clusterIndividualSamples {phemd}R Documentation

Computes cell subtype abundances for each sample

Description

Takes as input a Phemd object with all single-cell expression data of all single-cell samples in @data slot and Monocle2 object (already embedded and ordered) in @monocle_obj slot. Returns updated object with cell subtype frequencies of each sample in @data_cluster_weights slot

Usage

clusterIndividualSamples(obj, verbose = FALSE,
  cell_model = c("monocle2", "seurat"))

Arguments

obj

'Phemd' object containing single-cell expression data of all samples in @data slot and Monocle2 object (already embedded and ordered) in @monocle_obj slot

verbose

Boolean that determines whether progress (sequential processing of samples) should be printed. FALSE by default

cell_model

Either "monocle2" or "seurat" depending on method used to model cell state space

Details

embedCells and orderCellsMonocle need to be called before calling this function.

Value

'Phemd' object with cell subtype frequencies of each sample in @data_cluster_weights slot

Examples


my_phemdObj <- createDataObj(all_expn_data, all_genes, as.character(snames_data))
my_phemdObj_lg <- removeTinySamples(my_phemdObj, 10)
my_phemdObj_lg <- aggregateSamples(my_phemdObj_lg, max_cells=1000)
my_phemdObj_monocle <- embedCells(my_phemdObj_lg, data_model = 'gaussianff', sigma=0.02, maxIter=2)
my_phemdObj_monocle <- orderCellsMonocle(my_phemdObj_monocle)
my_phemdObj_final <- clusterIndividualSamples(my_phemdObj_monocle)


[Package phemd version 1.0.0 Index]