Description Usage Arguments Value Examples
Perform network smoothing of gene expression or other omics data
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ## 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
1 2 3 4 5 6 | 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)
|
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