This step is required for PARC and TRIMAP, which are included in our FCSExpress Anaconda environment. It may also be required for other libraries.

• Go to this link: https://visualstudio.microsoft.com/visual-cpp-build-tools/ and click on Download Build Tools.
• Double-click on Vs_Buildtools.exe (it may be in your computer’s "Downloads" folder).
  • If asked to "Allow Installer to make changes", click Yes.
  • If you got a message about privacy, click Continue.
• From the Workloads tab (see screenshot below), choose Desktop development with C++.
• Under Optional components in the right pane (see screenshot below), only MSVC… and Windows 10 SDK (or Windows 11 SDK) are necessary. You may “uncheck” the others to save disk space.
• Click Install.

• This will take about 4 minutes to install and about 4.51 GB of space (8.15 GB on Windows 11) are required.
• When done, exit the Visual Studio Installer window.
• If you are prompted to restart your computer, please do so.
• You may now go on to the next step of Installing Anaconda, Python and Required Packages, found below.

Skip this step if you already have Python and the desired libraries installed on your computer.

If Anaconda is already installed on your computer, skip to the step below called "Launch Anaconda Navigator".

A convenient way to install Python is installing Anaconda, which is a distribution of Python for scientists (3.5 GB space is required).

• Follow the steps at the link below. You do not need to log in or sign up at any point when installing Anaconda and Python. If you are prompted to login or sign up, exit the pop-up window.
  • How to download and install Anaconda on Windows

Once Anaconda is installed, we can create an environment to work with FCS Express. An environment is a collection of libraries of interest. Environments simplify library installations, avoid system pollution, sidestep dependency conflicts and minimize reproducibility Issues.

• Launch Anaconda Navigator from the Windows Start Menu

• Wait for the software to finish loading (wait for the blue animation to cease).

• If Anaconda asks to update, click Yes.

• If prompted to quit Anaconda, click Yes.

• If prompted again to update, click Update now.

• When the update is complete, click Launch Navigator.

• Click Environments.

• Download the FCSExpress environment you need:

• In Anaconda > Environments, click Import.

• Select Local drive and click on the File Folder icon next to it
• Find the FCSExpress_v#.yml file that you downloaded from the website. It may be in your computer’s "Downloads" folder.
• Click Open.
• Click Import.
• The FCSExpress environment has now been loaded into your Anaconda Navigator.

If the Python directory you wish to use in FCS Express is the Default, Registered Python on your computer, you may skip this step. The Default, Registered Python can be found in Windows RegistryEditor > Computer\HKEY_CURRENT_USER\Software\Python\PythonCore\3.X\InstallPath

• Launch FCS Express version 7.08.0018 or later.
• Open a New Layout
• Click File > Options.
• In the left pane, click Files/Directories
• Under Directory for Python (if not using Default, Registered Python), enter the path where the pythonXXX.dll is located for the environment you wish to use.

• • If you have installed Python via Anaconda, (and wish to use the FCSExpress environment) enter the path of your FCSExpress Anaconda environment into your FCS Express user option (Files/Directories > Directory for Python)
    • The path is found by hovering over the Anaconda environment (i.e. C:\Users\krittenbach\Anaconda3\envs\FCSExpress in the screenshot below)

• • • If desired, the Python directory (for an Anaconda environment) can alternatively be found and copied by the following steps:
      • Click on the desired environment name in the Anaconda Navigator.
      • Click the green "play" button next to the environment name.
      • Click Open Terminal
      • Enter the following text: where python and press the Enter key
      • Copy the path that contains your environment (i.e. C:\Users\krittenbach\Anaconda3\envs\FCSExpress)

• • • • Paste the path into the FCS Express user option (Files/Directories > Directory for Python)

• • If you installed Python from python.org, the path is likely similar to: C:\Users\XXXXXXXX\AppData\Local\Programs\Python\PythonXXX

• In FCS Express, click OK to apply the new user option.
• You can now try using the example scripts (provided on this page) in FCS Express.

