Run UMAP dimension reduction — runUMAP • Giotto

run UMAP

runUMAP(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "feats"),
  dim_reduction_to_use = "pca",
  dim_reduction_name = NULL,
  dimensions_to_use = 1:10,
  name = NULL,
  feats_to_use = NULL,
  return_gobject = TRUE,
  n_neighbors = 40,
  n_components = 2,
  n_epochs = 400,
  min_dist = 0.01,
  n_threads = NA,
  spread = 5,
  set_seed = TRUE,
  seed_number = 1234L,
  verbose = TRUE,
  toplevel_params = 2L,
  ...
)

Arguments

gobject

giotto object

feat_type

feature type

spat_unit

spatial unit

expression_values

expression values to use

reduction

cells or genes

dim_reduction_to_use

use another dimension reduction set as input

dim_reduction_name

name of dimension reduction set to use

dimensions_to_use

number of dimensions to use as input

name

arbitrary name for UMAP run

feats_to_use

if dim_reduction_to_use = NULL, which features to use

return_gobject

logical: return giotto object (default = TRUE)

n_neighbors

UMAP param: number of neighbors

n_components

UMAP param: number of components

n_epochs

UMAP param: number of epochs

min_dist

UMAP param: minimum distance

n_threads

UMAP param: threads/cores to use

spread

UMAP param: spread

set_seed

use of seed

seed_number

seed number to use

verbose

verbosity of function

toplevel_params

parameters to extract

...

Arguments passed on to uwot::umap

metric

Type of distance metric to use to find nearest neighbors. For nn_method = "annoy" this can be one of:

"euclidean" (the default)
"cosine"
"manhattan"
"hamming"
"correlation" (a distance based on the Pearson correlation)
"categorical" (see below)

For nn_method = "hnsw" this can be one of:

"euclidean"
"cosine"
"correlation"

If rnndescent is installed and nn_method = "nndescent" is specified then many more metrics are avaiable, including:

"braycurtis"
"canberra"
"chebyshev"
"dice"
"hamming"
"hellinger"
"jaccard"
"jensenshannon"
"kulsinski"
"rogerstanimoto"
"russellrao"
"sokalmichener"
"sokalsneath"
"spearmanr"
"symmetrickl"
"tsss"
"yule"

For more details see the package documentation of rnndescent. For nn_method = "fnn", the distance metric is always "euclidean".

If X is a data frame or matrix, then multiple metrics can be specified, by passing a list to this argument, where the name of each item in the list is one of the metric names above. The value of each list item should be a vector giving the names or integer ids of the columns to be included in a calculation, e.g. metric = list(euclidean = 1:4, manhattan = 5:10).

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 "categorical". Factor columns are treated different from numeric columns and although multiple factor columns can be specified in a vector, each factor column specified is processed individually. If you specify a non-factor column, it will be coerced to a factor.

For a given data block, you may override the pca and pca_center arguments for that block, by providing a list with one unnamed item containing the column names or ids, and then any of the pca or pca_center overrides as named items, e.g. metric = list(euclidean = 1:4, manhattan = list(5:10, pca_center = FALSE)). This exists to allow mixed binary and real-valued data to be included and to have PCA applied to both, but with centering applied only to the real-valued data (it is typical not to apply centering to binary data before PCA is applied).

learning_rate

Initial learning rate used in optimization of the coordinates.

init

Type of initialization for the coordinates. Options are:

"spectral" Spectral embedding using the normalized Laplacian of the fuzzy 1-skeleton, with Gaussian noise added.
"normlaplacian". Spectral embedding using the normalized Laplacian of the fuzzy 1-skeleton, without noise.
"random". Coordinates assigned using a uniform random distribution between -10 and 10.
"lvrandom". Coordinates assigned using a Gaussian distribution with standard deviation 1e-4, as used in LargeVis (Tang et al., 2016) and t-SNE.
"laplacian". Spectral embedding using the Laplacian Eigenmap (Belkin and Niyogi, 2002).
"pca". The first two principal components from PCA of X if X is a data frame, and from a 2-dimensional classical MDS if X is of class "dist".
"spca". Like "pca", but each dimension is then scaled so the standard deviation is 1e-4, to give a distribution similar to that used in t-SNE. This is an alias for init = "pca", init_sdev = 1e-4.
"agspectral" An "approximate global" modification of "spectral" which all edges in the graph to a value of 1, and then sets a random number of edges (negative_sample_rate edges per vertex) to 0.1, to approximate the effect of non-local affinities.
A matrix of initial coordinates.

