cageminer
Workflows
Standard Workflow
Integrate GWAS SNPs and coexpression networks to mine and score high-confidence candidate genes.
library(cageminer)
library(GenomicRanges)
# 1. Visualize SNP distribution across chromosomes
plot_snp_distribution(snp_pos)
plot_snp_circos(chr_length, gene_ranges, snp_pos)
# 2. Identify genes close to SNPs
candidates1 <- mine_step1(gene_ranges, snp_pos)
# 3. Infer a gene coexpression network
sft <- BioNERO::SFT_fit(pepper_se, net_type = "signed", cor_method = "pearson")
gcn <- BioNERO::exp2gcn(pepper_se, net_type = "signed", cor_method = "pearson",
module_merging_threshold = 0.8, SFTpower = sft$power)
# 4. Filter candidates in coexpression modules enriched in guide genes
candidates2 <- mine_step2(pepper_se, gcn = gcn, guides = guides$Gene, candidates = candidates1$ID)
# 5. Identify candidates with altered expression in a condition of interest
candidates3 <- mine_step3(pepper_se, candidates = candidates2$candidates, sample_group = "PRR_stress")
# Alternatively, perform steps 2-5 automatically using mine_candidates:
# candidates <- mine_candidates(gene_ranges = gene_ranges, marker_ranges = snp_pos,
# exp = pepper_se, gcn = gcn, guides = guides$Gene,
# sample_group = "PRR_stress")
# 6. Score and prioritize candidates
hubs <- BioNERO::get_hubs_gcn(pepper_se, gcn)
scored <- score_genes(candidates3, hubs$Gene, tfs$Gene_ID)
Note: Inputs are GRanges of SNP and gene coordinates, a SummarizedExperiment of expression data, and a vector of guide genes; output is a ranked data frame of scored candidate genes.
When to Use
- To integrate GWAS-derived SNPs with transcriptomic coexpression networks to prioritize candidate genes.
- To identify genes physically close to SNPs using
mine_step1.
- To find coexpression modules enriched in known guide genes using
mine_step2.
- To score and rank candidate genes based on their correlation with a condition, hub status, and transcription factor annotation using
score_genes.
When NOT to Use
- When you do not have a set of known "guide genes" associated with the phenotype of interest, as
mine_step2 relies on guide gene enrichment.
- When you only have SNP data without matching transcriptomic data.
Data Requirements
- Gene coordinates and SNP positions stored as
GRanges or GRangesList (for multiple traits) objects.
- Expression data stored as a
SummarizedExperiment object.
- Guide genes and transcription factors provided as character vectors of gene IDs.
Key Parameters
- gene_ranges: A
GRanges object containing gene coordinates.
- snp_pos / marker_ranges: A
GRanges or GRangesList object containing SNP positions.
- expand_intervals (TRUE): Logical indicating whether to expand SNP coordinates upstream and downstream.
- net_type ("signed"): Network type for GCN inference in
BioNERO::SFT_fit.
- cor_method ("pearson"): Correlation method for GCN inference.
- sample_group: The condition of interest in the sample metadata (e.g., "PRR_stress").
Best Practices
- Use
plot_snp_distribution and plot_snp_circos to visually inspect SNP distributions across chromosomes before running the mining pipeline.
- Use
simulate_windows to evaluate different sliding window sizes for selecting putative candidates in mine_step1.
- Ensure that the same gene IDs are used across the gene ranges, expression data, guide genes, and transcription factor lists.
Common Pitfalls
- No candidates found in Step 2: Ensure the guide genes are present in the expression dataset and that the coexpression network has well-defined modules.
- Mismatched gene IDs: Verify that gene IDs in
gene_ranges, pepper_se, and guides use the exact same nomenclature.
Alternatives
BioNERO for general gene coexpression network analysis.rtracklayer for importing genomic coordinates from GFF/GTF files.
Citations
- Almeida-Silva, F., & Venancio, T. M. (2022). cageminer: an R/Bioconductor package to prioritize candidate genes by integrating genome-wide association studies and gene coexpression networks. in silico Plants, 4(2), diac018.
References