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"
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.