Package 'treefit'

Generate a 2-dimensional linked star tree data

Description

Generate a 2-dimensional linked star tree data. Each star tree data contain n_samples_vector[i] data points and fit a star tree with n_arms_vector[i] arms.

Usage

generate_2d_n_arms_linked_star_data(n_samples_vector, n_arms_vector, fatness)

Arguments

n_samples_vector	The vector of the number of samples to be generated. For example, c(200, 100, 300) means that the first tree has 200 samples, the second tree has 100 samples and the third tree has 300 samples.
n_arms_vector	The vector of the number of arms to be generated. For example, c(3, 2, 5) means the first tree fits a star tree with 3 arms, the second tree fits a star tree with 2 arms and the third tree fits a star tree with 5 arms. The size of n_arms_vector must equal to the size of n_samples_vector.
fatness	How fat from the based tree. ⁠[0.0, 1.0]⁠ is available value range.

Value

A generated martix. The rows and columns correspond to samples and features.

Examples

# Generate a 2-dimensional linked star tree data that contain
# 200-400-300 data points and fit a linked star tree with 3-5-4
# arms. The generated data are a bit noisy but tree-like.
linked_star.tree_like <-
  treefit::generate_2d_n_arms_linked_star_data(c(200, 400, 300),
                                               c(3, 5, 4),
                                               0.1)
plot(linked_star.tree_like)

# Generate a 2-dimensional linked star tree data that contain
# 300-200 data points and fit a linked star tree with 4-3 arms.
# The generated data are very noisy and less tree-like.
linked_star.less_tree_like <-
  treefit::generate_2d_n_arms_linked_star_data(c(300, 200),
                                               c(4, 3),
                                               0.9)
plot(linked_star.less_tree_like)

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Generate a 2-dimensional star tree data

Description

Generate a 2-dimensional star tree data that contain n_samples data points and fit a star tree with n_arms arms.

Usage

generate_2d_n_arms_star_data(n_samples, n_arms, fatness)

Arguments

n_samples	The number of samples to be generated.
n_arms	The number of arms to be generated.
fatness	How fat from the based star tree. ⁠[0.0, 1.0]⁠ is available value range.

Value

A generated martix. The rows and columns correspond to samples and features.

Examples

# Generate a 2-dimensional star tree data that contain 500 data points
# and fit a star tree with 3 arms. The generated data are a bit noisy but
# tree-like.
star.tree_like <- treefit::generate_2d_n_arms_star_data(500, 3, 0.1)
plot(star.tree_like)

# Generate a 2-dimensional star tree data that contain 600 data points
# and fit a star tree with 5 arms. The generated data are very noisy and
# less tree-like.
star.less_tree_like <- treefit::generate_2d_n_arms_star_data(600, 5, 0.9)
plot(star.less_tree_like)

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Generate a multi-dimensional star tree data

Description

Generate a multi-dimensional star tree data that contain n_samples data points and fit a star tree with n_arms arms.

Usage

generate_n_arms_star_data(n_features, n_samples, n_arms, fatness)

Arguments

n_features	The number of features (dimensions) to be generated.
n_samples	The number of samples to be generated.
n_arms	The number of arms to be generated.
fatness	How fat from the based star tree. ⁠[0.0, 1.0]⁠ is available value range.

Value

A generated martix. The rows and columns correspond to samples and features.

Examples

# Generate a 100-dimensional star tree data that contain 500 data points
# and fit a star tree with 3 arms. The generated data are a bit noisy but
# tree-like.
star100.tree_like <- treefit::generate_n_arms_star_data(100, 500, 3, 0.1)
# Reduce dimension to visualize.
star3.tree_like = prcomp(star100.tree_like, rank.=3)$x
plotly::plot_ly(data.frame(star3.tree_like),
                x=~PC1,
                y=~PC2,
                z=~PC3,
                type="scatter3d",
                mode="markers",
                marker=list(size=1))

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Generate perturbated expression by k-NN data

Description

Generate perturbated expression from the original expression based on k-NN (k-nearest neighbor) data.

Usage

perturbate_knn(expression, strength = 1)

Arguments

expression	The original expression. The rows and columns correspond to samples and features. The expression is normalized count of features.
strength	How much perturbated. 0.0 is weak. 1.0 is strong.

Value

A perturbated expression as a matrix. The matrix's expression values are perturbated from the original expression values. The shape of the matrix is the same as the original expression. The dimension names of the matrix are also the same as the original expression.

Note

This is an API for advanced users. This API may be changed.

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Generate perturbated counts by the Poisson distribution

Description

Generate perturbated counts from the original counts by the Poisson distribution.

Usage

perturbate_poisson(counts, strength = 1)

Arguments

counts	The original counts. The rows and columns correspond to samples and features. The values are count of features.
strength	How much perturbated. 0.0 is weak. 1.0 is strong.

Value

A perturbated counts as a matrix. The matrix's counts are perturbated from the original counts. The shape of the matrix is the same as the original counts. The dimension names of the matrix are also the same as the original counts.

Note

This is an API for advanced users. This API may be changed.

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Plot estimated results

Description

Plot estimate results to get insight.

Usage

## S3 method for class 'treefit'
plot(x, ...)

Arguments

x	The estimated result by treefit() to be visualized.
...	The more estimated results to be visualized together or other graphical parameters.

Value

A plot object as a ggplot object. It plots the given one or more estimated results to get insights from one or more treefit() results.

Examples

## Not run: 
# Generate a tree data.
tree <- treefit::generate_2d_n_arms_star_data(200, 3, 0.1)
# Estimate the goodness-of-fit between tree models and the tree data.
fit <- treefit::treefit(list(expression=tree), "tree")
# Visualize the estimated result.
plot(fit)

# You can mix multiple estimated results by adding "name" column.
tree2 <- treefit::generate_2d_n_arms_star_data(200, 3, 0.9)
fit2 <- treefit::treefit(list(expression=tree2), "tree2")
plot(fit, fit2)

## End(Not run)

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

Description

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

Usage

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

## 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)

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