run.umap | R Documentation |
This function takes an object of class iCellR and runs UMAP on PCA data.
run.umap(
x = NULL,
my.seed = 0,
dims = 1:10,
n_neighbors = 15,
n_components = 2,
metric = "euclidean",
n_epochs = NULL,
learning_rate = 1,
scale = FALSE,
init = "spectral",
init_sdev = NULL,
spread = 1,
min_dist = 0.01,
set_op_mix_ratio = 1,
local_connectivity = 1,
bandwidth = 1,
repulsion_strength = 1,
negative_sample_rate = 5,
a = NULL,
b = NULL,
nn_method = NULL,
n_trees = 50,
search_k = 2 * n_neighbors * n_trees,
approx_pow = FALSE,
y = NULL,
target_n_neighbors = n_neighbors,
target_metric = "euclidean",
target_weight = 0.5,
pca = NULL,
pca_center = TRUE,
pcg_rand = TRUE,
fast_sgd = FALSE,
ret_model = FALSE,
ret_nn = FALSE,
n_threads = 1,
n_sgd_threads = 0,
grain_size = 1,
tmpdir = tempdir(),
verbose = getOption("verbose", TRUE)
)
x |
An object of class iCellR. |
my.seed |
seed number, default = 0. |
dims |
PC dimentions to be used for UMAP analysis. |
n_neighbors |
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
|
n_components |
The dimension of the space to embed into. This defaults
to |
metric |
Type of distance metric to use to find nearest neighbors. One of:
Only applies if If Each metric calculation results in a separate fuzzy simplicial set, which are intersected together to produce the final set. Metric names can be repeated. Because non-numeric columns are removed from the data frame, it is safer to use column names than integer ids. Factor columns can also be used by specifying the metric name
For a given data block, you may override the |
n_epochs |
Number of epochs to use during the optimization of the
embedded coordinates. By default, this value is set to |
learning_rate |
Initial learning rate used in optimization of the coordinates. |
scale |
Scaling to apply to
For UMAP, the default is |
init |
Type of initialization for the coordinates. Options are:
For spectral initializations, ( |
init_sdev |
If non- |
spread |
The effective scale of embedded points. In combination with
|
min_dist |
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 |
set_op_mix_ratio |
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 |
local_connectivity |
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. |
bandwidth |
The effective bandwidth of the kernel if we view the algorithm as similar to Laplacian Eigenmaps. Larger values induce more connectivity and a more global view of the data, smaller values concentrate more locally. |
repulsion_strength |
Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples. |
negative_sample_rate |
The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding. |
a |
More specific parameters controlling the embedding. If |
b |
More specific parameters controlling the embedding. If |
nn_method |
Method for finding nearest neighbors. Options are:
By default, if
Multiple nearest neighbor data (e.g. from two different precomputed
metrics) can be passed by passing a list containing the nearest neighbor
data lists as items.
The |
n_trees |
Number of trees to build when constructing the nearest
neighbor index. The more trees specified, the larger the index, but the
better the results. With |
search_k |
Number of nodes to search during the neighbor retrieval. The
larger k, the more the accurate results, but the longer the search takes.
With |
approx_pow |
If |
y |
Optional target data for supervised dimension reduction. Can be a
vector, matrix or data frame. Use the
Unlike |
target_n_neighbors |
Number of nearest neighbors to use to construct the
target simplicial set. Default value is |
target_metric |
The metric used to measure distance for |
target_weight |
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. Only applies if |
pca |
If set to a positive integer value, reduce data to this number of
columns using PCA. Doesn't applied if the distance |
pca_center |
If |
pcg_rand |
If |
fast_sgd |
If |
ret_model |
If |
ret_nn |
If |
n_threads |
Number of threads to use. |
n_sgd_threads |
Number of threads to use during stochastic gradient
descent. If set to > 1, then results will not be reproducible, even if
'set.seed' is called with a fixed seed before running. Set to
|
grain_size |
Minimum batch size for multithreading. If the number of
items to process in a thread falls below this number, then no threads will
be used. Used in conjunction with |
tmpdir |
Temporary directory to store nearest neighbor indexes during
nearest neighbor search. Default is |
verbose |
If |
An object of class iCellR.
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