Package 'phateR' reference manual

Title:	PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding
Description:	PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories. PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another. To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology at <doi:10.1038/s41587-019-0336-3>.
Authors:	Krishnan Srinivasan [aut], Scott Gigante [cre]
Maintainer:	Scott Gigante <[email protected]>
License:	GPL-2 \| file LICENSE
Version:	1.0.7
Built:	2025-03-09 05:11:39 UTC
Source:	https://github.com/cran/phateR

Convert a PHATE object to a data.frame

Description

Returns the embedding matrix with column names PHATE1 and PHATE2

Usage

## S3 method for class 'phate'
as.data.frame(x, ...)
## S3 method for class 'phate'
as.data.frame(x, ...)

Arguments

`x`	A fitted PHATE object
`...`	Arguments for as.data.frame()

Convert a PHATE object to a matrix

Description

Returns the embedding matrix. All components can be accessed using phate$embedding, phate$diff.op, etc

Usage

## S3 method for class 'phate'
as.matrix(x, ...)
## S3 method for class 'phate'
as.matrix(x, ...)

Arguments

`x`	A fitted PHATE object
`...`	Arguments for as.matrix()

Check that the current PHATE version in Python is up to date.

Description

Check that the current PHATE version in Python is up to date.

Usage

check_pyphate_version()
check_pyphate_version()

KMeans on the PHATE potential Clustering on the PHATE operator as introduced in Moon et al. This is similar to spectral clustering.

Description

KMeans on the PHATE potential Clustering on the PHATE operator as introduced in Moon et al. This is similar to spectral clustering.

Usage

cluster_phate(phate, k = 8, seed = NULL)
cluster_phate(phate, k = 8, seed = NULL)

Arguments

`phate`	`phate()` output
`k`	Number of clusters (default: 8)
`seed`	Random seed for kmeans (default: NULL)

Value

clusters Integer vector of cluster assignments

Examples

if (reticulate::py_module_available("phate")) {

# Load data
# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)

# Run PHATE
phate.tree <- phate(tree.data.small$data)

# Clustering
cluster_phate(phate.tree)
}
if (reticulate::py_module_available("phate")) {

# Load data
# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)

# Run PHATE
phate.tree <- phate(tree.data.small$data)

# Clustering
cluster_phate(phate.tree)
}

Convert a PHATE object to a data.frame for ggplot

Description

Passes the embedding matrix to ggplot with column names PHATE1 and PHATE2

Usage

## S3 method for class 'phate'
ggplot(data, ...)
## S3 method for class 'phate'
ggplot(data, ...)

Arguments

`data`	A fitted PHATE object
`...`	Arguments for ggplot()

Examples

if (reticulate::py_module_available("phate") && require(ggplot2)) {

# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
ggplot(phate.tree, aes(x=PHATE1, y=PHATE2, color=tree.data.small$branches)) +
  geom_point()

}
if (reticulate::py_module_available("phate") && require(ggplot2)) {

# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
ggplot(phate.tree, aes(x=PHATE1, y=PHATE2, color=tree.data.small$branches)) +
  geom_point()

}

Install PHATE Python Package

Description

Install PHATE Python package into a virtualenv or conda env.

Usage

install.phate(
  envname = "r-reticulate",
  method = "auto",
  conda = "auto",
  pip = TRUE,
  ...
)
install.phate(
  envname = "r-reticulate",
  method = "auto",
  conda = "auto",
  pip = TRUE,
  ...
)

Arguments

`envname`	Name of environment to install packages into
`method`	Installation method. By default, "auto" automatically finds a method that will work in the local environment. Change the default to force a specific installation method. Note that the "virtualenv" method is not available on Windows.
`conda`	Path to conda executable (or "auto" to find conda using the PATH and other conventional install locations).
`pip`	Install from pip, if possible.
`...`	Additional arguments passed to conda_install() or virtualenv_install().

Details

On Linux and OS X the "virtualenv" method will be used by default ("conda" will be used if virtualenv isn't available). On Windows, the "conda" method is always used.

Performs L1 normalization on input data such that the sum of expression values for each cell sums to 1, then returns normalized matrix to the metric space using median UMI count per cell effectively scaling all cells as if they were sampled evenly.

Description

Performs L1 normalization on input data such that the sum of expression values for each cell sums to 1, then returns normalized matrix to the metric space using median UMI count per cell effectively scaling all cells as if they were sampled evenly.

Usage

library.size.normalize(data, verbose = FALSE)
library.size.normalize(data, verbose = FALSE)

Arguments

`data`	matrix (n_samples, n_dimensions) 2 dimensional input data array with n cells and p dimensions
`verbose`	boolean, default=FALSE. If true, print verbose output

Value

data_norm matrix (n_samples, n_dimensions) 2 dimensional array with normalized gene expression values

Run PHATE on an input data matrix

Description

PHATE is a data reduction method specifically designed for visualizing high dimensional data in low dimensional spaces.

