nbresidual: Extract Pearson residuals from the results of NEBULA

In nebula: Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data

Extract Pearson residuals from the results of NEBULA

Description

Calculates and returns Pearson residuals from NEBULA analysis results. Can compute either marginal or conditional residuals.

Usage

nbresidual(nebula, count, id, pred = NULL, offset = NULL, conditional = FALSE)

Arguments

nebula	An object of the result obtained from running the function nebula.
count	A raw count matrix of the single-cell data. The rows are the genes, and the columns are the cells. The matrix can be a matrix object or a sparse dgCMatrix object.
id	A vector of subject IDs. The length should be the same as the number of columns of the count matrix.
pred	A design matrix of the predictors. The rows are the cells and the columns are the predictors. If not specified, an intercept column will be generated by default.
offset	A vector of the scaling factor. The values must be strictly positive. If not specified, a vector of all ones will be generated by default.
conditional	A logical value. By default (FALSE), the function returns marginal Pearson residuals. If TRUE, the function will return conditional Pearson residuals.

Details

Extract Pearson residuals from the results of NEBULA

Value

residuals: A matrix of Pearson residuals. The number of columns is the number of cells in the count matrix. The rows correspond to gene IDs reported in the result from nebula.

gene: Gene names corresponding to the row names of the count matrix.

Examples

library(nebula)
data(sample_data)
pred = model.matrix(~X1+X2+cc,data=sample_data$pred)
re = nebula(count=sample_data$count,id=sample_data$sid,pred=pred)
resid = nbresidual(re,count=sample_data$count,id=sample_data$sid,pred=pred)

nebula documentation built on July 12, 2026, 9:06 a.m.