find_markers {scGPS} | R Documentation

## find marker genes

### Description

Find DE genes from comparing one clust vs remaining

### Usage

    find_markers(expression_matrix = NULL, cluster = NULL,
      selected_cluster = NULL, fitType = "local",
      dispersion_method = "per-condition", sharing_Mode = "maximum")

### Arguments

`expression_matrix` | is a normalised expression matrix.
`cluster` | corresponding cluster information in the expression_matrix by running CORE clustering or using other methods.
`selected_cluster` | a vector of unique cluster ids to calculate
`fitType` | string specifying 'local' or 'parametric' for DEseq dispersion estimation
`dispersion_method` | one of the options c( 'pooled', 'pooled-CR', per-condition', 'blind' )
`sharing_Mode` | one of the options c("maximum", "fit-only", "gene-est-only")

### Value

a `list` containing sorted DESeq analysis results

### Author(s)

Quan Nguyen, 2017-11-25

### Examples

    day2 <- day_2_cardio_cell_sample
    mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts,
        GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
    # depending on the data, the DESeq::estimateDispersions function requires
    # suitable fitType
    # and dispersion_method options
    DEgenes <- find_markers(expression_matrix=assay(mixedpop1),
                            cluster = colData(mixedpop1)[,1],
                            selected_cluster=c(1), #can also run for more
                            #than one clusters, e.g.selected_cluster = c(1,2)
                            fitType = "parametric",
                            dispersion_method = "blind",
                            sharing_Mode="fit-only"
                            )
    names(DEgenes)

[Package scGPS version 1.2.0 Index]