RunSWNE: Wrapper for running SWNE analysis

Description Usage Arguments Value

View source: R/run_swne.R

Description

Wrapper for running SWNE analysis

Usage

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## S3 method for class 'seurat'
RunSWNE(object, proj.method = "umap",
  reduction.use = "pca", cells.use = NULL, dims.use = NULL,
  genes.use = NULL, dist.metric = "cosine", distance.matrix = NULL,
  n.cores = 8, k, k.range, var.genes, loss = "mse", genes.embed,
  hide.factors = T, n_pull = 3, alpha.exp = 1.25, snn.exp = 1,
  reduction.name = "swne", reduction.key = "SWNE_",
  return.format = c("embedding", "seurat"), ...)

## S3 method for class 'Pagoda2'
RunSWNE(object, proj.method = "umap",
  dist.metric = "cosine", n.cores = 8, k, k.range, var.genes,
  loss = "mse", genes.embed, hide.factors = T, n_pull = 3,
  n.var.genes = 3000, alpha.exp = 1.25, snn.exp = 1)

## S3 method for class 'dgCMatrix'
RunSWNE(data.matrix, proj.method = "umap",
  dist.metric = "cosine", n.cores = 3, k, k.range,
  var.genes = rownames(data.matrix), loss = "mse", genes.embed,
  hide.factors = T, n_pull = 3, alpha.exp = 1.25, snn.exp = 1)

## S3 method for class 'matrix'
RunSWNE(data.matrix, proj.method = "umap",
  dist.metric = "cosine", n.cores = 3, k, k.range,
  var.genes = rownames(data.matrix), loss = "mse", genes.embed,
  hide.factors = T, n_pull = 3, alpha.exp = 1.25, snn.exp = 1)

## S3 method for class 'dgTMatrix'
RunSWNE(data.matrix, proj.method = "umap",
  dist.metric = "cosine", n.cores = 3, k, k.range,
  var.genes = rownames(data.matrix), loss = "mse", genes.embed,
  hide.factors = T, n_pull = 3, alpha.exp = 1.25, snn.exp = 1)

Arguments

object

A Seurat or Pagoda2 object with normalized data

proj.method

Method to use to project factors in 2D. Either "sammon" or "umap"

reduction.use

Which dimensional reduction (e.g. PCA, ICA) to use for the tSNE. Default is PCA.

cells.use

Which cells to analyze (default, all cells)

dims.use

Which dimensions to use as input features

genes.use

If set, run the SWNE on this subset of genes (instead of running on a set of reduced dimensions). Not set (NULL) by default

distance.matrix

If set, runs tSNE on the given distance matrix instead of data matrix (experimental)

n.cores

Number of cores to use (passed to FindNumFactors)

k

Number of NMF factors (passed to RunNMF). If none given, will be derived from FindNumFactors.

k.range

Range of factors for FindNumFactors to iterate over if k is not given

var.genes

vector to specify variable genes. Will infer from Seurat or use full dataset if not given.

loss

loss function to use (passed to RunNMF)

genes.embed

Genes to add to the SWNE embedding

hide.factors

Hide factors when plotting SWNE embedding

n_pull

Maximum number of factors "pulling" on each sample

alpha.exp

Increasing alpha.exp increases how much the NMF factors "pull" the samples (passed to EmbedSWNE)

snn.exp

Decreasing snn.exp increases the effect of the similarity matrix on the embedding (passed to EmbedSWNE)

reduction.name

dimensional reduction name, specifies the position in the object$dr list. swne by default

reduction.key

dimensional reduction key, specifies the string before the number for the dimension names. SWNE_ by default

return.format

format to return ("seurat" object or raw "embedding")

n.var.genes

Number of variable genes to use

data.matrix

a data matrix (genes x cells) which has been pre-normalized

batch

Vector of batch effects to correct for

dist.use

Similarity function to use for calculating factor positions (passed to EmbedSWNE). Options include pearson (correlation), IC (mutual information), cosine, euclidean.

Value

A list of factor (H.coords) and sample coordinates (sample.coords) in 2D


yanwu2014/swne documentation built on Dec. 7, 2018, 8:55 a.m.