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 1.1.1 Index]