1. Once a Pipeline is created, the Python transformation pipeline step can be added from the + > Miscellaneous category.
2. If the pipeline is already applied to a plot, you may get the warning below asking whether you want to disable the automatic execution of the pipeline and control the execution by the "Execute Transformation Pipeline" button instead.
3. The Python Transformation pipeline steps will be added to the pipeline.
4. Select the input parameters of interest.
5. From one of the example scripts included on this webpage (below), highlight all of the text beginning with the "#". Press Ctrl+C or right-click>Copy.
6. In the Python Script text box, right right-click and choose Clear Script to clear the example script .
7. Ensure the cursor is in the left-most position of the first line. To confirm this, press the Home button on your keyboard.
8. In FCS Express, click inside the Python Script text box and press  Ctrl+V to paste the script. Note: Right-Click > Paste is not available.
9. Click Save Script.
10. The User Defined Options section of the Python Transformation step will then be populated based on the script code.
11. Drag the Pipeline from the Transformations window onto the plot in the layout.
12. If you do not have "Automatically Run Pipeline" checked, press Execute Transformation Pipeline in the pipeline root step.
13. Running the pipeline may take a few seconds or several minutes, depending on the file size, chosen script and computer power.
14. Once the transformation is applied to a plot and the run is completed, the output parameters generated by the pipeline  will be accessible as any other classical parameters by clicking on the X and Y axis title of the plot of interest (e.g. Trimap 1 and Trimap 2 in the screenshot below).

Note: When the script is done running, the title of your plot will say "Pipeline transformed". In case your plot title does not contain the filename, you can check if the transformation is applied in the Overlay formatting dialog for that plot.

In this example script, the Python Transformation pipeline step is defined to run PARC (PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells; Shobana V Stassen; Bioinformatics, Volume 36, Issue 9, 1 May 2020).

Note: to run this script, Python, pandas and parc should be properly installed on the user computer.

#This script allows you to run PARC as a Python Transformation pipeline step in FCS Express 7)
#Detailed info on PARC in Python can be found at https://github.com/ShobiStassen/PARC

from FcsExpress import *
try:
    from pandas import DataFrame
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pandas. " +
        "To test this script please install pandas: " +
        "(https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)")

try:
    import parc
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain parc. " +
        "To test this script please install parc: " +
        "(https://github.com/ShobiStassen/PARC)")

def RegisterOptions(params):
    RegisterIntegerOption("dist_std_local",2)
    RegisterStringOption("jac_std_global","median")
    RegisterIntegerOption("resolution_parameter",1)

def RegisterParameters(opts, params):
    RegisterClassification("Parc Cluster Assignments")

def Execute(opts, data, res):
    df = DataFrame(data)

    #Function to check whether an object is float
    def isfloat(num):
        try:
            float(num)
            return True
        except ValueError:
            return False

    #checking whether opts["jac_std_global"] is set to median or whether it is float
    if opts["jac_std_global"]=="median":
        jac_std_global="median"
    elif isfloat(opts["jac_std_global"])==True:
        jac_std_global=float(opts["jac_std_global"])
    else:
        raise Exception("The jac_std_global only accept median or numerical values")

    print("Running PARC with dist_std_local {}, jac_std_global {}, resolution_parameter {}, on {} data points".format(opts["dist_std_local"],jac_std_global,opts["resolution_parameter"],NumberDataPoints))
    parc_obj = parc.PARC(df.to_numpy(),dist_std_local=opts["dist_std_local"],jac_std_global=jac_std_global,resolution_parameter=opts["resolution_parameter"]) #initiate PARC
    parc_obj.run_PARC() #run the clustering
    res["Parc Cluster Assignments"] = parc_obj.labels #saving the cluster assignment into the res object
    return res #return the res object that will be re-imported into FCS Express

In this example script, the Python Transformation pipeline step is defined to run HDBSCAN (Campello R.J.G.B., Moulavi D., Sander J. (2013) Density-Based Clustering Based on Hierarchical Density Estimates. In: Pei J., Tseng V.S., Cao L., Motoda H., Xu G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_14).

HDBSCAN can be useful to identify clusters on UMAP plots. When HDBSCA is used for this purpose, creating a UMAP map with dense clusters (i.e. keeping the UMAP "Min Low Dim Distance" value low) will help HDBSCAN clustering.

