netSmooth: Perform network smoothing of gene expression or other omics...

In BIMSBbioinfo/netSmooth: Network smoothing for scRNAseq

Description

Perform network smoothing of gene expression or other omics data

Usage

## S4 method for signature 'matrix'
netSmooth(
  x,
  adjMatrix,
  alpha = "auto",
  normalizeAdjMatrix = c("rows", "columns"),
  autoAlphaMethod = c("robustness", "entropy"),
  autoAlphaRange = 0.1 * (seq_len(9)),
  autoAlphaDimReduceFlavor = "auto",
  is.counts = TRUE,
  bpparam = BiocParallel::SerialParam(),
  ...
)

## S4 method for signature 'SummarizedExperiment'
netSmooth(x, ...)

## S4 method for signature 'SingleCellExperiment'
netSmooth(x, ...)

## S4 method for signature 'Matrix'
netSmooth(
  x,
  adjMatrix,
  alpha = "auto",
  normalizeAdjMatrix = c("rows", "columns"),
  autoAlphaMethod = c("robustness", "entropy"),
  autoAlphaRange = 0.1 * (seq_len(9)),
  autoAlphaDimReduceFlavor = "auto",
  is.counts = TRUE,
  bpparam = BiocParallel::SerialParam(),
  ...
)

## S4 method for signature 'DelayedMatrix'
netSmooth(
  x,
  adjMatrix,
  alpha = "auto",
  normalizeAdjMatrix = c("rows", "columns"),
  autoAlphaMethod = c("robustness", "entropy"),
  autoAlphaRange = 0.1 * (seq_len(9)),
  autoAlphaDimReduceFlavor = "auto",
  is.counts = TRUE,
  bpparam = BiocParallel::SerialParam(),
  filepath = NULL,
  ...
)

Arguments

x	matrix or SummarizedExperiment
adjMatrix	adjacency matrix of gene network to use
alpha	numeric in [0,1] or 'audo'. if 'auto', the optimal value for alpha will be automatically chosen among the values specified in 'autoAlphaRange', using the strategy specified in 'autoAlphaMethod'
normalizeAdjMatrix	how to normalize the adjacency matrix possible values are 'rows' (in-degree) and 'columns' (out-degree)
autoAlphaMethod	if 'robustness', pick alpha that gives the highest proportion of samples in robust clusters if 'entropy', pick alpha that gives highest Shannon entropy in 2D PCA embedding
autoAlphaRange	if ‘alpha=’optimal'', search these values for the best alpha
autoAlphaDimReduceFlavor	algorithm for dimensionality reduction that will be used to pick the optimal value for alpha. Either the 2D embedding to calculate the Shannon entropy for (if ‘autoAlphaMethod=’entropy''), or the dimensionality reduction algorithm to be used in robust clustering (if ‘autoAlphamethod=’robustness'')
is.counts	logical: is the assay count data
bpparam	instance of bpparam, for parallel computation with the ‘alpha=’auto'' option. See the BiocParallel manual.
...	arguments passed on to 'robustClusters' if using the robustness criterion for optimizing alpha
filepath	String: Path to location where hdf5 output file is supposed to be saved. Will be ignored when regular matrices or SummarizedExperiment are used as input.

Value

network-smoothed gene expression matrix or SummarizedExperiment object

Examples

x <- matrix(rnbinom(12000, size=1, prob = .1), ncol=60)
rownames(x) <- paste0('gene', seq_len(dim(x)[1]))

adj_matrix <- matrix(as.numeric(rnorm(200*200)>.8), ncol=200)
rownames(adj_matrix) <- colnames(adj_matrix) <- paste0('gene', seq_len(dim(x)[1]))
x.smoothed <- netSmooth(x, adj_matrix, alpha=0.5)

BIMSBbioinfo/netSmooth documentation built on Dec. 24, 2019, 8:08 p.m.