Title: PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding
Version: 1.0.7
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>.
License: GPL-2 | file LICENSE
Encoding: UTF-8
LazyData: true
Depends: R (≥ 3.3), Matrix (≥ 1.2-0)
Imports: methods, stats, graphics, reticulate (≥ 1.8), ggplot2, memoise
Suggests: gridGraphics, cowplot
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-02-11 18:04:00 UTC; runner
Author: Krishnan Srinivasan [aut], Scott Gigante ORCID iD [cre]
Maintainer: Scott Gigante <scott.gigante@yale.edu>
Repository: CRAN
Date/Publication: 2021-02-12 10:00:02 UTC

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, ...)

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, ...)

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()

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)

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)
}

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, ...)

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()

}

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,
  ...
)

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)

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,
  ...
)

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:

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)

}

Plot a PHATE object in base R

Description

Plot a PHATE object in base R

Usage

## 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)

}

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, ...)

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)

}

Summarize a PHATE object

Description

Summarize a PHATE object

Usage

## 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)

}

Fake branching data for examples

Description

A dataset containing high dimensional data that has 10 unique branches

Usage

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

Format

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

Source

The authors