SCOUTS
Single-Cell Outlier Selector
SCOUTS is a tool designed to quickly identify outliers in single-cell datasets, particularly datasets generated by mass cytometry (CyTOF) and single-cell RNA sequencing (scRNA-Seq) platforms.
SCOUTS provides an accessible way to explore uncommon cellular populations without requiring extensive programming knowledge.
Single-Cell Outlier Selector
Why OUTLIERS?
Outliers are often ignored or removed during data analysis. In cancer, however, individual outlier cells may be highly relevant because they can display uncommon characteristics associated with tolerance or resistance to therapy.
Why SCOUTS?
Many single-cell analysis pipelines require some level of programming knowledge. Although powerful tools are available for languages such as R, Python and Julia, the programming barrier can still be challenging for researchers entering the field.
SCOUTS simplifies this process through a desktop application where users can select parameters for outlier detection while the software performs the data selection and subsetting.
SCOUTS in a nutshell
SCOUTS selects outliers using a labelling method that calculates cutoff values based on the concept of Tukey's fences .
Users can calculate cutoff values independently for each population or select a reference sample whose cutoff is applied to all samples.
SCOUTS can identify cells that are outliers for individual parameters, cells that are outliers for at least one parameter, low-expression outliers, or populations that do not contain outliers.
Overview of the SCOUTS outlier selection workflow.
Documentation & Tutorials
Software & Source Code
Example of data visualization generated from SCOUTS results.
Exploring SCOUTS results
To facilitate the interpretation of the populations identified by SCOUTS, we also developed SCOUTS-violins.
This secondary desktop application allows users to visualize outlier populations identified by SCOUTS through violin plots, helping explore differences between cellular populations and individual parameters.
CITATION
Onzi G.R., Faccioni J., et al. SCOUTS: Single-Cell Outlier Selector. bioRxiv, 2020.
