ParameterConfig

S4 Class to hold parameters used in the analysis. It is constantly updated asthe analysis progresses.

Slots

bin_ptime_col

A character string representing the column name for binned Pseudotime values in 'Dense' data. See `colData` from the SingleCellExperiment package.

path_prefix

Update Description..

root_label

Update Description..

ptime_col

A character string representing the column name for inferred Pseudotime values in 'Sparse' data. See `colData` from the SingleCellExperiment package.

bin_method

A character string representing the algorithm used for binning.

path_col

A character string representing the column name for branching path assignment in 'Sparse' or 'Dense' data. See `colData` from the SingleCellExperiment package.

bin_col

A character string representing the name of the column in which bin labels are stored.

bin_size_col

A character string representing the name of the column in which bin sizes per bin are stored.

bin_mem_col

A character string representing the name of the column in which cells per bin are stored.

anno_col

A character string representing the column name for cell level metadata containing cell level annotations. (Default is "cell_type").

g

Update Description..

p_value

Significance Level.

min_na

Minimum values needed per gene across cells to estimate the model.

mt_correction

A character string specifying the p-value correction method.

link

Type of link function to use in the model. Default is "log".

epsilon

Model convergence tolerance.

selection_method

A character string specifying the method for stepwise regression.

offset

A logical value specifying whether to use offset during fitting.

log_offset

A logical value specifying whether to take the logarithm of the offsets.

max_it

Maximum number of iterations to fit the model.

poly_degree

Order of the polynomial linear model.

distribution

Distribution of the error term.

cluster_method

Clustering method used for clustering significant genes.

use_dim

Dimension to use for filling the missing values before clustering.

fill_na

Method to fill the missing values.