More information on DBSCAN for Python can be found here.

Note #1: to run this script, Python, pandas, and hdbscan should be properly installed on the user computer.

Note #2: If noisy data points are detected by HDBSCA, they will be grouped under "HDBSCAN Cluster Assignment" equal to "1" in FCS Express.

Note #3: with large dataset, HDBSCAN might return a "Could not find -c" error. If that happens, setting core_dist_n_jobs to 1 might solve the issue.

#HDBSCAN
from FcsExpress import *

try:
    from pandas import DataFrame
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pandas. " +
        "To test this script please install pandas: " +
        "(https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)")

try:
    import hdbscan
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain hdbscan. " +
        "To test this script please install hdbscan: " +
        "(https://pypi.org/project/hdbscan/)")

from collections import Counter

def RegisterOptions(params):
    RegisterIntegerOption("min_cluster_size",5)
    RegisterStringOption("min_samples","None")
    RegisterIntegerOption("core_dist_n_jobs",4)

def RegisterParameters(opts, params):
    RegisterClassification("HDBSCAN Cluster Assignments")
    RegisterParameter("HDBSCAN Outlier scores")

def Execute(opts, data, res):
    df = DataFrame(data)

    #See if doing default min_samples
    if opts["min_samples"] == "None":
        min_samples = None # Setting n_neighbors to "None" leads to a default choice
    elif opts["min_samples"].isnumeric():
        min_samples = int(opts["min_samples"])
    else:
        raise Exception("The min_samples option only accept None or integer values")

    print("Running HDBSCAN on {} data points, with min_cluster_size={}, min_samples={} and core_dist_n_jobs={}".format(NumberDataPoints,opts["min_cluster_size"],min_samples,opts["core_dist_n_jobs"]))

    clusterer = hdbscan.HDBSCAN(min_cluster_size=opts["min_cluster_size"],
                                min_samples=min_samples,
                                core_dist_n_jobs=opts["core_dist_n_jobs"])
    cluster_labels = clusterer.fit_predict(df)
    outlier_scores=clusterer.outlier_scores_

    #Noisy data points are labelled by HDBScan with cluster assignemnt of -1.
    #FCS Express requires cluster assignment values starting from 0.
    #So, in case noisy data points are present, cluster labels are increased by 1
    if min(cluster_labels)==-1:
        noisy=Counter(cluster_labels)[-1] #number of noisy data points
        print(noisy, "noisy datapoints have been detected" +
            " and will be grouped under HDBSCAN Cluster Assignment equal to 1 in FCS Express")
        cluster_labels=cluster_labels+1 # making cluster assignment values starting at 0.

    res["HDBSCAN Cluster Assignments"] = cluster_labels
    res["HDBSCAN Outlier scores"] = outlier_scores
    return res

In this example script, the Python Transformation pipeline step is defined to run TriMap (TriMap: Large-scale Dimensionality Reduction Using Triplets; Ehsan Amid, Manfred K. Warmuth; arXiv:1910.00204; manuscript submitted on 1st Oct 2019 but rejected on 20th Dec 2019).

Note: to run this script, Python, pandas and trimap should be properly installed on the user computer.

#This script allow to run TriMap as a Python Transformation pipeline step in FCS Express 7
#Detailed info on TriMap in Python can be found at https://pypi.org/project/trimap/

from FcsExpress import *
try:
    from pandas import DataFrame
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pandas. " +
        "To test this script please install pandas: " +
        "(https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)")

try:
    import trimap
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain trimap. " +
        "To test this script please install trimap: " +
        "(https://pypi.org/project/trimap/)")

parameter_map = {
    "n_inliers": "Number Nearest Neighbors",
    "n_outliers": "Number Outliers",
    "n_random": "Number Random Triplets",
    "distance": "Distance Measure",
    "weight_adj": "Gamma",
    "lr": "Learning Rate",
    "n_iters": "Number Iterations"
}

