View source: R/interpretation.R
top_differential_markers | R Documentation |
Retrieve top marker genes from list of differential analysis
top_differential_markers( object, top = 1, gene_col = "gene", logFC_col = "avg_log2FC", qvalue_col = "p_val_adj", order_by = c("logFC_col", "qvalue_col")[1], pseudogene_pattern = NULL )
object |
A data.frame of differential features at each
clustering iteration produced by |
top |
An integer specifying the number of top features to retrieve per cluster. |
gene_col |
A character specifying the column in which to retrieve the gene / feature name. |
logFC_col |
A character specifying the column in which to retrieve the logFC. |
qvalue_col |
A character specifying the column in which to retrieve the adjusted p.value. |
order_by |
A character specifying the column by which to order the top markers (default to logFC_col. |
pseudogene_pattern |
A character specifying the pattern of 'pseudo-genes' to exclude from the top markers. |
#scRNA data("IDC_DA_scRNA", package = "IDclust") top_differential_markers( IDC_DA_scRNA, top = 1, gene_col = "gene", logFC_col = "avg_log2FC", qvalue_col = "p_val_adj", order_by = "logFC_col", pseudogene_pattern = NULL ) #scEpigenomics data("scExp", package = "IDclust") data("IDC_DA_scEpigenomics", package = "IDclust") # We must first add the gene information to the DA list: IDC_DA_scEpigenomics = add_gene_to_DA_list( scExp = scExp, IDC_DA = IDC_DA_scEpigenomics, feature_ID_col = "ID", gene_col = "Gene", distanceToTSS = 1000, split = TRUE, split_char = ", " ) top_differential_markers( IDC_DA_scEpigenomics, top = 3, gene_col = "Gene", logFC_col = "logFC", qvalue_col = "qval", order_by = "logFC_col", pseudogene_pattern = "Rik|Vmn|Gm|AW" )
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