Description Usage Arguments Details Value References Examples

Tests for significantly high variability in droplet-based datasets.

1 |

`counts` |
raw count matrix (e.g. from NBumiConvertData). |

`fit` |
output from NBumiFitModel or NBumiFitBasicModel. |

`fdr_thresh` |
multiple testing correction threshold to apply to filter output. |

`suppress.plot` |
whether to plot mean vs variance & selected features. |

`method` |
whether to use DANB dispersions or raw sample variances. |

Assumes a constant dispersion parameter due to technical noise (see: [1]), which is estimated using linear regression. Gene-specific observed sample variances are compared to their respective expected variances. Significance is evaluated using a Z-test with the expected variance of the sample variance for a Negative Binomial (see: [2]).

The method argument controls whether the expected and observed variances are adjusted to account for uneven library sizes between cells. The default "DANB" option does the adjustment.

a data.frame with columns: Gene, effect.size, p.value, q.value

[1] Svensson, V. (2019) Droplet scRNA-seq is not zero-inflated. bioRxiv. doi: https://doi.org/10.1101/582064 [2] Rose, C. and Smith, M. D. Mathematical Statistics with Mathematica. New York: Springer-Verlag, 2002. p264

1 2 3 4 5 | ```
library(M3DExampleData)
counts <- NBumiConvertData(Mmus_example_list$data)
fit <- NBumiFitModel(counts);
HVGs <- NBumiHVG(counts, fit, suppress.plot=TRUE);
HVGs_uncorrected <- NBumiHVG(counts, fit, suppress.plot=TRUE, method="basic");
``` |

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