mine_candidates {cageminer} | R Documentation |
Mine high-confidence candidate genes in a single step
mine_candidates( gene_ranges = NULL, marker_ranges = NULL, window = 2, expand_intervals = TRUE, gene_col = "ID", exp = NULL, gcn = NULL, guides = NULL, metadata, sample_group, min_cor = 0.2, alpha = 0.05, continuous = FALSE )
gene_ranges |
A GRanges object with genomic coordinates of all genes in the genome. |
marker_ranges |
Genomic positions of SNPs. For a single trait, a GRanges object. For multiple traits, a GRangesList or CompressedGRangesList object, with each element of the list representing SNP positions for a particular trait. |
window |
Sliding window (in Mb) upstream and downstream relative to each SNP. Default: 2. |
expand_intervals |
Logical indicating whether or not to expand markers that are represented by intervals. This is particularly useful if users want to use a custom interval defined by linkage disequilibrium, for example. Default: TRUE. |
gene_col |
Column of the GRanges object containing gene ID. Default: "ID", the default for gff/gff3 files imported with rtracklayer::import. |
exp |
Expression data frame with genes in row names and samples in column names or a SummarizedExperiment object. |
gcn |
Gene coexpression network returned by |
guides |
Guide genes as a character vector or as a data frame with genes in the first column and gene annotation class in the second column. |
metadata |
Sample metadata with samples in row names and sample
information in the first column. Ignored if |
sample_group |
Level of sample metadata to be used for filtering in gene-trait correlation. |
min_cor |
Minimum correlation value for
|
alpha |
Numeric indicating significance level. Default: 0.05 |
continuous |
Logical indicating whether metadata is continuous or not. Default: FALSE |
A data frame with mined candidate genes and their correlation to the condition of interest.
data(pepper_se) data(snp_pos) data(gene_ranges) data(guides) data(gcn) set.seed(1) candidates <- mine_candidates(gene_ranges, snp_pos, exp = pepper_se, gcn = gcn, guides = guides, sample_group = "PRR_stress")