monocle_UMAP: Uniform Manifold Approximation and Projection in Monocle 3...

In jacobheng/cellwrangler: Supplemental toolkit for single-cell RNAseq analysis

Description

Monocle 3 alpha's implementation of UMAP. Finds a low dimensional embedding of the data that approximates an underlying manifold. This functions relies on the python implementation of UMAP (https://github.com/lmcinnes/umap). This function is a pass through to the python code. Documentation is reproduced from the UMAP package here for convenience. The original publication of UMAP can be found here: https://arxiv.org/abs/1802.03426 (McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018). A useful notebook (written in python) to check for the effects of each parameter in UMAP can be found here: https://nbviewer.jupyter.org/github/CrakeNotSnowman/umapNotebooks/blob/master/UMAP

Usage

monocle_UMAP(X, python_home = system("which python", intern = TRUE),
  log = TRUE, n_neighbors = 15L, n_component = 2L,
  metric = "correlation", n_epochs = NULL, negative_sample_rate = 5L,
  learning_rate = 1, init = "spectral", min_dist = 0.1, spread = 1,
  set_op_mix_ratio = 1, local_connectivity = 1L,
  repulsion_strength = 1, a = NULL, b = NULL, random_state = 0L,
  metric_kwds = reticulate::dict(), angular_rp_forest = FALSE,
  target_n_neighbors = -1L, target_metric = "categorical",
  target_metric_kwds = reticulate::dict(), target_weight = 0.5,
  transform_seed = 42L, verbose = FALSE, return_all = FALSE)

Arguments

X	the dataset upon which to perform umap dimension reduction
python_home	The python home directory where umap is installed
log	A logic argument to determine whether we need to calculate log of the input data. Default to be true
n_neighbors	float (optional, default 15) The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100.
n_component	int (optional, default 2) The dimension of the space to embed into. This defaults to 2 to provide easy visualization, but can reasonably be set to any integer value in the range 2 to 100.
metric	string or function (optional, default 'correlation') The metric to use to compute distances in high dimensional space. If a string is passed it must match a valid predefined metric. Valid string metrics include: * euclidean * manhattan * chebyshev * minkowski * canberra * braycurtis * mahalanobis * wminkowski * seuclidean * cosine * correlation * haversine * hamming * jaccard * dice * russelrao * kulsinski * rogerstanimoto * sokalmichener * sokalsneath * yule Metrics that take arguments (such as minkowski, mahalanobis etc.) can have arguments passed via the metric_kwds dictionary. At this time care must be taken and dictionary elements must be ordered appropriately; this will hopefully be fixed in the future.
n_epochs	int The number of training epochs to be used in optimizing the low dimensional embedding. Larger values result in more accurate embeddings. If None is specified a value will be selected based on the size of the input dataset (200 for large datasets, 500 for small).
negative_sample_rate	int (optional, default 5) The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding.
learning_rate	float (optional, default 1.0) The initial learning rate for the embedding optimization.
init	string (optional, default 'spectral') How to initialize the low dimensional embedding. Options are: * 'spectral': use a spectral embedding of the fuzzy 1-skeleton * 'random': assign initial embedding positions at random. * A numpy array of initial embedding positions.
min_dist	float (optional, default 0.1) The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the “spread“ value, which determines the scale at which embedded points will be spread out.
spread	float (optional, default 1.0) The effective scale of embedded points. In combination with “min_dist“ this determines how clustered/clumped the embedded points are.
set_op_mix_ratio	float (optional, default 1.0) Interpolate between (fuzzy) union and intersection as the set operation used to combine local fuzzy simplicial sets to obtain a global fuzzy simplicial sets. Both fuzzy set operations use the product t-norm. The value of this parameter should be between 0.0 and 1.0; a value of 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy intersection.
local_connectivity	int (optional, default 1) The local connectivity required – i.e. the number of nearest neighbors that should be assumed to be connected at a local level. The higher this value the more connected the manifold becomes locally. In practice this should be not more than the local intrinsic dimension of the manifold.
repulsion_strength	float (optional, default 1.0) Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples.
a	float (optional, default None) (not passed in) More specific parameters controlling the embedding. If None these values are set automatically as determined by “min_dist“ and “spread“.
b	float (optional, default None) (not passed in) More specific parameters controlling the embedding. If None these values are set automatically as determined by “min_dist“ and “spread“.
random_state	int, RandomState instance or None, optional (default: None) (not passed in) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by 'np.random'.
metric_kwds	dict (optional, default ) (not passed in) Arguments to pass on to the metric, such as the “p“ value for Minkowski distance. In R, a list should be passed in if you want to use this argument. The dict function from reticulate package will then convert it into a dictionary for python to use.
angular_rp_forest	bool (optional, default False) Whether to use an angular random projection forest to initialise the approximate nearest neighbor search. This can be faster, but is mostly on useful for metric that use an angular style distance such as cosine, correlation etc. In the case of those metrics angular forests will be chosen automatically.
target_n_neighbors	int (optional, default -1) The number of nearest neighbors to use to construct the target simplcial set. If set to -1 use the “n_neighbors“ value.
target_metric	string or callable (optional, default 'categorical') The metric used to measure distance for a target array is using supervised dimension reduction. By default this is 'categorical' which will measure distance in terms of whether categories match or are different. Furthermore, if semi-supervised is required target values of -1 will be trated as unlabelled under the 'categorical' metric. If the target array takes continuous values (e.g. for a regression problem) then metric of 'l1' or 'l2' is probably more appropriate.
target_metric_kwds	dict (optional, default None) Keyword argument to pass to the target metric when performing supervised dimension reduction. If None then no arguments are passed on.
target_weight	float (optional, default 0.5) weighting factor between data topology and target topology. A value of 0.0 weights entirely on data, a value of 1.0 weights entirely on target. The default of 0.5 balances the weighting equally between data and target.
transform_seed	int (optional, default 42) Random seed used for the stochastic aspects of the transform operation. This ensures consistency in transform operations.
verbose	bool (optional, default False) Controls verbosity of logging.
return_all	Whether to return all slots after UMAP

Value

Embedding of the training data in low-dimensional space if return_all is set to be FALSE, otherwise the object returned from umap function, including the following elements: a, fit_transform, metric, random_state, alpha, gamma, metric_kwds, set_op_mix_ratio, angular_rp_forest, get_params, min_dist, set_params, b, graph, n_components, spread, bandwidth, init, n_epochs, verbose, embedding_, initial_alpha, n_neighbors, fit, local_connectivity, negative_sample_rate

jacobheng/cellwrangler documentation built on Aug. 12, 2019, 6:49 a.m.