Usage

phate(
  data,
  ndim = 2,
  knn = 5,
  decay = 40,
  n.landmark = 2000,
  gamma = 1,
  t = "auto",
  mds.solver = "sgd",
  knn.dist.method = "euclidean",
  knn.max = NULL,
  init = NULL,
  mds.method = "metric",
  mds.dist.method = "euclidean",
  t.max = 100,
  npca = 100,
  plot.optimal.t = FALSE,
  verbose = 1,
  n.jobs = 1,
  seed = NULL,
  potential.method = NULL,
  k = NULL,
  alpha = NULL,
  use.alpha = NULL,
  ...
)
phate(
  data,
  ndim = 2,
  knn = 5,
  decay = 40,
  n.landmark = 2000,
  gamma = 1,
  t = "auto",
  mds.solver = "sgd",
  knn.dist.method = "euclidean",
  knn.max = NULL,
  init = NULL,
  mds.method = "metric",
  mds.dist.method = "euclidean",
  t.max = 100,
  npca = 100,
  plot.optimal.t = FALSE,
  verbose = 1,
  n.jobs = 1,
  seed = NULL,
  potential.method = NULL,
  k = NULL,
  alpha = NULL,
  use.alpha = NULL,
  ...
)

Arguments

`data`	matrix (n_samples, n_dimensions) 2 dimensional input data array with n_samples samples and n_dimensions dimensions. If `knn.dist.method` is 'precomputed', `data` is treated as a (n_samples, n_samples) distance or affinity matrix
`ndim`	int, optional, default: 2 number of dimensions in which the data will be embedded
`knn`	int, optional, default: 5 number of nearest neighbors on which to build kernel
`decay`	int, optional, default: 40 sets decay rate of kernel tails. If NULL, alpha decaying kernel is not used
`n.landmark`	int, optional, default: 2000 number of landmarks to use in fast PHATE
`gamma`	float, optional, default: 1 Informational distance constant between -1 and 1. `gamma=1` gives the PHATE log potential, `gamma=0` gives a square root potential.
`t`	int, optional, default: 'auto' power to which the diffusion operator is powered sets the level of diffusion
`mds.solver`	'sgd', 'smacof', optional, default: 'sgd' which solver to use for metric MDS. SGD is substantially faster, but produces slightly less optimal results. Note that SMACOF was used for all figures in the PHATE paper.
`knn.dist.method`	string, optional, default: 'euclidean'. recommended values: 'euclidean', 'cosine', 'precomputed' Any metric from `scipy.spatial.distance` can be used distance metric for building kNN graph. If 'precomputed', `data` should be an n_samples x n_samples distance or affinity matrix. Distance matrices are assumed to have zeros down the diagonal, while affinity matrices are assumed to have non-zero values down the diagonal. This is detected automatically using `data[0,0]`. You can override this detection with `knn.dist.method='precomputed_distance'` or `knn.dist.method='precomputed_affinity'`.
`knn.max`	int, optional, default: NULL Maximum number of neighbors for which alpha decaying kernel is computed for each point. For very large datasets, setting `knn.max` to a small multiple of `knn` can speed up computation significantly.
`init`	phate object, optional object to use for initialization. Avoids recomputing intermediate steps if parameters are the same.
`mds.method`	string, optional, default: 'metric' choose from 'classic', 'metric', and 'nonmetric' which MDS algorithm is used for dimensionality reduction
`mds.dist.method`	string, optional, default: 'euclidean' recommended values: 'euclidean' and 'cosine'
`t.max`	int, optional, default: 100. Maximum value of t to test for automatic t selection.
`npca`	int, optional, default: 100 Number of principal components to use for calculating neighborhoods. For extremely large datasets, using n_pca < 20 allows neighborhoods to be calculated in log(n_samples) time.
`plot.optimal.t`	boolean, optional, default: FALSE If TRUE, produce a plot showing the Von Neumann Entropy curve for automatic t selection.
`verbose`	`int` or `boolean`, optional (default : 1) If `TRUE` or `⁠> 0⁠`, print verbose updates.
`n.jobs`	`int`, optional (default: 1) The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n.cpus + 1 + n.jobs) are used. Thus for n_jobs = -2, all CPUs but one are used
`seed`	int or `NULL`, random state (default: `NULL`)
`potential.method`	Deprecated. For log potential, use `gamma=1`. For sqrt potential, use `gamma=0`.
`k`	Deprecated. Use `knn`.
`alpha`	Deprecated. Use `decay`.
`use.alpha`	Deprecated To disable alpha decay, use `alpha=NULL`
`...`	Additional arguments for `graphtools.Graph`.