def RegisterOptions(params):
    RegisterIntegerOption(parameter_map["n_inliers"], 10)
    RegisterIntegerOption(parameter_map["n_outliers"], 5)
    RegisterIntegerOption(parameter_map["n_random"], 5)
    RegisterStringOption(parameter_map["distance"], "euclidean")
    RegisterFloatOption(parameter_map["weight_adj"], 500.0)
    RegisterFloatOption(parameter_map["lr"], 1000.0)
    RegisterIntegerOption(parameter_map["n_iters"], 400)

def RegisterParameters(opts, params):
    RegisterParameter("Trimap 1")
    RegisterParameter("Trimap 2")

def Execute(opts, data, res):
    df = DataFrame(data)
    embedding = trimap.TRIMAP(
        n_inliers=opts[parameter_map["n_inliers"]],
        n_outliers=opts[parameter_map["n_outliers"]],
        n_random=opts[parameter_map["n_random"]],
        distance=opts[parameter_map["distance"]],
        weight_adj=opts[parameter_map["weight_adj"]],
        lr=opts[parameter_map["lr"]],
        n_iters=opts[parameter_map["n_iters"]]
    ).fit_transform(df.to_numpy())
    res["Trimap 1"] = embedding[:, 0]
    res["Trimap 2"] = embedding[:, 1]
    return res

In this example script, the Python Transformation pipeline step is defined to run openTSNE (openTSNE: A Modular Python Library for t-SNE Dimensionality Reduction and Embedding. Poličar, P.G., Stražar, M. and Zupan, B. Journal of Statistical Software. 109, 3 (May 2024), 1–30. DOI:https://doi.org/10.18637/jss.v109.i03).

Note: to run this script, Python, pandas and openTSNE should be properly installed on the user computer.

#This script allows you to run openTSNE as a Python Transformation pipeline step in FCS Express 7 #Detailed info on openTSNE in Python can be found at https://opentsne.readthedocs.io/en/latest/index.html

from FcsExpress import *

try:
    from pandas import DataFrame
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pandas. " +
        "To test this script please install pandas: " +
        "(https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)")

try:
    from openTSNE import TSNE
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain OpenTSNE. " +
        "To test this script please install openTSNE: " +
        "(https://opentsne.readthedocs.io/en/latest/installation.html)")

def RegisterOptions(params):
    RegisterIntegerOption("Number Iterations", 500)
    RegisterIntegerOption("Perplexity", 30)
    RegisterFloatOption("Burnes-Hut Theta", 0.5)
    RegisterStringOption("Learning Rate", "auto")
    RegisterStringOption("Metric", "euclidean")
    RegisterIntegerOption("Random State", 42)
    RegisterIntegerOption("Number Threads", -1) #-1 means all processors
    RegisterStringOption("Initialization","pca")

def RegisterParameters(opts, params):
    RegisterParameter("openTSNE 1")
    RegisterParameter("openTSNE 2")

def Execute(opts, data, res):
    df = DataFrame(data)

    # See if doing auto learning
    if opts["Learning Rate"] == "auto":
        l_rate = 'auto' # Auto learning will be used
    elif opts["Learning Rate"].isnumeric():
        l_rate = float(opts["Learning Rate"])
    else:
        raise Exception("The Learning Rate option only accept auto or floating values")

    #Check initialization
    Initializations=["pca","spectral","random"]
    if opts["Initialization"] not in Initializations:
        raise Exception("The Initialization option only accept the following values: pca, spectral, random")

    #Run TSNE
    embedding = TSNE(
        theta=opts["Burnes-Hut Theta"],
        perplexity=opts["Perplexity"],
        n_iter=opts["Number Iterations"],
        learning_rate=l_rate,
        metric=opts["Metric"],
        random_state=opts["Random State"],
        n_jobs=opts["Number Threads"],
        initialization=opts["Initialization"]
    ).fit(df.to_numpy())

    print("Running TSNE using {} processors, with {} Initialization on {} data points, with Number of Iterations = {}, Perplexity = {}, Theta = {}, Learning Rate = {}, Metric = {}, Random State = {}".format(opts["Number Threads"],opts["Initialization"], NumberDataPoints, opts["Number Iterations"], opts["Perplexity"],opts["Burnes-Hut Theta"],l_rate,opts["Metric"],opts["Random State"]))

    res["openTSNE 1"] = embedding[:, 0]
    res["openTSNE 2"] = embedding[:, 1]

    return res

In this example script, the Python Transformation pipeline step is defined to run PaCMAP (Wang and Huang, at al., Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization; Journal of Machine Learning Research 22 (2021) 1-73). More information on PaCMAP for Python can be found here.

