# treefit: Estimate the goodness-of-fit between tree models and data In treefit: The First Software for Quantitative Trajectory Inference

## Description

Estimate the goodness-of-fit between tree models and data.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```treefit( target, name = NULL, perturbations = NULL, normalize = NULL, reduce_dimension = NULL, build_tree = NULL, max_p = 20, n_perturbations = 20 ) ```

## Arguments

 `target` The target data to be estimated. It must be one of them: `list(counts=COUNTS, expression=EXPRESSION)`: You must specify at least one of `COUNTS` and `EXPRESSION`. They are `matrix`. The rows and columns correspond to samples such as cells and features such as genes. `COUNTS`'s value is count data such as the number of genes expressed. `EXPRESSION`'s value is normalized count data. `Seurat` object `name` The name of `target` as string. `perturbations` How to perturbate the target data. If this is `NULL`, all available perturbation methods are used. You can specify used perturbation methods as `list`. Here are available methods: `normalize` How to normalize counts data. If this is `NULL`, the default normalization is applied. You can specify a function that normalizes counts data. `reduce_dimension` How to reduce dimension of expression data. If this is `NULL`, the default dimensionality reduction is applied. You can specify a function that reduces dimension of expression data. `build_tree` How to build a tree of expression data. If this is `NULL`, MST is built. You can specify a function that builds tree of expression data. `max_p` How many low dimension Laplacian eigenvectors are used. The default is 20. `n_perturbations` How many times to perturb. The default is 20.

## Value

An estimated result as a `treefit` object. It has the following attributes:

• `max_cca_distance`: The result of max canonical correlation analysis distance as `data.frame`.

• `rms_cca_distance`: The result of root mean square canonical correlation analysis distance as `data.frame`.

• `n_principal_paths_candidates`: The candidates of the number of principal paths.

`data.frame` of `max_cca_distance` and `rms_cca_distance` has the same structure. They have the following columns:

• `p`: Dimensionality of the feature space of tree structures.

• `mean`: The mean of the target distance values.

• `standard_deviation`: The standard deviation of the target distance values.

## Examples

 ```1 2 3 4 5 6 7 8``` ```## Not run: # Generate a star tree data that have normalized expression values # not count data. star <- treefit::generate_2d_n_arms_star_data(300, 3, 0.1) # Estimate tree-likeness of the tree data. fit <- treefit::treefit(list(expression=star)) ## End(Not run) ```

treefit documentation built on Jan. 18, 2022, 9:06 a.m.