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scran_qc
Simple quality control on single-cell data
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Options for choose_filter_thresholds().
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#include <choose_filter_thresholds.hpp>
Public Attributes | |
| bool | lower = true |
| bool | upper = true |
| double | num_mads = 3 |
| double | min_diff = 0 |
| bool | log = false |
Options for choose_filter_thresholds().
Should low values be considered as potential outliers? If false, no lower threshold is applied when defining outliers.
Should high values be considered as potential outliers? If false, no upper threshold is applied when defining outliers.
| double scran_qc::ChooseFilterThresholdsOptions::num_mads = 3 |
Number of MADs to use to define outliers. Larger values result in more relaxed thresholds. By default, we require 3 MADs, which is motivated by the low probability (less than 1%) of obtaining such a value under the normal distribution.
| double scran_qc::ChooseFilterThresholdsOptions::min_diff = 0 |
Minimum difference from the median to define outliers. This enforces a more relaxed threshold in cases where the MAD may be too small. If ChooseFilterThresholdsOptions::log = true, this difference is interpreted as a unit on the log-scale.
Whether the supplied median and MAD should be computed on the log-scale (i.e., FindMedianMadOptions::log = true). This focuses on the fold-change from the median when defining outliers. In practice, this is useful for metrics that are always positive and have right-skewed distributions, as the log-transformation symmetrizes the distribution and makes it more normal-like such that the ChooseFilterThresholdsOptions::num_mads interpretation can be applied. It also ensures that the defined threshold is always positive.
If this is set to true, the thresholds are converted back to the original scale of the metrics prior to filtering.
1.9.8