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 |

`min.diff` |
numeric scalar indicating the minimum difference from the
median to consider as an outlier. The outlier threshold is defined from the
larger of |

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")
example_sce <- SingleCellExperiment(
assays = list(counts = sc_example_counts), colData = sc_example_cell_info)
example_sce <- calculateQCMetrics(example_sce)
## with a set of feature controls defined
example_sce <- calculateQCMetrics(example_sce,
feature_controls = list(set1 = 1:40))
isOutlier(example_sce$total_counts, nmads = 3)
``` |

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