Core GeneTrackR workflow
Introduction
If you are new to the package, read Getting started with GeneTrackR first, then GeneTrackR deterministic example data for the shared file inventory, coordinate conventions, and designed truth. This article assumes that installation and the demo data model are already familiar. It is intentionally compact; use Complete candidate-locus workflow for the production-oriented path that also integrates signal tracks, interval features, browser inspection, reproducible export, and large-data guidance.
This vignette uses the single deterministic gtr_demo_*
dataset shipped with GeneTrackR. The same annotation, VCF, phenotype
table, and designed truth are shared across annotation, variant,
haplotype, LD, refinement, and variant-effect workflows.
Package documentation: https://renscq.github.io/GeneTrackR/.
Installation
After Bioconductor acceptance, install GeneTrackR with
BiocManager:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("GeneTrackR")
library(GeneTrackR)The development version remains available from the GeneTrackR GitHub repository.
Load the canonical demo inputs
library(GeneTrackR)
gp_file <- system.file("extdata", "gtr_demo.genePredExt", package = "GeneTrackR")
gtf_file <- system.file("extdata", "gtr_demo.gtf", package = "GeneTrackR")
gff_file <- system.file("extdata", "gtr_demo.gff3", package = "GeneTrackR")
bed_file <- system.file("extdata", "gtr_demo_features.bed", package = "GeneTrackR")
vcf_file <- system.file("extdata", "gtr_demo_variants.vcf", package = "GeneTrackR")
pheno_file <- system.file("extdata", "gtr_demo_pheno.tsv", package = "GeneTrackR")
gp <- read_genepred(gp_file, format = "genePredExt", verbose = FALSE)
gtf <- read_gtf(gtf_file, verbose = FALSE, progress = FALSE)
gff <- read_gff(gff_file, verbose = FALSE, progress = FALSE)
features <- read_bed(bed_file, verbose = FALSE, progress = FALSE)
vcf <- read_vcf(vcf_file, mode = "memory", verbose = FALSE)
pheno <- read_pheno(pheno_file, verbose = FALSE)The three annotation formats describe the same canonical 20-gene,
24-transcript model. The VCF contains 56 designed variants in 36
samples. Phenotype row order is intentionally different from VCF sample
order, so downstream analyses must align by sample_id.
Object contracts used in the workflow
The workflow deliberately passes package objects between functions
rather than rebuilding intermediate tables. gp is a
GenePred, features is a
FeatureTrack, vcf is a
VariantTrack, and pheno is a
data.table. The VariantTrack feeds
variant-phenotype and LD analyses; hap_gene_variant()
creates a HapVariant for haplotype plots,
haplotype-phenotype analysis, refinement, and variant-effect analysis;
refine_haplotype() creates a HapRefined for
refined plots.
Plotting functions have two return conventions in this vignette:
- gene/track/haplotype-variant plots return a ggplot/patchwork figure directly;
- phenotype and variant-effect plots return result objects with a
$figurefield plus analysis tables; plot_ld_block()returns the updatedLDTrackby default and stores its figure in$figure.
Annotation and feature retrieval
GeneA is a positive-strand protein-coding gene with two
transcripts. GeneB is a negative-strand two-transcript
gene.
genea <- retrieve_feature(gp, gene_id = "GeneA")
geneb <- retrieve_feature(gp, gene_id = "GeneB")
genea$genes
#> gene_id chrom strand gene_start gene_end n_transcripts gene_type
#> <char> <char> <char> <int> <int> <int> <char>
#> 1: GeneA chr1 + 12340001 12352000 2 coding
geneb$genes
#> gene_id chrom strand gene_start gene_end n_transcripts gene_type
#> <char> <char> <char> <int> <int> <int> <char>
#> 1: GeneB chr1 - 12356001 12366500 2 codingThe BED file contains promoters, enhancers, candidate regions, a QTL interval, repeats, and conserved regions around the designed loci.
