| Title: | Bipartite Graph-Based Hierarchical Clustering |
|---|---|
| Description: | Bipartite graph-based hierarchical clustering performs hierarchical clustering of groups of samples based on association patterns between two sets of variables. It is developed for pharmacogenomic datasets and datasets sharing the same data structure. In the context of pharmacogenomic datasets, the samples are cell lines, and the two sets of variables are typically expression levels and drug sensitivity values. For this method, sparse canonical correlation analysis from Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> is first applied to extract association patterns for each group of samples. Then, a nuclear norm-based dissimilarity measure is used to construct a dissimilarity matrix between groups based on the extracted associations. Finally, hierarchical clustering is applied. |
| Authors: | Calvin Chi [aut, cre, cph], Woojoo Lee [ctb], Donghwan Lee [ctb], Youngjo Lee [ctb], Yudi Pawitan [ctb] |
| Maintainer: | Calvin Chi <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.2 |
| Built: | 2025-01-30 03:06:36 UTC |
| Source: | https://github.com/calvintchi/hierbipartite |
Constructs edge weight matrix B representing association between set of variables in mat1 and set of variables in mat2 (see paper).
constructBipartiteGraph( mat1, mat2, n_subsample = 1, subsampling_ratio = 1, parallel = FALSE, maxCores = 7 )constructBipartiteGraph( mat1, mat2, n_subsample = 1, subsampling_ratio = 1, parallel = FALSE, maxCores = 7 )
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) |
a p x q matrix of bipartite graph edge weights
# 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$Y x = X[groups[["squamous_cell_carcinoma_esophagus"]], ] y = Y[groups[["squamous_cell_carcinoma_esophagus"]], ] # Extract bipartite edge weight matrix B with subsampling ## Not run: B = constructBipartiteGraph(x, y, n_subsample = 100, subsampling_ratio = 0.90, parallel = TRUE, maxCores = 2) ## End(Not run)# Extract bipartite edge weight matrix B for cell lines from the # squamous cell carcinoma, esophagus group data(ctrp2) groups = ctrp2$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)
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).
data(ctrp2)data(ctrp2)
A list with elements of gene expression, drug sensitivities, and group membership.
n x p gene expression matrix
n x q drug sensitivities matrix
List of starting groups. Each group is represented by a vector of row indices for X, Y.
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)
data(ctrp2) X = ctrp2[["X"]] Y = ctrp2[["Y"]] groups = ctrp2[["groups"]]data(ctrp2) X = ctrp2[["X"]] Y = ctrp2[["Y"]] groups = ctrp2[["groups"]]
Selects clusters from bipartite graph-based hierarchical clustering with p-value less than or equal to a p-value cutoff.
getSignificantMergedGroups(results, p = 0.05)getSignificantMergedGroups(results, p = 0.05)
results |
list of results from bipartite graph-based hierarchical clustering |
p |
p-value cutoff |
list of results from bipartite graph-based hierarchical clustering, but only with clusters with p-value at or below p-value cutoff
# 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)# 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)
Main bipartite graph-based hierarchial clustering algorithm. Visit here for vignette on using the hierBipartite package.
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 )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 )
X |
an n x p matrix of variable set 1 (e.g. gene expression) |
Y |
an n x q matrix of variable set 2 (e.g. drug sensitivity) |
groups |
a list of starting group membership (e.g. list("1" = c(1,2,3), "2" = c(4,5,6)) means group 1 has samples 1, 2, 3, and group 2 has samples 4, 5, 6. |
link |
string indicating link function as input to hclust(). One of "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid". |
n_subsample |
number of subsampling to generate matrix B (see paper) |
subsampling_ratio |
fraction of samples to sample for subsampling to generate matrix B (see paper) |
p.value |
boolean for whether to generate p-values for each merge |
n_perm |
number of permutations for generating p-values. Ignored if p.value = FALSE |
parallel |
boolean for whether to parallelize subsampling and p-value generation step |
maxCores |
maximum number of cores to use (only applicable when parallel = TRUE) |
p_cutoff |
p-value cutoff that determines whether merge is significant. If p-value > p_cutoff, p-values will not be calculated for future merges involving current group. Ignored if p.value = FALSE. |
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
# Get a small subset of the test dataset data(ctrp2) groups = ctrp2$groups X = ctrp2$X Y = ctrp2$Y groupNames = names(groups) groupSmall = groups[groupNames[1:3]] ## Not run: # Basic call of hierBipartite() on small test dataset result0 = hierBipartite(X, Y, groupSmall) # Calling hierBipartite() with subsampling result1 = hierBipartite(X, Y, groupSmall, n_subsample = 100, subsampling_ratio = 0.90) # Calling hierBipartite() with p-value generation result2 = hierBipartite(X, Y, groupSmall, n_perm = 100, p.value = TRUE, p_cutoff = 0.10) # Calling hierBipartite() with both subsampling and p-value generation (expensive) result3 = hierBipartite(X, Y, groupSmall, n_subsample = 100, subsampling_ratio = 0.90, n_perm = 100, p.value = TRUE, p_cutoff = 0.10) ## End(Not run)# Get a small subset of the test dataset data(ctrp2) groups = ctrp2$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)
Computes nuclear norm-based dissimilarity measure between two matrices.