For spectral initializations, ("spectral", "normlaplacian", "laplacian", "agspectral"), if more than one connected component is identified, no spectral initialization is attempted. Instead a PCA-based initialization is attempted. If verbose = TRUE the number of connected components are logged to the console. The existence of multiple connected components implies that a global view of the data cannot be attained with this initialization. Increasing the value of n_neighbors may help.

init_sdev

If non-NULL, scales each dimension of the initialized coordinates (including any user-supplied matrix) to this standard deviation. By default no scaling is carried out, except when init = "spca", in which case the value is 0.0001. Scaling the input may help if the unscaled versions result in initial coordinates with large inter-point distances or outliers. This usually results in small gradients during optimization and very little progress being made to the layout. Shrinking the initial embedding by rescaling can help under these circumstances. Scaling the result of init = "pca" is usually recommended and init = "spca" as an alias for init = "pca", init_sdev = 1e-4 but for the spectral initializations the scaled versions usually aren't necessary unless you are using a large value of n_neighbors (e.g. n_neighbors = 150 or higher). For compatibility with recent versions of the Python UMAP package, if you are using init = "spectral", then you should also set init_sdev = "range", which will range scale each of the columns containing the initial data between 0-10. This is not set by default to maintain backwards compatibility with previous versions of uwot.

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 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

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 NULL these values are set automatically as determined by min_dist and spread.

b

More specific parameters controlling the embedding. If NULL these values are set automatically as determined by min_dist and spread.

nn_method

Method for finding nearest neighbors. Options are:

"fnn". Use exact nearest neighbors via the FNN package.
"annoy" Use approximate nearest neighbors via the RcppAnnoy package.
"hnsw" Use approximate nearest neighbors with the Hierarchical Navigable Small World (HNSW) method (Malkov and Yashunin, 2018) via the RcppHNSW package. RcppHNSW is not a dependency of this package: this option is only available if you have installed RcppHNSW yourself. Also, HNSW only supports the following arguments for metric and target_metric: "euclidean", "cosine" and "correlation".
"nndescent" Use approximate nearest neighbors with the Nearest Neighbor Descent method (Dong et al., 2011) via the rnndescent package. rnndescent is not a dependency of this package: this option is only available if you have installed rnndescent yourself.

By default, if X has less than 4,096 vertices, the exact nearest neighbors are found. Otherwise, approximate nearest neighbors are used. You may also pass pre-calculated nearest neighbor data to this argument. It must be one of two formats, either a list consisting of two elements:

"idx". A n_vertices x n_neighbors matrix containing the integer indexes of the nearest neighbors in X. Each vertex is considered to be its own nearest neighbor, i.e. idx[, 1] == 1:n_vertices.
"dist". A n_vertices x n_neighbors matrix containing the distances of the nearest neighbors.

or a sparse distance matrix of type dgCMatrix, with dimensions n_vertices x n_vertices. Distances should be arranged by column, i.e. a non-zero entry in row j of the ith column indicates that the jth observation in X is a nearest neighbor of the ith observation with the distance given by the value of that element. The n_neighbors parameter is ignored when using precomputed nearest neighbor data. If using the sparse distance matrix input, each column can contain a different number of neighbors.

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, determines the accuracy of the Annoy nearest neighbor search. Only used if the nn_method is "annoy". Sensible values are between 10 to 100.

