SCOUTS

Single-Cell Outlier Selector

SCOUTS is a tool that quickly finds OUTLIERS in single-cell datasets, especially those obtained by mass cytometry (CyToF) and single-cell RNA sequencing (scRNA-Seq) platforms.

Why OUTLIERS?

Outliers are often ignored or even removed from data analysis. In cancer, however, single outlier cells can be of major importance, since they have uncommon characteristics that may confer them tolerance or resistance to therapy.

Why SCOUTS?

Many single-cell analysis pipelines require some level of programming knowledge in order to be used. While some great tools for languages like R, Python and Julia have been developed, the entry-level barrier of programming is still intimidating for many scientists starting on the field of single-cell analysis. With this in mind, we developed SCOUTS to simplify this process. Through a Desktop application, the user is able to choose the parameters for the outlier selection, and leave the hard work of programmatically selecting and subsetting the data to SCOUTS.

As a showcase of how to interpret and explore the data generated by SCOUTS, we also developed SCOUTS-violins, a secondary desktop application which displays the outlier populations identified by SCOUTS as violin plots.

SCOUTS in a nutshell

SCOUTS selects outliers by a labelling method, calculating a cutoff value based on the concept of Tukey's fences. Users can choose whether to select outliers by calculating the cutoff value for each population, or to select a reference sample whose cutoff is then applied for all samples. Additionally, SCOUTS can separately select cells that are outliers for each individual parameter (i.e. each gene or protein), or all cells that are outliers for at least one parameter. Other possibilities include selecting outliers on the bottom of the population distribution (low-expression outliers), or selecting the population of cells that are not outliers.

Refer to these links to download and learn more about SCOUTS:

We also distribute SCOUTS as a binary executable (experimental):




If you have any questions or comments, feel free to contact us or open an issue on GitHub.