reducedDims: Perform dimensional reduction

In sqjin/scAI: integrative analysis of single-cell transcriptomic and epigenomic profiles

Description

Dimension reduction by PCA, t-SNE or UMAP

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

object	scAI object
data.use	input data
do.scale	whether scale the data
do.center	whether scale and center the data
return.object	whether return scAI object
method	Method of dimensional reduction, one of tsne, FItsne and umap
dim.embed	dimensions of t-SNE embedding
dim.use	num of PCs used for t-SNE
perplexity	perplexity parameter in tsne
theta	parameter in tsne
check_duplicates	parameter in tsne
rand.seed	Set a random seed. By default, sets the seed to 42.
FItsne.path	File path of FIt-SNE
dimPC	the number of components to keep in PCA
do.fast	whether do fast PCA
weight.by.var	whether use weighted pc.scores
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.
n.components	The dimension of the space to embed into.
distance	This determines the choice of metric used to measure distance in the input space.
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).
learning.rate	The initial learning rate for the embedding optimization.
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.
spread	he effective scale of embedded points. In combination with min.dist this determines how clustered/clumped the embedded points are.
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.
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.
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 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.
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.

sqjin/scAI documentation built on Nov. 19, 2020, 4:04 p.m.