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 n_trees, determines the accuracy of the Annoy nearest neighbor search. Only used if the nn_method is "annoy".

approx_pow

If TRUE, use an approximation to the power function in the UMAP gradient, from https://martin.ankerl.com/2012/01/25/optimized-approximative-pow-in-c-and-cpp/. Ignored if dens_scale is non-NULL.

y

Optional target data for supervised dimension reduction. Can be a vector, matrix or data frame. Use the target_metric parameter to specify the metrics to use, using the same syntax as metric. Usually either a single numeric or factor column is used, but more complex formats are possible. The following types are allowed:

Factor columns with the same length as X. NA is allowed for any observation with an unknown level, in which case UMAP operates as a form of semi-supervised learning. Each column is treated separately.
Numeric data. NA is not allowed in this case. Use the parameter target_n_neighbors to set the number of neighbors used with y. If unset, n_neighbors is used. Unlike factors, numeric columns are grouped into one block unless target_metric specifies otherwise. For example, if you wish columns a and b to be treated separately, specify target_metric = list(euclidean = "a", euclidean = "b"). Otherwise, the data will be effectively treated as a matrix with two columns.
Nearest neighbor data, consisting of a list of two matrices, idx and dist. These represent the precalculated nearest neighbor indices and distances, respectively. This is the same format as that expected for precalculated data in nn_method. This format assumes that the underlying data was a numeric vector. Any user-supplied value of the target_n_neighbors parameter is ignored in this case, because the the number of columns in the matrices is used for the value. Multiple nearest neighbor data using different metrics can be supplied by passing a list of these lists.

Unlike X, all factor columns included in y are automatically used.

target_n_neighbors

Number of nearest neighbors to use to construct the target simplicial set. Default value is n_neighbors. Applies only if y is non-NULL and numeric.

target_metric

The metric used to measure distance for y if using supervised dimension reduction. Used only if y is numeric.

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 y is non-NULL.

pcg_rand

If TRUE, use the PCG random number generator (O'Neill, 2014) during optimization. Otherwise, use the faster (but probably less statistically good) Tausworthe "taus88" generator. The default is TRUE.

fast_sgd

If TRUE, then the following combination of parameters is set: pcg_rand = TRUE, n_sgd_threads = "auto" and approx_pow = TRUE. The default is FALSE. Setting this to TRUE will speed up the stochastic optimization phase, but give a potentially less accurate embedding, and which will not be exactly reproducible even with a fixed seed. For visualization, fast_sgd = TRUE will give perfectly good results. For more generic dimensionality reduction, it's safer to leave fast_sgd = FALSE. If fast_sgd = TRUE, then user-supplied values of pcg_rand, n_sgd_threads, and approx_pow are ignored.

ret_model

If TRUE, then return extra data that can be used to add new data to an existing embedding via umap_transform. The embedded coordinates are returned as the list item embedding. If FALSE, just return the coordinates. This parameter can be used in conjunction with ret_nn and ret_extra. Note that some settings are incompatible with the production of a UMAP model: external neighbor data (passed via a list to nn_method), and factor columns that were included via the metric parameter. In the latter case, the model produced is based only on the numeric data. A transformation using new data is possible, but the factor columns in the new data are ignored. Note that setting ret_model = TRUE forces the use of the approximate nearest neighbors method. Because small datasets would otherwise use exact nearest neighbor calculations, setting ret_model = TRUE means that different results may be returned for small datasets in terms of both the returned nearest neighbors (if requested) and the final embedded coordinates, compared to ret_model = FALSE, even if the random number seed is fixed. To avoid this, explicitly set nn_method = "annoy" in the ret_model = FALSE case.

ret_nn

If TRUE, then in addition to the embedding, also return nearest neighbor data that can be used as input to nn_method to avoid the overhead of repeatedly calculating the nearest neighbors when manipulating unrelated parameters (e.g. min_dist, n_epochs, init). See the "Value" section for the names of the list items. If FALSE, just return the coordinates. Note that the nearest neighbors could be sensitive to data scaling, so be wary of reusing nearest neighbor data if modifying the scale parameter. This parameter can be used in conjunction with ret_model and ret_extra.

ret_extra

A vector indicating what extra data to return. May contain any combination of the following strings:

