NBumi_HVG: Variance-based Feature Selection Accounting for Library Size...
In tallulandrews/M3D: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Description Usage Arguments Details Value References Examples

Tests for significantly high variability in droplet-based datasets.

1	NBumiHVG(counts, fit, fdr_thresh=0.05, suppress.plot=FALSE, method=c("DANB", "basic"))

`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

	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");