Calculate dispersion genes in a cell_data_set object
calculate_gene_dispersion.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
calculate_dispersion 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. These
genes and their disperion and mean expression can be plotted using the
plot_gene_dispersion function.
This function calculates dispersion genes in a cell_data_set object for downstream analysis.
Usage
calculate_gene_dispersion(
cds,
q = 3,
id_tag = "id",
symbol_tag = "gene_short_name",
method = "m3addon",
removeOutliers = T
)
calculate_gene_dispersion(
cds,
q = 3,
id_tag = "id",
symbol_tag = "gene_short_name",
method = "m3addon",
removeOutliers = T
)Arguments
- cds
The cell data set upon which to perform this operation.
- q
The polynomial degree.
- id_tag
The name of the feature data column corresponding to the unique id.
- symbol_tag
The name of the feature data column corresponding to the gene symbol.
- upper_lim
The upper limit of dispersion to consider.
- verbose
Boolean indicating whether to display verbose output.