find_markers_in_bulk: Find markers in bulk

In IOBR/IOBR: Immune Oncology Biological Research

Find markers in bulk

Description

The goal of this function is to find relevant results from the given gene expression data and meta information.

Usage

find_markers_in_bulk(
  pdata,
  eset,
  group,
  id_pdata = "ID",
  nfeatures = 2000,
  top_n = 20,
  thresh.use = 0.25,
  only.pos = TRUE,
  min.pct = 0.25,
  npcs = 30
)

Arguments

pdata	A data frame containing the meta information of the samples.
eset	A matrix containing the gene expression data or signature score.
group	A string representing the column name for grouping.
id_pdata	A string representing the column name for sample IDs, default is "ID".
nfeatures	A numeric value indicating the top n features to select from the variable features, default is 2000.
top_n	A numeric value representing the top n markers to select in each cluster, default is 20.
thresh.use	A numeric value representing the marker selection threshold, default is 0.25.
only.pos	A logical value indicating whether to select only positive markers, default is TRUE.
min.pct	A numeric value representing the minimum percentage threshold for marker selection, default is 0.25.

Examples

# loading expression data
data("eset_tme_stad", package = "IOBR")
colnames(eset_tme_stad) <- substring(colnames(eset_tme_stad), 1, 12)

data("pdata_sig_tme", package = "IOBR")
res <- find_markers_in_bulk(pdata = pdata_sig_tme, eset = eset_tme_stad, group = "TMEcluster")

# extracting top 15 markers of each TME clusters
top15 <-  res$top_markers %>% dplyr:: group_by(cluster) %>%  dplyr::top_n(15, avg_log2FC)

# visualization
cols <- c('#2692a4','#fc0d3a','#ffbe0b')
DoHeatmap(res$sce, top15$gene, group.colors = cols )+ scale_fill_gradientn(colours = rev(colorRampPalette(RColorBrewer::brewer.pal(11,"RdBu"))(256)))

IOBR/IOBR documentation built on May 5, 2024, 2:34 p.m.