Select genes in a cell_data_set for dimensionality reduction
select_genes.RdMonocle3 aims to learn how cells transition through a
biological program of gene expression changes in an experiment. Each cell
can be viewed as a point in a high-dimensional space, where each dimension
describes the expression of a different gene. Identifying the program of
gene expression changes is equivalent to learning a trajectory that
the cells follow through this space. However, the more dimensions there are
in the analysis, the harder the trajectory is to learn. Fortunately, many
genes typically co-vary with one another, and so the dimensionality of the
data can be reduced with a wide variety of different algorithms. Monocle3
provides two different algorithms for dimensionality reduction via
reduce_dimensions (UMAP and tSNE). The function
select_features is an optional step in the trajectory building
process before preprocess_cds. After calculating dispersion for
a cell_data_set using the calculate_gene_dispersion function, the
select_genes function allows the user to identify a set of genes
that will be used in downstream dimensionality reduction methods.
Usage
select_genes(
cds,
fit_min = 1,
fit_max = Inf,
logmean_ul = Inf,
logmean_ll = -Inf,
top_n = NULL
)Arguments
- cds
the cell_data_set upon which to perform this operation.
- fit_min
the minimum multiple of the dispersion fit calculation; default = 1
- fit_max
the maximum multiple of the dispersion fit calculation; default = Inf
- logmean_ul
the maximum multiple of the dispersion fit calculation; default = Inf
- logmean_ll
the maximum multiple of the dispersion fit calculation; default = Inf
- top
top_n if specified, will override the fit_min and fit_max to select the top n most variant features. logmena_ul and logmean_ll can still be used.