find_markers: find marker genes

Description Usage Arguments Value Author(s) Examples

View source: R/Utilities.R

Description

Find DE genes from comparing one clust vs remaining

Usage

1
2
3
4
5
6
7
8
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
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)

IMB-Computational-Genomics-Lab/scGPS documentation built on Dec. 6, 2020, 3:20 p.m.