--- a
+++ b/man/reducedDims.Rd
@@ -0,0 +1,87 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/scAI_model.R
+\name{reducedDims}
+\alias{reducedDims}
+\title{Perform dimensional reduction}
+\usage{
+reducedDims(object, data.use = object@fit$H, do.scale = TRUE,
+  do.center = TRUE, return.object = TRUE, method = "umap",
+  dim.embed = 2, dim.use = NULL, perplexity = 30, theta = 0.5,
+  check_duplicates = F, rand.seed = 42L, FItsne.path = NULL,
+  dimPC = 40, do.fast = TRUE, weight.by.var = TRUE,
+  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)
+}
+\arguments{
+\item{object}{scAI object}
+
+\item{data.use}{input data}
+
+\item{do.scale}{whether scale the data}
+
+\item{do.center}{whether scale and center the data}
+
+\item{return.object}{whether return scAI object}
+
+\item{method}{Method of dimensional reduction, one of tsne, FItsne and umap}
+
+\item{dim.embed}{dimensions of t-SNE embedding}
+
+\item{dim.use}{num of PCs used for t-SNE}
+
+\item{perplexity}{perplexity parameter in tsne}
+
+\item{theta}{parameter in tsne}
+
+\item{check_duplicates}{parameter in tsne}
+
+\item{rand.seed}{Set a random seed. By default, sets the seed to 42.}
+
+\item{FItsne.path}{File path of FIt-SNE}
+
+\item{dimPC}{the number of components to keep in PCA}
+
+\item{do.fast}{whether do fast PCA}
+
+\item{weight.by.var}{whether use weighted pc.scores}
+
+\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.}
+}
+\description{
+Dimension reduction by PCA, t-SNE or UMAP
+}