mine_step3: Step 3: Select candidates based on gene significance

In almeidasilvaf/cageminer: Candidate Gene Miner

Step 3: Select candidates based on gene significance

Description

Step 3: Select candidates based on gene significance

Usage

mine_step3(
  exp,
  metadata,
  metadata_cols = 1,
  candidates,
  sample_group,
  min_cor = 0.2,
  alpha = 0.05,
  ...
)

Arguments

exp	Expression data frame with genes in row names and samples in column names or a SummarizedExperiment object.
metadata	Sample metadata with samples in row names and sample information in the first column. Ignored if exp is a SummarizedExperiment object, as the colData will be extracted from the object.
metadata_cols	A vector (either numeric or character) indicating which columns should be extracted from column metadata if exp is a SummarizedExperiment object. The vector can contain column indices (numeric) or column names (character). By default, all columns are used.
candidates	Character vector of candidate genes to be inspected.
sample_group	Level of sample metadata to be used for filtering in gene-trait correlation.
min_cor	Minimum correlation value for BioNERO::gene_significance(). Default: 0.2
alpha	Numeric indicating significance level. Default: 0.05
...	Additional arguments to BioNERO::gene_significance.

Value

A data frame with mined candidate genes and their correlation to the condition of interest.

Examples

data(pepper_se)
data(snp_pos)
data(gene_ranges)
data(guides)
data(gcn)
data(mine2)
set.seed(1)
mine3 <- mine_step3(
    exp = pepper_se,
    candidates = mine2$candidates,
    sample_group = "PRR_stress"
)

almeidasilvaf/cageminer documentation built on Sept. 9, 2023, 5:18 p.m.