"model" Same as setting ret_model = TRUE.
"nn" Same as setting ret_nn = TRUE.
"fgraph" the high dimensional fuzzy graph (i.e. the fuzzy simplicial set of the merged local views of the input data). The graph is returned as a sparse symmetric N x N matrix of class dgCMatrix-class, where a non-zero entry (i, j) gives the membership strength of the edge connecting vertex i and vertex j. This can be considered analogous to the input probability (or similarity or affinity) used in t-SNE and LargeVis. Note that the graph is further sparsified by removing edges with sufficiently low membership strength that they would not be sampled by the probabilistic edge sampling employed for optimization and therefore the number of non-zero elements in the matrix is dependent on n_epochs. If you are only interested in the fuzzy input graph (e.g. for clustering), setting n_epochs = 0 will avoid any further sparsifying. Be aware that setting `binary_edge_weights = TRUE` will affect this graph (all non-zero edge weights will be 1).
"sigma" the normalization value for each observation in the dataset when constructing the smoothed distances to each of its neighbors. This gives some sense of the local density of each observation in the high dimensional space: higher values of sigma indicate a higher dispersion or lower density.

n_sgd_threads

Number of threads to use during stochastic gradient descent. If set to > 1, then be aware that if batch = FALSE, results will not be reproducible, even if set.seed is called with a fixed seed before running. Set to "auto" to use the same value as n_threads.

grain_size

The minimum amount of work to do on each thread. If this value is set high enough, then less than n_threads or n_sgd_threads will be used for processing, which might give a performance improvement if the overhead of thread management and context switching was outweighing the improvement due to concurrent processing. This should be left at default (1) and work will be spread evenly over all the threads specified.

tmpdir

Temporary directory to store nearest neighbor indexes during nearest neighbor search. Default is tempdir. The index is only written to disk if n_threads > 1 and nn_method = "annoy"; otherwise, this parameter is ignored.

batch

If TRUE, then embedding coordinates are updated at the end of each epoch rather than during the epoch. In batch mode, results are reproducible with a fixed random seed even with n_sgd_threads > 1, at the cost of a slightly higher memory use. You may also have to modify learning_rate and increase n_epochs, so whether this provides a speed increase over the single-threaded optimization is likely to be dataset and hardware-dependent.

opt_args

A list of optimizer parameters, used when batch = TRUE. The default optimization method used is Adam (Kingma and Ba, 2014).

method The optimization method to use. Either "adam" or "sgd" (stochastic gradient descent). Default: "adam".
beta1 (Adam only). The weighting parameter for the exponential moving average of the first moment estimator. Effectively the momentum parameter. Should be a floating point value between 0 and 1. Higher values can smooth oscillatory updates in poorly-conditioned situations and may allow for a larger learning_rate to be specified, but too high can cause divergence. Default: 0.5.
beta2 (Adam only). The weighting parameter for the exponential moving average of the uncentered second moment estimator. Should be a floating point value between 0 and 1. Controls the degree of adaptivity in the step-size. Higher values put more weight on previous time steps. Default: 0.9.
eps (Adam only). Intended to be a small value to prevent division by zero, but in practice can also affect convergence due to its interaction with beta2. Higher values reduce the effect of the step-size adaptivity and bring the behavior closer to stochastic gradient descent with momentum. Typical values are between 1e-8 and 1e-3. Default: 1e-7.
alpha The initial learning rate. Default: the value of the learning_rate parameter.

epoch_callback

A function which will be invoked at the end of every epoch. Its signature should be: (epoch, n_epochs, coords), where:

epoch The current epoch number (between 1 and n_epochs).
n_epochs Number of epochs to use during the optimization of the embedded coordinates.
coords The embedded coordinates as of the end of the current epoch, as a matrix with dimensions (N, n_components).

binary_edge_weights

If TRUE then edge weights in the input graph are treated as binary (0/1) rather than real valued. This affects the sampling frequency of neighbors and is the strategy used by the PaCMAP method (Wang and co-workers, 2020). Practical (Böhm and co-workers, 2020) and theoretical (Damrich and Hamprecht, 2021) work suggests this has little effect on UMAP's performance.

dens_scale

A value between 0 and 1. If > 0 then the output attempts to preserve relative local density around each observation. This uses an approximation to the densMAP method (Narayan and co-workers, 2021). The larger the value of dens_scale, the greater the range of output densities that will be used to map the input densities. This option is ignored if using multiple metric blocks.

nn_args

A list containing additional arguments to pass to the nearest neighbor method. For nn_method = "annoy", you can specify "n_trees" and "search_k", and these will override the n_trees and search_k parameters. For nn_method = "hnsw", you may specify the following arguments:

M The maximum number of neighbors to keep for each vertex. Reasonable values are 2 to 100. Higher values give better recall at the cost of more memory. Default value is 16.
ef_construction A positive integer specifying the size of the dynamic list used during index construction. A higher value will provide better results at the cost of a longer time to build the index. Default is 200.
ef A positive integer specifying the size of the dynamic list used during search. This cannot be smaller than n_neighbors and cannot be higher than the number of items in the index. Default is 10.

