## Table 2 |
||

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