The Cancer Genome Atlas data analysis simplified
An interactive interface for examining cancer gene expression data: The Cancer Genome Explorer (TCGEx).
TCGEx allows performing customized analyses in 33 different cancer types
These include survival modeling, principal component analysis, exploratory graphing, gene set enrichment analysis, and machine learning.
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Principal Component Analysis (PCA) Module
Reduce dimensionality of TCGA gene expression data and visualize complex datasets with flexible inputs.
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Receiver Operating Characteristic Curve (ROC) Analysis Module
Evaluate the predictive power of genetic features and biomarkers.
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Heatmap Module
Visualize multi-dimensional gene expression data and perform hierarchical clustering to identify meaningful subsets in the data
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Gene Set Enrichment Analysis (GSEA) Module
Perform GSEA in custom patient subsets to test the enrichment of genes with known functional associations
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Machine Learning Analysis Module
Identify the strongest predictors for a custom outcome variable using linear regression-based ML algorithms (Lasso, Ridge and Elastic-Net).
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Kaplan-Meier Analysis Module
Examine the effect of categorized gene expression and other metadata features on survival.
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Cox Proportional Hazards Survival Analysis Module
Model the effect of multiple factors on survival simultaneously and calculate hazard ratios
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Correlated Gene Module
Visualize transcript levels in pre-defined data subsets and perform statistical comparisons
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TCGExplorer.
High-throughput cancer data analyzer
We created TCGEx to enable wider use of TCGA data and facilitate researchers with no coding background to perform comprehensive analyses. TCGEx provides a responsive and user-friendly interface with customizable parameters that allows tailoring the analyses to different scientific contexts. This open-source tool written in R/Shiny offers a point-and-click solution for perfoming complex analyses of human cancer data and it can generate publication-ready plots to accelerate cancer research.
Intersection of disciplines 
Making sense of high-throughput cancer data requires a harmonious integration of molecular biology, statistics, and computer science. These advanced fields arguably differentiated from each other so much that it became necessary to establish new bridges between them to accelarate research.Read more…
We developed TCGEx with this vision in our minds. Based on our prior expertise and the cancer research literature, we identified most commonly utilized analytical methods and packaged them into user-friendly modules with flexible user-selected parameters. This way, users can customize the analyses to their specific needs and utilize TCGEx in a variety of research contexts. We realize that learning software tools can be challenging, especially for researchers who have not dealt with high-throughput data previously. To ease this pain, we developed step-by-step guides in each module that introduce the necessary knobs and buttons to users to help getting started. Importantly, interactive analysis modules can be conveniently adapted to a range of study contexts leading to publication-ready plots in a few seconds. The source code for TCGEx can be found in GitHub
Who is behind TCGEx?
TCGEx was developed by the tremendous graduate and undergraduate students of the Ekiz Lab in the Department of Molecular Biology and Genetics at Izmir Institute of Technology, Turkey.Read more about us…
Cite Us
The preprint describing TCGEx can be found on BioRxiv:
Kus M.E, Sahin C, Kilic E, Askin A, Ozgur M.M, Karahanogulları G, Aksit A, O’Connell R.M, Ekiz H.A. TCGEx: A visual interface for multifaceted analyses of The Cancer Genome Atlas gene expression data. bioRxiv 2023.08.14.553075; doi: https://doi.org/10.1101/2023.08.14.553075TCGEx: A visual interface for multifaceted analyses of The Cancer Genome Atlas gene expression data
Please Select Cancer Project(s)
Upload your own dataset
Normalize data
Filter data
Kaplan-Meier Survival Analysis
Cox Proportional Hazards Survival Analysis
Boxplot Metadata Analysis
Graphing options
Selected pairwise comparisons
Scatterplot Correlation Analysis
Correlated Gene Table Analysis
Heatmap Analysis
Gene Sets Enrichment Analysis (GSEA)
Receiver Operating Characteristic (ROC) Analysis
Principal Compenent Analysis (PCA)
Response variable(s)
Predictor variables
Plot selected coefficient(s)
The team behind TCGEx
H. Atakan Ekiz
Dr. Atakan Ekiz is the group leader of Tumor Immunology and Bioinformatics Lab at IzTech. He is fascinated by the mechanisms by which the immune system interacts with cancer. His studies focus on understanding the roles of noncoding RNAs in tumor immunity and developing user-friendly analysis interfaces to facilitate biological data analysis. When he is not doing science, he can be found playing guitar or piano, and dancing salsa or Argentine Tango.
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Muhammet Emre Kuş
Emre is a Ph.D. student at Ekiz Lab at IzTech. Emre investigates the role of microRNAs in cancer and develops user-friendly interfaces that enable an in-depth analysis of high-throughput data. Emre is a former football player and presently serves as the head of the IzTech football community. He also leads a group that organizes awareness and entertainment events. In his spare time, he assumes the identity of a wanderlust who likes to travel the world and discover new places.
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Emre Kılıç
A multi-disciplinary undergraduate scientist who is passionate to expand his horizons in different areas. Emre likes adapting his expertise to different contexts and produce effective products for scientific community around the world. Drinking coffee and dancing are his essential requirements. He is responsible for code shiny-fication and modularization.
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Çağatay Şahin
Highly driven, curious, mission-oriented, and big-data lover undergrad who enjoys being a part of a brilliant team. Within the team, he working on building the shiny modules
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Arda Aşkın
Arda is a molecular biology undergraduate at IzTech. He loves exploring new areas in natural and computational sciences. In his free time, he likes going to theater and riding his bike.
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Mustafa Mert Özgür
Mert is a senior student at Bilkent University in the Department of Molecular Biology and Genetics. He is primarily interested in cancer immunology and related computational workflow that combines power of mathematics and bioinformatics. Determined and enthusiastic researcher who enjoys photography, playing guitar, dance, and traveling.
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Gökhan Karahanoğulları
Gökhan is an undergraduate student in Mathematics Department at IzTech. With his inquisitive personality, he taught himself programming at the age of eleven. Nowadays, this curiosity has turned to investigate natural sciences with the power of mathematics. He likes dealing with digital design. In his spare time, he takes walks in nature and plays with his cats.
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Ahmet Akşit
Ahmet started his informatics career in 1989 with computer education in technical high school. His motto; "to develop and disseminate free and open source software."
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You Can Cite Us ↓
Kus M.E, Sahin C, Kilic E, Askin A, Ozgur M.M, Karahanogulları G, Aksit A, O’Connell R.M, Ekiz H.A. TCGEx: A visual interface for multifaceted analyses of The Cancer Genome Atlas gene expression data. bioRxiv 2023.08.14.553075; doi: https://doi.org/10.1101/2023.08.14.553075
bioRxiv