For nn_method = "nndescent", you may specify the following arguments:

n_trees The number of trees to use in a random projection forest to initialize the search. A larger number will give more accurate results at the cost of a longer computation time. The default of NULL means that the number is chosen based on the number of observations in X.
max_candidates The number of potential neighbors to explore per iteration. By default, this is set to n_neighbors or 60, whichever is smaller. A larger number will give more accurate results at the cost of a longer computation time.
n_iters The number of iterations to run the search. A larger number will give more accurate results at the cost of a longer computation time. By default, this will be chosen based on the number of observations in X. You may also need to modify the convergence criterion delta.
delta The minimum relative change in the neighbor graph allowed before early stopping. Should be a value between 0 and 1. The smaller the value, the smaller the amount of progress between iterations is allowed. Default value of 0.001 means that at least 0.1 neighbor graph must be updated at each iteration.
init How to initialize the nearest neighbor descent. By default this is set to "tree" and uses a random project forest. If you set this to "rand", then a random selection is used. Usually this is less accurate than using RP trees, but for high-dimensional cases, there may be little difference in the quality of the initialization and random initialization will be a lot faster. If you set this to "rand", then the n_trees parameter is ignored.
pruning_degree_multiplier The maximum number of edges per node to retain in the search graph, relative to n_neighbors. A larger value will give more accurate results at the cost of a longer computation time. Default is 1.5. This parameter only affects neighbor search when transforming new data with umap_transform.
epsilon Controls the degree of the back-tracking when traversing the search graph. Setting this to 0.0 will do a greedy search with no back-tracking. A larger value will give more accurate results at the cost of a longer computation time. Default is 0.1. This parameter only affects neighbor search when transforming new data with umap_transform.
max_search_fraction Specifies the maximum fraction of the search graph to traverse. By default, this is set to 1.0, so the entire graph (i.e. all items in X) may be visited. You may want to set this to a smaller value if you have a very large dataset (in conjunction with epsilon) to avoid an inefficient exhaustive search of the data in X. This parameter only affects neighbor search when transforming new data with umap_transform.

Value

giotto object with updated UMAP dimension reduction

Details

See umap for more information about these and other parameters.

Input for UMAP dimension reduction can be another dimension reduction (default = 'pca')
To use gene expression as input set dim_reduction_to_use = NULL
If dim_reduction_to_use = NULL, feats_to_use can be used to select a column name of highly variable features (see calculateHVF) or simply provide a vector of features
multiple UMAP results can be stored by changing the name of the analysis

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> cell_spatInfo_spatVector.shp
#> cell
#> 
#> 3.2 read Giotto spatial centroid information
#> cell
#> 
#> 3.3 read Giotto spatial overlap information
#> No overlaps were found, overlap loading will be
#>  skipped
#> 
#> 4. read Giotto image information
#> a giotto python environment was found
#> Using python path:
#>  "/Users/yuanlab/Library/r-miniconda/envs/giotto_env/bin/pythonw"

runUMAP(g)
#> umap has already been used, will be overwritten
#> > umap already exists and will be replaced with
#>  new dimension reduction object
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> [SUBCELLULAR INFO]
#> polygons      : cell 
#> [AGGREGATE INFO]
#> expression -----------------------
#>   [cell][rna] raw normalized scaled
#> spatial locations ----------------
#>   [cell] raw
#> spatial networks -----------------
#>   [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#>   [cell][rna] cluster_metagene DWLS
#> dim reduction --------------------
#>   [cell][rna] pca custom_pca umap custom_umap tsne
#> nearest neighbor networks --------
#>   [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images      : alignment image 
#> 
#> 
#> Use objHistory() to see steps and params used