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+++ b/man/runUMAP.Rd
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/scAI_model.R
+\name{runUMAP}
+\alias{runUMAP}
+\title{Perform dimension reduction using UMAP}
+\usage{
+runUMAP(data.use, n.neighbors = 30L, n.components = 2L,
+  distance = "correlation", n.epochs = NULL, learning.rate = 1,
+  min.dist = 0.3, spread = 1, set.op.mix.ratio = 1,
+  local.connectivity = 1L, repulsion.strength = 1,
+  negative.sample.rate = 5, a = NULL, b = NULL, seed.use = 42L,
+  metric.kwds = NULL, angular.rp.forest = FALSE, verbose = TRUE)
+}
+\arguments{
+\item{data.use}{input data}
+
+\item{n.neighbors}{This determines the number of neighboring points used in
+local approximations of manifold structure. Larger values will result in more
+global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50.}
+
+\item{n.components}{The dimension of the space to embed into.}
+
+\item{distance}{This determines the choice of metric used to measure distance in the input space.}
+
+\item{n.epochs}{the number of training epochs to be used in optimizing the low dimensional embedding. Larger values result in more accurate embeddings. If NULL is specified, a value will be selected based on the size of the input dataset (200 for large datasets, 500 for small).}
+
+\item{learning.rate}{The initial learning rate for the embedding optimization.}
+
+\item{min.dist}{This controls how tightly the embedding is allowed compress points together.
+Larger values ensure embedded points are moreevenly distributed, while smaller values allow the
+algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5.}
+
+\item{spread}{he effective scale of embedded points. In combination with min.dist this determines how clustered/clumped the embedded points are.}
+
+\item{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.}
+
+\item{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.}
+
+\item{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.}
+
+\item{negative.sample.rate}{The number of negative samples to select per positive sample in the
+optimization process. Increasing this value will result in greater repulsive force being applied, greater optimization cost, but slightly more accuracy.}
+
+\item{a}{More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread.}
+
+\item{b}{More specific parameters controlling the embedding. If NULL, these values are set automatically as determined by min. dist and spread.}
+
+\item{seed.use}{Set a random seed. By default, sets the seed to 42.}
+
+\item{metric.kwds}{A dictionary of arguments to pass on to the metric, such as the p value for Minkowski distance}
+
+\item{angular.rp.forest}{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.}
+
+\item{verbose}{Controls verbosity
+This function is modified from Seurat package}
+}
+\description{
+Perform dimension reduction using UMAP
+}