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

Description Usage Arguments Value Examples

Description

Perform network smoothing of gene expression or other omics data

Usage

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## 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

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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)

netSmooth documentation built on Nov. 8, 2020, 5:33 p.m.