Value

"phate" object containing:

embedding: the PHATE embedding
operator: The PHATE operator (python phate.PHATE object)
params: Parameters passed to phate

Examples

if (reticulate::py_module_available("phate")) {

# Load data
# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)

# Run PHATE
phate.tree <- phate(tree.data.small$data)
summary(phate.tree)
## PHATE embedding
## knn = 5, decay = 40, t = 58
## Data: (3000, 100)
## Embedding: (3000, 2)

library(graphics)
# Plot the result with base graphics
plot(phate.tree, col=tree.data.small$branches)
# Plot the result with ggplot2
if (require(ggplot2)) {
  ggplot(phate.tree) +
    geom_point(aes(x=PHATE1, y=PHATE2, color=tree.data.small$branches))
}

# Run PHATE again with different parameters
# We use the last run as initialization
phate.tree2 <- phate(tree.data.small$data, t=150, init=phate.tree)
# Extract the embedding matrix to use in downstream analysis
embedding <- as.matrix(phate.tree2)

}
if (reticulate::py_module_available("phate")) {

# Load data
# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)

# Run PHATE
phate.tree <- phate(tree.data.small$data)
summary(phate.tree)
## PHATE embedding
## knn = 5, decay = 40, t = 58
## Data: (3000, 100)
## Embedding: (3000, 2)

library(graphics)
# Plot the result with base graphics
plot(phate.tree, col=tree.data.small$branches)
# Plot the result with ggplot2
if (require(ggplot2)) {
  ggplot(phate.tree) +
    geom_point(aes(x=PHATE1, y=PHATE2, color=tree.data.small$branches))
}

# Run PHATE again with different parameters
# We use the last run as initialization
phate.tree2 <- phate(tree.data.small$data, t=150, init=phate.tree)
# Extract the embedding matrix to use in downstream analysis
embedding <- as.matrix(phate.tree2)

}

Plot a PHATE object in base R

Description

Plot a PHATE object in base R

Usage

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

Arguments

`x`	A fitted PHATE object
`...`	Arguments for plot()

Examples

if (reticulate::py_module_available("phate")) {

library(graphics)
# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
plot(phate.tree, col=tree.data.small$branches)

}
if (reticulate::py_module_available("phate")) {

library(graphics)
# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
plot(phate.tree, col=tree.data.small$branches)

}

Print a PHATE object

Description

This avoids spamming the user's console with a list of many large matrices

Usage

## S3 method for class 'phate'
print(x, ...)
## S3 method for class 'phate'
print(x, ...)

Arguments

`x`	A fitted PHATE object
`...`	Arguments for print()

Examples

if (reticulate::py_module_available("phate")) {

# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
print(phate.tree)
## PHATE embedding with elements
## $embedding : (3000, 2)
## $operator : Python PHATE operator
## $params : list with elements (data, knn, decay, t, n.landmark, ndim,
##                               gamma, npca, mds.method,
##                               knn.dist.method, mds.dist.method)

}
if (reticulate::py_module_available("phate")) {

# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
print(phate.tree)
## PHATE embedding with elements
## $embedding : (3000, 2)
## $operator : Python PHATE operator
## $params : list with elements (data, knn, decay, t, n.landmark, ndim,
##                               gamma, npca, mds.method,
##                               knn.dist.method, mds.dist.method)

}

Summarize a PHATE object

Description

Summarize a PHATE object

Usage

## S3 method for class 'phate'
summary(object, ...)
## S3 method for class 'phate'
summary(object, ...)

Arguments

`object`	A fitted PHATE object
`...`	Arguments for summary()

Examples

if (reticulate::py_module_available("phate")) {

# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
summary(phate.tree)
## PHATE embedding
## knn = 5, decay = 40, t = 58
## Data: (3000, 100)
## Embedding: (3000, 2)

}
if (reticulate::py_module_available("phate")) {

# data(tree.data)
# We use a smaller tree to make examples run faster
data(tree.data.small)
phate.tree <- phate(tree.data.small$data)
summary(phate.tree)
## PHATE embedding
## knn = 5, decay = 40, t = 58
## Data: (3000, 100)
## Embedding: (3000, 2)

}

Fake branching data for examples

Description

A dataset containing high dimensional data that has 10 unique branches

Usage

tree.data
tree.data

Format

A list containing data, a matrix with 3000 rows and 100 variables and branches, a factor containing 3000 elements.

Source

The authors

Fake branching data for running examples fast

Description

A dataset containing high dimensional data that has 10 unique branches

Usage

tree.data.small
tree.data.small

Format

A list containing data, a matrix with 250 rows and 50 variables and branches, a factor containing 250 elements.

Source

The authors

Package 'phateR'

Help Index

Convert a PHATE object to a data.frame

Description

Usage

Arguments

Convert a PHATE object to a matrix

Description

Usage

Arguments

Check that the current PHATE version in Python is up to date.

Description

Usage

KMeans on the PHATE potential Clustering on the PHATE operator as introduced in Moon et al. This is similar to spectral clustering.

Description

Usage

Arguments

Value

Examples

Convert a PHATE object to a data.frame for ggplot

Description

Usage

Arguments

Examples

Install PHATE Python Package

Description

Usage

Arguments

Details

Performs L1 normalization on input data such that the sum of expression values for each cell sums to 1, then returns normalized matrix to the metric space using median UMI count per cell effectively scaling all cells as if they were sampled evenly.

Description

Usage

Arguments

Value

Run PHATE on an input data matrix

Description

Usage

Arguments

Value

Examples

Plot a PHATE object in base R

Description

Usage

Arguments

Examples

Print a PHATE object

Description

Usage

Arguments

Examples

Summarize a PHATE object

Description

Usage

Arguments

Examples

Fake branching data for examples

Description

Usage

Format

Source

Fake branching data for running examples fast

Description

Usage

Format

Source