevant statistical measures, including the calculated p-value, are also listed.
To download a more detailed description of the statistical analyses, press “Download Full Report” to export a (.txt) file of relevant calculated statistical measures for each of the saturation types.
To begin a new statistical analysis, press the back button. If you would like to restart entirely and navigate back home, press the back button.
To view the workflow of THRESHOLD in addition to a more in-depth analysis of standardization options and validation of calculations click the link to the .pdf file below.
This project was developed by Finán Gammell, Jennifer Li, Christopher Elco, Jessica Plavicki, and Dr. Alper Uzun in the Uzun Lab at Brown University.
Reach out to alper_uzun@brown.edu with any questions.
The MIT License (MIT)
Copyright (c) 2025 Finán Gammell
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