Awesome
phateR
<!-- README.md is generated from README.Rmd. Please edit that file -->This R package provides an implementation of the PHATE dimensionality reduction and visualization method.
For a thorough overview of the PHATE visualization method, please see the Nature Biotechnology publication.
For our Python and Matlab implementations, please see KrishnaswamyLab/PHATE.
Table of Contents
Installation
In order to use PHATE in R, you must also install the Python package.
If python
or pip
are not installed, you will need to install them.
We recommend Miniconda3 to install
Python and pip
together, or otherwise you can install pip
from
https://pip.pypa.io/en/stable/installing/.
Installation from CRAN and PyPi
First install phate
in Python by running the following code from a
terminal:
pip install --user phate
Then install phateR
from CRAN by running the following code in R:
install.packages("phateR")
Installation with devtools
and reticulate
The development version of PHATE can be installed directly from R with
devtools
:
if (!suppressWarnings(require(devtools))) install.packages("devtools")
reticulate::py_install("phate", pip=TRUE)
devtools::install_github("KrishnaswamyLab/phateR")
Installation from source
The latest source version of PHATE can be accessed by running the following in a terminal:
git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git
cd PHATE/Python
python setup.py install --user
cd ../phateR
R CMD INSTALL
If the phateR
folder is empty, you have may forgotten to use the
--recursive
option for git clone
. You can rectify this by running
the following in a terminal:
cd PHATE
git submodule init
git submodule update
cd Python
python setup.py install --user
cd ../phateR
R CMD INSTALL
Quick Start
If you have loaded a data matrix data
in R (cells on rows, genes on
columns) you can run PHATE as follows:
library(phateR)
data_phate <- phate(data)
phateR accepts R matrices, Matrix
sparse matrices, data.frame
s, and
any other data type that can be converted to a matrix with the function
as.matrix
.
Tutorial
This is a basic example running phate
on a highly branched example
dataset that is included with the package. You can read a tutorial on
running PHATE on single-cell RNA-seq at
http://htmlpreview.github.io/?https://github.com/KrishnaswamyLab/phateR/blob/master/inst/examples/bonemarrow_tutorial.html
or in inst/examples
. Running this tutorial from start to finish should
take approximately 3 minutes.
First, let’s load the tree data and examine it with PCA.
library(phateR)
#> Loading required package: Matrix
data(tree.data)
plot(prcomp(tree.data$data)$x, col=tree.data$branches)
<img src="man/figures/README-example-data-1.png" width="100%" />
Now we run PHATE on the data. We’ll just go ahead and try with the default parameters.
# runs phate
tree.phate <- phate(tree.data$data)
summary(tree.phate)
#> PHATE embedding
#> k = 5, alpha = 40, t = auto
#> Data: (3000, 100)
#> Embedding: (3000, 2)
Let’s plot the results.
# plot embedding
palette(rainbow(10))
plot(tree.phate, col = tree.data$branches)
<img src="man/figures/README-plot-results-1.png" width="100%" />
Good news! Our branches separate nicely. However, most of the
interesting activity seems to be concentrated into one region of the
plot - in this case we should try the square root potential instead by
using gamma=0
. We can also try increasing t
to make the structure a
little clearer - in this case, because synthetic data in unusually
structured, we can use a very large value, like 120, but in biological
data the automatic t
selection is generally very close to ideal. Note
here that if we pass our previous result in with the argument init
, we
won’t have to recompute the diffusion operator.
# runs phate with different parameters
tree.phate <- phate(tree.data$data, gamma=0, t=120, init=tree.phate)
# plot embedding
palette(rainbow(10))
plot(tree.phate, col = tree.data$branches)
<img src="man/figures/README-adjust-parameters-1.png" width="100%" />
We can also pass the PHATE object directly to ggplot
, if it is
installed.
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 3.5.3
ggplot(tree.phate, aes(x=PHATE1, y=PHATE2, color=tree.data$branches)) +
geom_point()
<img src="man/figures/README-ggplot-1.png" width="100%" />
Issues
FAQ
- Should genes (features) by rows or columns?
To be consistent with common dimensionality reductions such as PCA
(stats::prcomp
) and t-SNE (Rtsne::Rtsne
), we require that cells
(observations) be rows and genes (features) be columns of your input
data.
- Can I run PHATE with Seurat?
PHATE was removed from Seurat in version 3. You can install a version of Seurat with RunPHATE
included by following the instructions at https://github.com/satijalab/seurat/pull/1172#issuecomment-564782167.
- I have installed PHATE in Python, but phateR says it is not installed!
Check your reticulate::py_discover_config("phate")
and compare it to
the version of Python in which you installed PHATE (run which python
and which pip
in a terminal.) Chances are reticulate
can’t find the
right version of Python; you can fix this by adding the following line
to your ~/.Renviron
:
PATH=/path/to/my/python
You can read more about Renviron
at
https://CRAN.R-project.org/package=startup/vignettes/startup-intro.html.
Help
Please let us know of any issues at the GitHub
repository. If you
have any questions or require assistance using PHATE, please read the
documentation at https://CRAN.R-project.org/package=phateR/phateR.pdf
or by running help(phateR::phate)
or contact us at
https://krishnaswamylab.org/get-help.