Description Usage Arguments References See Also
Modified version of https://github.com/KlugerLab/FIt-SNE/
Please do not directly call this function as it requires compiled FIt-SNE code to run.
If you want to run FIt-SNE, please have a look at run.fitsne
function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | fftRtsne(
X,
dims = 2,
perplexity = 30,
theta = 0.5,
max_iter = 750,
fft_not_bh = TRUE,
ann_not_vptree = TRUE,
stop_early_exag_iter = 250,
exaggeration_factor = 12,
no_momentum_during_exag = FALSE,
start_late_exag_iter = -1,
late_exag_coeff = 1,
mom_switch_iter = 250,
momentum = 0.5,
final_momentum = 0.8,
learning_rate = "auto",
n_trees = 50,
search_k = -1,
rand_seed = -1,
nterms = 3,
intervals_per_integer = 1,
min_num_intervals = 50,
K = -1,
sigma = -30,
initialization = "pca",
max_step_norm = 5,
data_path = NULL,
result_path = NULL,
load_affinities = NULL,
fast_tsne_path = NULL,
nthreads = 0,
perplexity_list = NULL,
get_costs = FALSE,
df = 1
)
|
X |
No default. Matrix containing the data which dimension need to be reduced. |
perplexity |
Default = 30. Perplexity is used to determine the bandwidth of the Gaussian kernel in the input space |
theta |
Default = 0.5. For exact t-SNE, set to 0. If non-zero, then will use either Barnes Hut or FIt-SNE based on nbody_algo. If Barnes Hut, then this determines the accuracy of BH approximation. |
max_iter |
Default = 750. Number of iterations of t-SNE to run. |
fft_not_bh |
Default = TRUE. If theta is nonzero, this determines whether to use FIt-SNE or Barnes Hut approximation. |
ann_not_vptree |
Default = TRUE. Use vp-trees (as in bhtsne) or approximate nearest neighbors (default). Set to be TRUE for approximate nearest neighbors. |
stop_early_exag_iter |
Default = 250. When to switch off early exaggeration. |
exaggeration_factor |
Default = 12. Coefficient for early exaggeration (>1). |
no_momentum_during_exag |
Default = FALSE. Set to 0 to use momentum and other optimization tricks. Can be set to 1 to do plain, vanilla gradient descent (useful for testing large exaggeration coefficients). |
start_late_exag_iter |
Default = -1. When to start late exaggeration. Set to -1 by default to not use late exaggeration. |
late_exag_coeff |
Default = 1. Late exaggeration coefficient. Set to 1 by default to not use late exaggeration. |
mom_switch_iter |
Default = 250. Iteration number to switch from momentum to final_momentum. |
momentum |
Default = 0.5.Initial value of momentum. |
final_momentum |
Default = 0.8. Value of momentum to use later in the optimisation. |
learning_rate |
Default = 'auto'. Set to desired learning rate or 'auto', which sets learning rate to N/exaggeration_factor where N is the sample size, or to 200 if N/exaggeration_factor < 200. |
n_trees |
Default = 50. When using Annoy, the number of search trees to use. |
search_k |
Default = -1. When using Annoy, the number of nodes to inspect during search. Default is -1 which translate to 3perplexityn_trees (or K*n_trees when using fixed sigma). |
rand_seed |
Default = -1. Seed for random initialisation. Set to -1 by default to initialise random number generator with current time. |
nterms |
Default = 3. If using FIt-SNE, this is the number of interpolation points per sub-interval. |
intervals_per_integer |
Default = 1. See min_num_intervals. |
min_num_intervals |
Default = 50. Let maxloc = ceil(max(max(X))) and minloc = floor(min(min(X))). i.e. the points are in a minloc^no_dims by maxloc^no_dims interval/square. The number of intervals in each dimension is either min_num_intervals or ceil((maxloc - minloc)/intervals_per_integer), whichever is larger. min_num_intervals must be an integer >0, and intervals_per_integer must be >0. Defaults are min_num_intervals=50 and intervals_per_integer = 1. |
K |
Default = -1. Number of nearest neighbours to get when using fixed sigma. |
sigma |
Default = -30. Fixed sigma value to use when perplexity==-1. |
initialization |
Default = 'pca'. pca', 'random', or N x no_dims array to intialize the solution. |
max_step_norm |
Default = 5. Maximum distance that a point is allowed to move on one iteration. Larger steps are clipped to this value. This prevents possible instabilities during gradient descent. Set to -1 to switch it off. |
load_affinities |
Default = NULL. If 1, input similarities are loaded from a file and not computed. If 2, input similarities are saved into a file. If 0, affinities are neither saved nor loaded. |
fast_tsne_path |
Default = NULL. Path to FItSNE executable. |
nthreads |
Default = 0. Number of threads to use, set to use all available threads by default. |
perplexity_list |
Default = NULL. If perplexity==0 then perplexity combination will be used with values taken from perplexity_list. |
get_costs |
Default = FALSE. Logical indicating whether the KL-divergence costs computed every 50 iterations should be returned. |
df |
Default = 1.0. Positive numeric that controls the degree of freedom of t-distribution. The actual degree of freedom is 2*df-1. The standard t-SNE choice of 1 degree of freedom corresponds to df=1. Large df approximates Gaussian kernel. df<1 corresponds to heavier tails, which can often resolve substructure in the embedding. See Kobak et al. (2019) for details. |
dim |
Default = 2. Dimensionality of the embedding (reduced data). |
Linderman, G., Rachh, M., Hoskins, J., Steinerberger, S., Kluger., Y. (2019). Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nature Methods. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402590/.
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