Note: to run this experiment, Python, pandas and pacmap should be properly installed on the user computer.

#This script allows you to run PaCMAP as a Python Transformation pipeline step in FCS Express 7
#Detailed info on PaCMAP in Python can be found at https://github.com/YingfanWang/PaCMAP

from FcsExpress import *
try:
    from pandas import DataFrame
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pandas. " +
        "To test this script please install pandas: " +
        "(https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)")

try:
    import pacmap
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pacmap. " +
        "To test this script please install pacmap: " +
        "(https://github.com/YingfanWang/PaCMAP)")

def RegisterOptions(params):
    RegisterIntegerOption("Number of dimensions", 2)
    RegisterStringOption("Number of neighbors", "None")
    RegisterFloatOption("MN_ratio", 0.5)
    RegisterFloatOption("FP_ratio", 2.0)
    RegisterStringOption("Initialization","pca")
    RegisterIntegerOption("Random seed", 4)
    RegisterIntegerOption("Number of iterations", 450)

def RegisterParameters(opts, params):
    for dim in range(opts["Number of dimensions"]):
        RegisterParameter("PaCMAP {}".format(dim + 1))

def Execute(opts, data, res):

#Reading data
    df = DataFrame(data)
    X = df.to_numpy()

    #See if doing default n_neighbors
    if opts["Number of neighbors"] == "None":
        n_neighbors = None # Setting n_neighbors to "None" leads to a default choice
    elif opts["Number of neighbors"].isnumeric():
        n_neighbors = int(opts["Number of neighbors"])
    else:
        raise Exception("The Number of neighbors option only accept None or integer values")

    #Check initialization
    Initializations=["pca","random"]
    if opts["Initialization"] not in Initializations:
        raise Exception("The Initialization option only accept the following values: pca, random")

    #Initializing the pacmap instance
    embedding = pacmap.PaCMAP(
    n_components=opts["Number of dimensions"],
    n_neighbors=n_neighbors,
    MN_ratio=opts["MN_ratio"],
    FP_ratio=opts["FP_ratio"],
    random_state=opts["Random seed"],
    num_iters=opts["Number of iterations"],
    verbose=True)

    #Running PaCMAP
    X_transformed = embedding.fit_transform(X, init=opts["Initialization"])

    print("Running PaCMAP with {} Initialization on {} data points, with Number of Neighbors set to {}".format(opts["Initialization"], NumberDataPoints, n_neighbors))

    #Loop over dimensions and set results
    for dim in range(opts["Number of dimensions"]):
        res["PaCMAP {}".format(dim + 1)] = X_transformed[:, dim]

    return res

In this example script, the Python Transformation pipeline step is defined to run Ivis (Szubert, B., Cole, J.E., Monaco, C. et al. Structure-preserving visualisation of high dimensional single-cell datasets. Sci Rep 9,8914 (2019). https://doi.org/10.1038/s41598-019-45301-0). More information on Ivis for Python can be found here and here.

Note: to run this script, Python, pandas and ivis should be properly installed on the user computer.

#This script allows you to run Ivis as a Python Transformation pipeline step in FCS Express 7
#Detailed info on Ivis in Python can be found at https://bering-ivis.readthedocs.io/en/latest/python_package.html

from FcsExpress import *

try:
    from pandas import DataFrame
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pandas. " +
        "To test this script please install pandas: " +
        "(https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)")

try:
    from ivis import Ivis
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain ivis. " +
    "To test this script please install ivis: " +
    "(https://github.com/beringresearch/ivis)")

try:
    from scipy import sparse
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain scipy. " +
        "To test this script please install scypy: " +
        "(https://scipy.org/)")

def RegisterOptions(params):
    RegisterIntegerOption("Embedding Dimensions",2)
    RegisterIntegerOption("Number of Nearest Neighbours",15)
    RegisterIntegerOption("Number of epochs without progress",10)
    RegisterStringOption("Type of input", "numpy array")
    RegisterStringOption("Keras Model", "auto")

