FindClassBasedOnMC {IRISFGM} | R Documentation |
This function is for performing Markov chain clustering regarding generated co-expression gene modules. This clustering method is working for relative small dataset.
If you have a large dataset, We recommend you should use Seurat clustering wrapped in this IRISFGM package. See details RunLTMG
,
RunDimensionReduction
, and RunClassification
.
FindClassBasedOnMC(object, ...) .final(object = NULL, method = "MCL", K = 5) ## S4 method for signature 'IRISFGM' FindClassBasedOnMC(object = NULL, method = "MCL", K = 5)
object |
input IRIS-FGM object |
... |
other arguments passed to methods |
method |
using MCL(Markov Cluster) algorithm to predict clusters. There is alternative option which is 'SC.' ( Unnormalized spectral clustering function. Uses Partitioning Around Medoids clustering instead of K-means.) |
K |
expected number of predicted clusters when you are using 'SC' method for cell clustering and this parameter does not work for 'MCL' |
It will reture cell clustering results based on MCL method.
data(example_object) example_object<- RunLTMG(example_object,Gene_use = "200") example_object <- CalBinaryMultiSignal(example_object) # Due to generate intermedie files, please make sure to set working directory example_object <- RunBicluster(example_object, DiscretizationModel = 'LTMG', OpenDual = FALSE, NumBlockOutput = 1000, BlockOverlap = 0.7, BlockCellMin = 15) example_object <- FindClassBasedOnMC(example_object)