Construct Bipartite Graph Edge Weight Matrix of Gene-drug Association Patterns

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

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)

Processed Cancer Cell Line Encyclopedia (CCLE) and Cancer Therapeutics Response Portal (CTRP2) Dataset

Description

Smaller test dataset version of the "CTRP2" carcinoma dataset in the paper. Specifically, only the top 1,000 transcripts by correlation with drug sensitivity are included instead of 5,000. Otherwise the dataset has been processed exactly as described in the paper. Note the expression dataset is provided by CCLE and the drug sensitivity dataset is provided by CTRP2, and the pharmacogenomic datasets in the paper are are referred to by the resource providing the sensitivity data. The cell lines are grouped by carcinoma subtype and primary site (e.g. lung NSC).

Usage

data(ctrp2)

Format

A list with elements of gene expression, drug sensitivities, and group membership.

X

n x p gene expression matrix

Y

n x q drug sensitivities matrix

groups

List of starting groups. Each group is represented by a vector of row indices for X, Y.

References

Barretina, J., et al. (2012). The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 483(7391), 603–607. (PubMed)

Seashore-Ludlow, B., et al. (2015). Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer discovery, 5(11), 1210-1223. (PubMed)

Examples

data(ctrp2)
X = ctrp2[["X"]]
Y = ctrp2[["Y"]]
groups = ctrp2[["groups"]]

Given index from hclust merge matrix, return X, Y row indices corresponding to groups specified by index

Description

Given index from hclust merge matrix, return X, Y row indices corresponding to groups specified by index

Usage

getMergeGroupRows(idx, groups, groupMerges)

Arguments

Value

vector of row indices corresponding to X, Y

Select Significant Results from 'HierBipartite'

Description

Selects clusters from bipartite graph-based hierarchical clustering with p-value less than or equal to a p-value cutoff.

Usage

getSignificantMergedGroups(results, p = 0.05)

Arguments

Value

list of results from bipartite graph-based hierarchical clustering, but only with clusters with p-value at or below p-value cutoff

Examples

# sample bipartite graph-based hierarchical clustering of three groups
data(ctrp2)

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

groupNames = names(groups)
groupSmall = groups[groupNames[1:3]]

## Not run: 
result = hierBipartite(X, Y, groupSmall)

# set fictitious p-values, with one cluster with p-value less than the cutoff
# and the other not
result$nodePvals = list(0.03, 0.12)
getSignificantMergedGroups(result, p = 0.05)

## End(Not run)

Bipartite Graph-based Hierarchical Clustering

Description

Main bipartite graph-based hierarchial clustering algorithm. Visit here for vignette on using the hierBipartite package.

Usage

hierBipartite(
  X,
  Y,
  groups,
  link = "ward.D2",
  n_subsample = 1,
  subsampling_ratio = 1,
  p.value = FALSE,
  n_perm = 100,
  parallel = FALSE,
  maxCores = 7,
  p_cutoff = 0.1
)

Arguments

Value

list of results from bipartite graph-based hierarchical clustering, containing up to

• hclustObj: hclust object

• groupMerges: list of clusters after each merge, in order of merge. Each cluster is indicated by a vector of cell line groups

• nodePvals: list of p-value of each new merge, in order of merge. Only available if p.value = TRUE

• D: dissimilarity matrix

Examples

# Get a small subset of the test dataset
data(ctrp2)

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

groupNames = names(groups)
groupSmall = groups[groupNames[1:3]]

## Not run: 
# Basic call of hierBipartite() on small test dataset
result0 = hierBipartite(X, Y, groupSmall)

# Calling hierBipartite() with subsampling
result1 = hierBipartite(X, Y, groupSmall, n_subsample = 100, subsampling_ratio = 0.90)

# Calling hierBipartite() with p-value generation
result2 = hierBipartite(X, Y, groupSmall, n_perm = 100, p.value = TRUE, p_cutoff = 0.10)

# Calling hierBipartite() with both subsampling and p-value generation (expensive)
result3 = hierBipartite(X, Y, groupSmall, n_subsample = 100, subsampling_ratio = 0.90,
                        n_perm = 100, p.value = TRUE, p_cutoff = 0.10)

## End(Not run)

Matrix dissimilarity

Description

Computes nuclear norm-based dissimilarity measure between two matrices.

