netSmooth,matrix-method {netSmooth} | R Documentation |
Perform network smoothing of gene expression or other omics data
## 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, ... )
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. |
network-smoothed gene expression matrix or SummarizedExperiment object
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)