matrixDissimilarity(B1, B2)matrixDissimilarity(B1, B2)
B1 |
first p x q bipartite graph edge weight matrix |
B2 |
second p x q bipartite graph edge weight matrix |
nuclear norm-based dissimilarity
# 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$Y x1 = X[groups[["squamous_cell_carcinoma_esophagus"]], ] y1 = Y[groups[["squamous_cell_carcinoma_esophagus"]], ] ## Not run: B1 = constructBipartiteGraph(x1, y1) ## End(Not run) x2 = X[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ] y2 = Y[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ] ## Not run: B2 = constructBipartiteGraph(x2, y2) matrixDissimilarity(B1, B2) ## End(Not run)# Compute matrix dissimilarity in edge weight matrix between squamous cell # carcinoma, esophagus and squamous cell carcinoma, upper aerodigestive data(ctrp2) groups = ctrp2$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)
Generates null distribution of dissimilarity measures between group 1 (X1, Y1) and group 2 (X2, Y2).
null_distri(X1, Y1, X2, Y2, n.perm = 100, parallel = FALSE, maxCores = 7)null_distri(X1, Y1, X2, Y2, n.perm = 100, parallel = FALSE, maxCores = 7)
X1 |
an n x p matrix of variable set 1 (e.g. gene expression) from group 1 |
Y1 |
an n x q matrix of variable set 2 (e.g. drug sensitivity) from group 1 |
X2 |
an n x p matrix of variable set 1 (e.g. gene expression) from group 2 |
Y2 |
an n x q matrix of varaible set 2 (e.g. drug sensitivity) from group 2 |
n.perm |
number of null dissimilarity measures to generate |
parallel |
boolean for whether to parallelize permutation |
maxCores |
maximum number of cores to use (only applicable when parallel = TRUE) |
vector of length n.perm of null dissimilarity measures
# 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$Y x1 = X[groups[["squamous_cell_carcinoma_esophagus"]], ] y1 = Y[groups[["squamous_cell_carcinoma_esophagus"]], ] x2 = X[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ] y2 = Y[groups[["squamous_cell_carcinoma_upper_aerodigestive"]], ] ## Not run: dissimilarities = null_distri(x1, y1, x2, y2, n.perm = 100) ## End(Not run)# Get data for group squamous cell carcinoma, esophagus and for group # squamous cell carcinoma, upper aerodigestive data(ctrp2) groups = ctrp2$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)
Computes p-value as number of null dissimilarities less than or equal to observed dissimilarity.
p_value(dissimilarity, dissimilarities)p_value(dissimilarity, dissimilarities)
dissimilarity |
observed dissimilarity |
dissimilarities |
null distribution of dissimilarities |
p-value
# simulate null distribution of dissimilarities dissimilarities = runif(100, min = 0, max = 1) d = 0.10 p_value(d, dissimilarities)# simulate null distribution of dissimilarities dissimilarities = runif(100, min = 0, max = 1) d = 0.10 p_value(d, dissimilarities)
'scca' is used to perform sparse canonical covariance analysis (SCCA)
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)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)
X |
n-by-p data matrix, where n is the number of subjects and p is the number of variables |
Y |
n-by-q data matrix, where q is the number of variables |
penalty |
"HL" is the unbounded penalty proposed by Lee and Oh (2009). "LASSO" (Tibshirani, 1996), "SCAD" (Fan and Li, 2001) and "SOFT" (soft thresholding) are also available as other penalty options. Default is "HL". |
lamx |
A vector specifying grid points of the tuning parameter for X. Default is (1,2,3). |
lamy |
A vector specifying grid points of the tuning parameter for Y. Default is (1,2,3). |
nc |
Number of components (canonical vectors). Default is 1. |
tuning |
How to find optimal tuning parameters for the sparsity. If tuning="CV.full", then the tuning parameters are selected automatically via K-fold cross-validation by using 2-dim'l grid search. If "CV.alt", then a sequential 1-dim'l search method is applied instead of the 2-dim'l grid search. Default is "CV.alt". |
K |
Perform K-fold cross-validation. |
seed |
Seed number for initialization. A random initial point is generated for tuning="CV.alt". |
center |
The columns of the data matrix are centered to have mean zero. Default is TRUE. |
scale |
The columns of the data matrix are scaled to have variance 1. Default is FALSE. |
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.
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
Woojoo Lee, Donghwan Lee, Youngjo Lee and Yudi Pawitan
Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) Sparse Canonical Covariance Analysis for High-throughput Data
## 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)## 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)