|Summary of outlier detection methods in the QUAliFiER package|
|Outlier function||Type||Use case|
|outlier.cutoff||threshold||1. % of cells in WBC gate for RBC Lysis|
|2. % of total events as boundary events|
|3. Minimum total event count|
|outlier.norm||robust normal||1. Stability of MFI of a population vs time|
|2. Consistency of gating (%) of a population|
|3. High variability groups when measuring between–group variation (i.e. log(IQR) for boxplots)|
|4. Individual outliers from residuals of robust regression (i.e. in xyplot)|
|qoutlier||1.5 × IQR||1. Outliers within groups for boxplots|
Outlier detection methods provided by QUAliFiER include fixed threshold cutoffs (outlier.cutoff), outlier calls based on t–statistic or Z–score cutoffs or based on significance levels (α) (outlier.norm), or calls based on the interquartile range (IQR) of a set of statistics (qoutlier) like that typically used for highlighting outliers in box–plots. Use cases for each are shown in the table.
Finak et al.
Finak et al. BMC Bioinformatics 2012 13:252 doi:10.1186/1471-2105-13-252