Description Usage Arguments Value Examples

Convenience function to determine which values for a metric are outliers based on median-absolute-deviation (MAD).

1 2 |

`metric` |
numeric or integer vector of values for a metric |

`nmads` |
scalar, number of median-absolute-deviations away from median required for a value to be called an outlier |

`type` |
character scalar, choice indicate whether outliers should be looked for at both tails (default: "both") or only at the lower end ("lower") or the higher end ("higher") |

`log` |
logical, should the values of the metric be transformed to the log10 scale before computing median-absolute-deviation for outlier detection? |

`subset` |
logical or integer vector, which subset of values should be
used to calculate the median/MAD? If |

`batch` |
factor of length equal to |

a logical vector of the same length as the `metric`

argument

1 2 3 4 5 6 7 8 9 10 | ```
data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data=sc_example_cell_info)
rownames(pd) <- pd$Cell
example_sceset <- newSCESet(countData=sc_example_counts, phenoData=pd)
example_sceset <- calculateQCMetrics(example_sceset)
## with a set of feature controls defined
example_sceset <- calculateQCMetrics(example_sceset, feature_controls = 1:40)
isOutlier(example_sceset$total_counts, nmads = 3)
``` |

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