predict_celltype.Seurat performance improvement #2
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predict_celltype.Seurat was converting the full data matrix from sparse to dense. Then the predict_celltype.matrix method filtered only the genes present in the db. This is not optimal as it generates a potentially huge matrix to later on keep a much smaller number of rows. Instead, a method for predicting sparse matrices is given, that first filters the genes and then converts the matrix from sparse to dense.
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Hi,
I was doing a quick check of your package and I have a quick fix for using less RAM when dealing with sparse matrices, such as the ones given by the Seurat package.
predict_celltype.Seuratwas converting the full data matrix from sparse to dense. Then thepredict_celltype.matrixmethod filtered only the genes present in the db. This is not optimal as itgenerates a potentially huge matrix to later on keep a much smaller number of rows.
Instead, a method for predicting sparse matrices
predict_celltype.dgCMatrixis given, thatfirst filters the genes present in the
dband then converts the filtered matrix from sparse to dense.It's also a good solution to close #1, as SingleCellExperiments that have the matrix defined as a dgCMatrix will use the given method as well.
Closes #1