find_markers {scGPS} | R Documentation |
Find DE genes from comparing one clust vs remaining
find_markers( expression_matrix = NULL, cluster = NULL, selected_cluster = NULL, fitType = "local", dispersion_method = "per-condition", sharing_Mode = "maximum" )
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") |
a list
containing sorted DESeq analysis results
Quan Nguyen, 2017-11-25
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)