LAUSR

The increasing number of measurable markers and the need to integrate flow cytometry datasets with data generated by other high throughput technologies, require the use of innovative tools, easy enough to be used by people with diverse levels of informatics skills. Flow cytometry analysis software has principally been designed for single sample analysis and it does not cover all the successive analysis steps such as integration with metadata and complex visualization. Here, we illustrated the use of data integration and visualization tools generally used in the business sector to analyze datasets generated by mass and flow cytometry. We selected a study that used mass cytometry to characterize immune cells in lung adenocarcinoma and a second study that used flow cytometry to characterize the expression signature of CD markers on human immune cells. These two examples showed the effectiveness of these tools in the analysis of cytometry data and the possibility to expand their use in any field of biology.