Single-cell analysis has become an essential method for studying the individual cells that make up tissues and organs. Unlike traditional methods, which analyze groups of cells, single-cell technologies allow researchers to examine the unique characteristics of each cell. This detailed approach helps to uncover the complexity of biological systems, like how different types of cells behave or how they respond to treatments. However, analyzing these vast amounts of data can be challenging, especially when it comes to comparing different ways of grouping or classifying cells.

What is tagtango?

tagtango is a new software tool developed at the Lausanne University Hospital designed to help researchers compare and analyze the results of different methods used to classify single cells into groups or clusters. It’s an R package and web application that simplifies this process, allowing users to easily explore and visualize how different clustering and annotation techniques group cells. By providing a platform that brings together single-cell data analysis and interactive visualizations, tagtango helps scientists see patterns and differences in their data that might not be apparent with traditional analysis methods.

Overview of the annotation comparison performed by tagtango

Panel (a) displays a Sankey diagram comparing the annotations performed by CITE-sort and Azimuth’s level 2 (i.e. ‘celltype.l2’). The diagram was filtered using tagtango to only include cells annotated as ‘CD4+ T-cells’ by Azimuth’s main cell type classification (i.e. ‘celltype.l1’) and links containing at least 20 cells. The coloured links in the diagram indicate the cell populations selected for deeper analysis. Panels (b) and (f) display rose plots of the normalized expression for the genes and protein markers deemed most relevant in the selected cell population ‘central memory CD4+ T cell’. Panels (c) and (g) display the same for the selected cell population ‘CD45RO- naive CD4+ T cell’. Panels (d) and (h) display a direct comparison between the ADT and RNA marker normalized expression for the two selected cell populations, including only those relevant markers. The colours of the bars match those of the selected links in panel (a). Panels (e) and (i) present the UMAP representation of all cells calculated using all markers for the ADT and RNA expression data, respectively. The colours of the points match those of the selected links in panel (a).

Why is tagtango important?

In biological research, especially in fields like genomics and immunology, understanding the biological differences between various cell types is crucial. When single-cell data is collected, researchers often use algorithms to sort cells into clusters based on similarities in their gene expression or other features. However, there are many different ways to perform this classification, and different methods may lead to different results. Some methods may group cells in ways that highlight certain biological aspects, while others may focus on different features.

tagtango makes it easier to compare how these different methods group cells, helping researchers identify which method provides the most useful insights for their specific research question. With its user-friendly interface, tagtango allows scientists to see and understand these differences without having to dive deep into complex programming or data analysis techniques.

How does tagtango work?

tagtango allows users to upload their single-cell data and then compare how different clustering and annotation methods classify the cells. The platform offers interactive tools that let users visualize the differences between methods and explore how well each one explains the biological variation in their data. For example, it can show how different methods might group cells based on gene expression patterns or other markers, allowing researchers to see which method best aligns with their biological hypotheses.

Why should researchers use tagtango?

• Easy-to-use: tagtango is designed to be accessible even to researchers who may not be experts in programming or bioinformatics. Its intuitive interface makes it easy for users to interact with complex single-cell data.
• Cross-platform: It works on various operating systems, making it portable and flexible for different research environments.
• Improved analysis: By offering an interactive and comparative platform, tagtango helps researchers make more informed decisions about which clustering methods to use for their studies, potentially improving the accuracy and relevance of their findings.

Conclusion

As the field of single-cell research continues to grow, tools like tagtango provide critical support for understanding the complexity of individual cells. By making it easier to compare and visualize different methods of clustering and annotation, this tool can help researchers make better sense of their data, uncover new biological insights, and ultimately contribute to the advancement of medical and scientific knowledge. Whether you’re studying cancer, neuroscience, or developmental biology, tagtango could be a game-changer in how you explore the data that shapes our understanding of life at the cellular level.

Availability – tagtango is freely available at https://github.com/bernibra/tagtango as an R package as well as an online web service at https://tagtango.unil.ch.