#The Number of Nearest Neighbours , the Number of epochs without progress
#and the Keras Model are tunable parameters that should be selected
#on the basis of dataset size and complexity.
#Please refer to https://bering-ivis.readthedocs.io/en/latest/hyperparameters.html

def RegisterParameters(opts, params):
    for dim in range(opts["Embedding Dimensions"]):
        RegisterParameter("Ivis {}".format(dim + 1))

def Execute(opts, data, res):
    df = DataFrame(data)
    #Converting the input data to either numpy array or spare matrix based on the user selection
    if opts["Type of input"]=="numpy array":
        X=df.to_numpy()
    elif opts["Type of input"]=="sparse matrix":
        X=sparse.csr_matrix(df)
    else:
        raise Exception("The Type of input option only accept the following values: numpy array, sparse matrix")

    # Determine model. For more details, see:
    # https://bering-ivis.readthedocs.io/en/latest/hyperparameters.html
    models = ["maaten","szubert","hinton"]
    if opts["Keras Model"]=="auto":
        if NumberDataPoints > 500000:
            keras_model = "szubert"
        else:
            keras_model = "maaten"
    elif opts["Keras Model"] in models:
        keras_model = opts["Keras Model"]
    else:
        raise Exception("The Model option only accept the following values: auto, maaten, szubert, hinton")

    print("Running Ivis with keras model {} on {} data points, using {} as input data type".format(keras_model, NumberDataPoints,opts["Type of input"]))

    # Set ivis parameters
    model = Ivis(embedding_dims = opts["Embedding Dimensions"],
                 k = opts["Number of Nearest Neighbours"],
                 n_epochs_without_progress = opts["Number of epochs without progress"],
                 model = keras_model
                 )
    embeddings = model.fit_transform(X) # Generate embeddings
    #Loop over dimensions and set results
    for dim in range(opts["Embedding Dimensions"]):
        res["Ivis {}".format(dim + 1)] = embeddings[:, dim]

    return res #return the res object that will be re-imported into FCS Express

In this example script, the Python Transformation pipeline step is defined to run Phate (Moon, K.R., van Dijk, D., Wang, Z. et al. Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol 37, 1482–1492 (2019). https://doi.org/10.1038/s41587-019-0336-3). More information on Phate for Python can be found here.

Note: to run this script, Python, pandas and phate should be properly installed on the user computer.

#This script allows you to run Phate as a Python Transformation pipeline step in FCS Express 7.

#Detailed info on Phate in Python can be found at https://pypi.org/project/phate/

from FcsExpress import *
try:
    from pandas import DataFrame
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain Pandas. " +
        "To test this script please install pandas: " +
        "(https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)")

try:
    import phate
except ModuleNotFoundError:
    raise RuntimeError("Current Python distribution does not contain phate. " +
        "To test this script please install phate: " +
        "(https://pypi.org/project/phate/)")

parameter_map = {
    "knn": "Number Nearest Neighbors",
    "n_components": "Number of dimensions"
    }

def RegisterOptions(params):
    #The full list of available parameters can be found at #https://phate.readthedocs.io/en/stable/api.html
    RegisterIntegerOption(parameter_map["knn"], 5)
    RegisterIntegerOption(parameter_map["n_components"], 2)

def RegisterParameters(opts, params):
    for dim in range(opts[parameter_map["n_components"]]):
        RegisterParameter("Phate {}".format(dim + 1))

def Execute(opts, data, res):
    df = DataFrame(data)
    phate_operator = phate.PHATE(
        knn=opts[parameter_map["knn"]],
        n_components=opts[parameter_map["n_components"]]
        )

    df_phate = phate_operator.fit_transform(df)

    #Loop over dimensions and set results
    for dim in range(opts[parameter_map["n_components"]]):
        res["Phate {}".format(dim + 1)] = df_phate[:, dim]

    return res

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