Package 'netSmooth'

Human Protein-Protein interaction graph

Description

An adjacency matrix of the 10 percent highest confidence interactions between human proteins on STRINGdb.

Usage

human.ppi

Format

A square matrix where A_ij=1 if gene i interacts with gene j

Details

See the script in 'system.file(package="netSmooth", "data-raw", "make_ppi_from_string.R")' for full details of how this object was made.

Source

http://www.string-db.org/

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Mouse Protein-Protein interaction graph

Description

An adjacency matrix of the 10 percent highest confidence interactions between mouse proteins on STRINGdb.

Usage

mouse.ppi

Format

A square matrix where A_ij=1 if gene i interacts with gene j

Details

See the script in 'system.file(package="netSmooth", "data-raw", "make_ppi_from_string.R")' for full details of how this object was made.

Source

http://www.string-db.org/

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Perform network smoothing of gene expression or other omics data

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)

---

Pick the dimensionality reduction method for a dataset that gives the 2D embedding with the highest entropy

Description

Pick the dimensionality reduction method for a dataset that gives the 2D embedding with the highest entropy

Usage

## S4 method for signature 'matrix'
pickDimReduction(x, flavors = c("pca", "tsne", "umap"), is.counts = TRUE)

## S4 method for signature 'SummarizedExperiment'
pickDimReduction(x)

## S4 method for signature 'Matrix'
pickDimReduction(x, flavors = c("pca", "tsne", "umap"), is.counts = TRUE)

## S4 method for signature 'DelayedMatrix'
pickDimReduction(x, flavors = c("pca", "tsne", "umap"), is.counts = TRUE)

Arguments

x	matrix or SummarizedExperiment object [GENES x SAMPLES]
flavors	list of dimensionality reduction algorithms to try. Currently the options are "pca", "tsne" and "umap"
is.counts	logical: is exprs count data

Value

name of dimensionality reduction method that gives the highest 2d entropy

Examples

x <- matrix(rnbinom(60000, size=1, prob = .1), ncol=100)
pickDimReduction(x)

---

Perform robust clustering on dataset, and calculate the proportion of samples in robust clusters

Description

Perform robust clustering on dataset, and calculate the proportion of samples in robust clusters

Usage

## S4 method for signature 'SummarizedExperiment'
robustClusters(x, dimReduceFlavor = "auto", is.counts = TRUE, ...)

## S4 method for signature 'matrix'
robustClusters(x, ...)

Arguments

x	matrix or SummarizedExperiment object
dimReduceFlavor	algorithm for dimensionality reduction step of clustering procedure. May be 'pca', 'tsne', 'dm', 'umap' or 'auto', which uses shannon entropy to pick the algorithm.
is.counts	logical: is the data counts
...	arguments passed on to 'clusterExperimentWorkflow'

Value

list(clusters, proportion.robust)

Examples

data("smallscRNAseq")
robustClusters(smallscRNAseq, dimReduceFlavor='pca')

---

A small human Protein-Protein interaction graph for use in examples.

Description

Contains a synthetic PPI of human genes.

Usage

smallPPI

Format

An object of class matrix with 611 rows and 611 columns.

---

A small single cell RNA-seq dataset for use in examples.

Description

Contains scRNAseq profiles of human blastomeres.

Usage

smallscRNAseq

Format

SingleCellExperiment

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44183

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