DunnIndex

Dunnindex is a statistical measure used in biomedical research to quantify the difference between two or more cell populations. Developed by Joseph L. Dunn and colleagues, it is designed to provide a sensitive and robust method for analyzing data from high-throughput experiments, such as gene expression arrays and flow cytometry.

The Dunnindex is calculated as the proportion of cells that are in the highest expression group, or the percentage of cells that are in the most variable group, compared to the entire population. It is often used to compare the differences between control and treated groups, or between different treatment conditions. The Dunnindex is a non-parametric test, which means it does not require the data to be normally distributed or to follow any specific underlying distribution.

A high Dunnindex score indicates that the cell population is highly heterogeneous, or that there are significant differences between the cells. Conversely, a low Dunnindex score suggests that the cells are more uniform, or that there are few differences between them. The Dunnindex is widely used in the analysis of single-cell RNA sequencing (scRNA-seq) data, which can provide insights into the expression of genes across a large number of individual cells.

Compared to other methods, such as the Gini index, the Dunnindex is considered to be more robust and sensitive to subtle changes in the data. However, it is still a relatively new metric, and its limitations and potential biases are being explored. As such, the Dunnindex is best used in conjunction with other analysis methods and validated results to fully understand the underlying biology of the system being studied.