NBumi_FSHighVar: Variance-based Feature Selection
In tallulandrews/M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq
NBumiFeatureSelectionHighVar R Documentation
Variance-based Feature Selection
Description
Ranks genes by residual dispersion from mean-dispersion power-law relationship.
Usage
NBumiFeatureSelectionHighVar(fit)
Arguments
fit
output from NBumiFitModel or NBumiFitBasicModel.
Details
Uses linear regression on log-transformed mean expression and fitted dispersions. Ranks genes by the residuals of this fit, negative = high variance, positive = low variance.
Value
Sorted vector of residuals.
Examples
library(M3DExampleData)
counts <- NBumiConvertData(Mmus_example_list$data)
fit <- NBumiFitModel(counts);
HVGs <- names(NBumiFeatureSelectionHighVar(fit)[1:2000]);
tallulandrews/M3Drop documentation built on March 6, 2024, 1:49 a.m.
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| NBumiFeatureSelectionHighVar | R Documentation |
Variance-based Feature Selection
Description
Ranks genes by residual dispersion from mean-dispersion power-law relationship.
Usage
NBumiFeatureSelectionHighVar(fit)
Arguments
fit |
output from NBumiFitModel or NBumiFitBasicModel. |
Details
Uses linear regression on log-transformed mean expression and fitted dispersions. Ranks genes by the residuals of this fit, negative = high variance, positive = low variance.
Value
Sorted vector of residuals.
Examples
library(M3DExampleData)
counts <- NBumiConvertData(Mmus_example_list$data)
fit <- NBumiFitModel(counts);
HVGs <- names(NBumiFeatureSelectionHighVar(fit)[1:2000]);
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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