## Generate some random data
Data <- matrix(abs(rnorm(3000, sd=2)),ncol=10,nrow=30)
## Initializing SincellObject
mySincellObject <- sc_InitializingSincellObject(Data)
## Assessmet of cell-to-cell distance matrix after dimensionality reduction
## with Principal Component Analysis (PCA), with Independent Component
## Analysis (ICA), or with non-metric Multidimensional Scaling (nonmetric-MDS)
mySincellObject_PCA <- sc_DimensionalityReductionObj(mySincellObject,
method="PCA",dim=2)
mySincellObject_ICA <- sc_DimensionalityReductionObj(mySincellObject,
method="ICA",dim=2)
mySincellObject_classicalMDS <- sc_DimensionalityReductionObj(mySincellObject,
method="classical-MDS",dim=2)
mySincellObject_nonmetricMDS <- sc_DimensionalityReductionObj(mySincellObject,
method="nonmetric-MDS",dim=2)
## Assessment of cell-state hierarchy
mySincellObject_PCA<- sc_GraphBuilderObj(mySincellObject_PCA,
graph.algorithm="SST")
mySincellObject_ICA<- sc_GraphBuilderObj(mySincellObject_ICA,
graph.algorithm="SST")
mySincellObject_classicalMDS<- sc_GraphBuilderObj(mySincellObject_classicalMDS,
graph.algorithm="SST")
mySincellObject_nonmetricMDS<- sc_GraphBuilderObj(mySincellObject_nonmetricMDS,
graph.algorithm="SST")
## Comparisson of hierarchies obtained from different methods
myComparissonOfGraphs<-sc_ComparissonOfGraphs(
mySincellObject_PCA[["cellstateHierarchy"]],
mySincellObject_ICA[["cellstateHierarchy"]],
mySincellObject_classicalMDS[["cellstateHierarchy"]],
mySincellObject_nonmetricMDS[["cellstateHierarchy"]],
graph.names=c("PCA","ICA","classicalMDS","nonmetricMDS")
)
plot(hclust(myComparissonOfGraphs))Run the code above in your browser using DataLab