constructBipartiteGraph: Construct Bipartite Graph Edge Weight Matrix of Gene-drug...

In hierBipartite: Bipartite Graph-Based Hierarchical Clustering

Description

Constructs edge weight matrix B representing association between set of variables in mat1 and set of variables in mat2 (see paper).

Usage

constructBipartiteGraph(
  mat1,
  mat2,
  n_subsample = 1,
  subsampling_ratio = 1,
  parallel = FALSE,
  maxCores = 7
)

Arguments

mat1	an n x p matrix of variable set 1 (e.g. gene expression)
mat2	an n x q matrix of variable set 2 (e.g. drug sensitivity)
n_subsample	number of times to perform subsampling to generate B
subsampling_ratio	fraction of samples to subsample each time
parallel	boolean for whether to parallelize subsampling
maxCores	maximum number of cores to use (only applicable when parallel = TRUE)

Value

a p x q matrix of bipartite graph edge weights

Examples

# Extract bipartite edge weight matrix B for cell lines from the
# squamous cell carcinoma, esophagus group
data(ctrp2)

groups = ctrp2$groups
X = ctrp2$X
Y = ctrp2$Y

x = X[groups[["squamous_cell_carcinoma_esophagus"]], ]
y = Y[groups[["squamous_cell_carcinoma_esophagus"]], ]

# Extract bipartite edge weight matrix B with subsampling
## Not run: 
B = constructBipartiteGraph(x, y, n_subsample = 100,
                            subsampling_ratio = 0.90,
                            parallel = TRUE, maxCores = 2)

## End(Not run)

hierBipartite documentation built on Feb. 16, 2021, 5:07 p.m.