CoTRA: an integrated R/Shiny framework for transparent bulk and single-cell RNA-seq analysis
CoTRA is an open-source, modular R/Shiny framework that unifies transparent and reproducible workflows for both bulk and single-cell RNA-seq analysis, offering extensive functionality, cross-platform compatibility, and validated performance comparable to or exceeding existing tools.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer
In the modern study of life, scientists have developed a powerful way to read the instructions inside our cells. These instructions, written in a molecule called RNA, act as a daily logbook, recording which genes are active and which are quiet at any given moment. By reading these logs, researchers can see how a healthy body functions and how disease changes the conversation between cells. For years, scientists have been able to read these logs from entire tissues, a method that gives a broad overview of what is happening. More recently, they have learned to read the logs from individual cells, revealing a much more detailed picture where rare cell types and subtle changes can be spotted. However, turning these raw logs into a clear story has become a difficult task. The data is massive, and making sense of it usually requires a researcher to jump between many different computer programs, each with its own rules and language. This fragmentation makes it hard to keep track of the steps taken, to share the work with others, or to repeat the analysis to be sure the results are real.
To solve this problem, a team of researchers has built a new digital workshop called CoTRA. This tool is designed to bring the entire process of reading and interpreting these cellular logs into a single, easy-to-use window on a computer screen. Instead of forcing scientists to write complex computer code or switch between separate software packages, CoTRA offers a visual interface where users can upload their data, adjust the settings, and watch the analysis unfold. The system handles two main types of data: the broad logs from whole tissues and the detailed logs from individual cells. It guides the user through checking the quality of the data, finding the differences between healthy and sick samples, and identifying the biological pathways that are turning on or off. Crucially, the tool does not hide the decisions behind the scenes. It lets the user see and change the specific rules used for the analysis, ensuring that the process remains transparent and that the results can be trusted.
The researchers tested this new system to see if it could produce reliable results. They started with a dataset from mice that had a form of retinal degeneration, a condition where the light-sensing cells in the eye slowly die. Using CoTRA, they analyzed the logs from the eyes of these sick mice and compared them to the eyes of healthy mice. The system successfully separated the two groups, showing a clear difference in their genetic activity. It identified nearly two thousand genes that were behaving differently in the diseased eyes, with the results matching almost perfectly with a previous, highly respected study that had used a different, more complex method. The tool also correctly highlighted that the sick eyes showed signs of increased immune activity and a loss of the genes responsible for vision, confirming that the software could accurately reproduce established scientific findings.
The team then pushed the tool further by testing it on a single-cell dataset from the same type of mouse eyes. This is a much more complex job, as it involves sorting thousands of individual cells into their specific types, such as light-sensing rods, color-sensing cones, and support cells. CoTRA managed to group the cells into sixteen distinct clusters and correctly identified the major cell types present in the retina. Once the cells were sorted, the software allowed the researchers to look inside specific groups to see how they changed during the disease. They found that the support cells in the sick eyes showed a strong reaction to stress and inflammation, while the light-sensing cells showed signs of damage. The system also calculated the activity of various biological pathways, showing exactly which internal processes were most affected in each cell type. This demonstrated that the tool could handle the intricate work of single-cell analysis without losing the ability to provide clear, interpretable answers.
Beyond just finding results, the researchers designed CoTRA to protect the privacy and ownership of the data. Unlike many online tools that require scientists to upload their sensitive data to a central server, this software can run entirely on a researcher's own computer or within their own institution's secure network. This means that data from clinical trials or unpublished studies never has to leave the researcher's control. The system also generates a complete record of the analysis, including the specific settings used and the final outputs, making it easy to share the work with colleagues or to repeat the study later to verify the findings. By combining the power of advanced statistical methods with a simple, visual interface, CoTRA offers a way to make complex genetic analysis accessible to more scientists, ensuring that the journey from raw data to biological discovery remains clear, reproducible, and under the control of the researcher.
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