| group_cell {nebula} | R Documentation |
Group cells according to subject IDs
Description
Group cells according to subject IDs
Usage
group_cell(count, id, pred = NULL, offset = NULL)
Arguments
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. |
Value
count: A reordered count matrix. If the cells are already grouped, the function returns NULL.
id: A reordered subject ID vector.
pred: A reordered design matrix of the predictors.
offset: A reordered vector of the scaling factor.
Examples
library(nebula)
data(sample_data)
pred = model.matrix(~X1+X2+cc,data=sample_data$pred)
df_order = group_cell(count=sample_data$count,id=sample_data$sid,pred=pred)