Usage

matrixDissimilarity(B1, B2)

Arguments

Value

nuclear norm-based dissimilarity

Examples

# Compute matrix dissimilarity in edge weight matrix between squamous cell
# carcinoma, esophagus and squamous cell carcinoma, upper aerodigestive
data(ctrp2)

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

x1 = X[groups[["squamous_cell_carcinoma_esophagus"]], ]
y1 = Y[groups[["squamous_cell_carcinoma_esophagus"]], ]

## Not run: 
B1 = constructBipartiteGraph(x1, y1)

## End(Not run)

x2 = X[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ]
y2 = Y[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ]

## Not run: 
B2 = constructBipartiteGraph(x2, y2)
matrixDissimilarity(B1, B2)

## End(Not run)

Given group indices for a merge from hclust merge matrix, return group names of merged cluster

Description

Given group indices for a merge from hclust merge matrix, return group names of merged cluster

Usage

newMergedGroup(idx1, idx2, groups, groupMerges)

Arguments

Value

vector of group names after merge of group indicated by idx1 and idx2

Null distribution of dissimilarity measures

Description

Generates null distribution of dissimilarity measures between group 1 (X1, Y1) and group 2 (X2, Y2).

Usage

null_distri(X1, Y1, X2, Y2, n.perm = 100, parallel = FALSE, maxCores = 7)

Arguments

Value

vector of length n.perm of null dissimilarity measures

Examples

# Get data for group squamous cell carcinoma, esophagus and for group
# squamous cell carcinoma, upper aerodigestive
data(ctrp2)

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

x1 = X[groups[["squamous_cell_carcinoma_esophagus"]], ]
y1 = Y[groups[["squamous_cell_carcinoma_esophagus"]], ]

x2 = X[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ]
y2 = Y[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ]

## Not run: 
dissimilarities = null_distri(x1, y1, x2, y2, n.perm = 100)

## End(Not run)

P-value of Similarity in Gene-drug Associations

Description

Computes p-value as number of null dissimilarities less than or equal to observed dissimilarity.

Usage

p_value(dissimilarity, dissimilarities)

Arguments

Value

p-value

Examples

# simulate null distribution of dissimilarities
dissimilarities = runif(100, min = 0, max = 1)

d = 0.10
p_value(d, dissimilarities)

Only scales features (columns) with positive variance

Description

Only scales features (columns) with positive variance

Usage

scale_features(mat)

Arguments

Value

an n x p matrix with columns with positive variance scaled

Sparse canonical covariance analysis

Description

'scca' is used to perform sparse canonical covariance analysis (SCCA)

Usage

scca(X,Y,penalty="HL",lamx=c(1,2,3),lamy=c(1,2,3),nc=1,
tuning="CV.alt",K=5,seed=NULL,center=TRUE,scale=FALSE)

Arguments

Details

Sparse CCA uses a random-effect model approach to obtain sparse regression. This model gives unbounded gains for zero loadings at the origin. Various penalty functions can be adapted as well.

Value

• A: p-by-nc matrix, k-th colum of A corresponds to k-th pattern

• B: q-by-nc matrix, k-th colum of B corresponds to k-th pattern (canonical vector) for Y

• U: n-by-nc matrix. k-th column of U corresponds to k-th score associated with k-th pattern for X

• V: n-by-nc matrix. k-th column of V corresponds to k-th score associated with k-th pattern for Y

• lambda: nc-by-2 matrix. k-th row of lambda corresponds to the optimal tuning parameters for k-th pattern pairs

• CR: average cross-validated sample covariance

Author(s)

Woojoo Lee, Donghwan Lee, Youngjo Lee and Yudi Pawitan

References

Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) Sparse Canonical Covariance Analysis for High-throughput Data

Examples

## Example 1
## A very simple simulation example
n<-10; p<-50; q<-20
X = matrix(rnorm(n*p),ncol=p)
Y = matrix(rnorm(n*q),ncol=q)
scca(X,Y)