head(features$data)
#> feature_id name chrom start end
#> <char> <char> <char> <int> <int>
#> 1: BED_4 SeedWeight_QTL|QTL chr1 12330001 12380000
#> 2: BED_2 GeneA_enhancer|enhancer chr1 12338201 12338750
#> 3: BED_1 GeneA_promoter|promoter chr1 12339001 12340000
#> 4: BED_3 GeneA_candidate_region|candidate_region chr1 12339701 12352000
#> 5: BED_5 GeneA_repeat|repeat chr1 12343201 12343800
#> 6: BED_6 GeneA_conserved|conserved_region chr1 12350401 12350900
#> type level score strand source gene_id transcript_id parent_id gene_type
#> <char> <char> <num> <char> <char> <char> <char> <char> <char>
#> 1: BED feature 0 * BED <NA> <NA> <NA> <NA>
#> 2: BED feature 0 * BED <NA> <NA> <NA> <NA>
#> 3: BED feature 0 + BED <NA> <NA> <NA> <NA>
#> 4: BED feature 0 * BED <NA> <NA> <NA> <NA>
#> 5: BED feature 0 * BED <NA> <NA> <NA> <NA>
#> 6: BED feature 0 * BED <NA> <NA> <NA> <NA>
#> exon_number phase attribute
#> <int> <char> <char>
#> 1: NA <NA> <NA>
#> 2: NA <NA> <NA>
#> 3: NA <NA> <NA>
#> 4: NA <NA> <NA>
#> 5: NA <NA> <NA>
#> 6: NA <NA> <NA>Variant retrieval
The main GeneA interval contains SNPs, insertions, deletions, a high-LD block, and a deliberately incomplete upstream variant.
genea_variants <- retrieve_vcf(
vcf,
chrom = "chr1",
start = 12339700,
end = 12352000,
as = "VariantTrack",
verbose = FALSE
)
genea_variants
#> <VariantTrack>
#> variants : 12
#> format : VCF
#> coordinate: 1-based position
genea_variants$data[, c("variant_id", "chrom", "pos", "ref", "alt"), with = FALSE]
#> variant_id chrom pos ref alt
#> <char> <char> <int> <char> <char>
#> 1: varAup01 chr1 12339750 A T
#> 2: varA01 chr1 12340250 A G
#> 3: varA02 chr1 12340600 T C
#> 4: varA03 chr1 12342550 C G
#> 5: varLD01 chr1 12342620 G A
#> 6: varLD02 chr1 12342710 A T
#> 7: varLD03 chr1 12342805 C T
#> 8: varLD04 chr1 12342920 G C
#> 9: varLD05 chr1 12343040 T G
#> 10: varLD06 chr1 12343180 A C
#> 11: varA04 chr1 12344500 A ATG
#> 12: varA05 chr1 12351050 ATG Aretrieve_vcf() returns a data.table by
default; as = "VariantTrack" is set here because the subset
is retained as a package object.
GeneA haplotypes
The GeneA gene body was designed to produce four balanced genotype-defined groups of nine samples each.
hap <- hap_gene_variant(
vcf,
annotation = gp,
gene_id = "GeneA",
genotype_mode = "string",
min_variant_number = 1
)
#> [GeneTrackR] Retrieved variants: 11.
hap$haplotypes
#> Key: <hap_id>
#> hap_id sample_n samples varA01 varA02 varA03
#> <char> <int> <char> <char> <char> <char>
#> 1: Hap1 9 S10;S11;S12;S13;S14;S15;S16;S17;S18 A C C
#> 2: Hap2 9 S01;S02;S03;S04;S05;S06;S07;S08;S09 A T C
#> 3: Hap3 9 S19;S20;S21;S22;S23;S24;S25;S26;S27 G C G
#> 4: Hap4 9 S28;S29;S30;S31;S32;S33;S34;S35;S36 G T G
#> varLD01 varLD02 varLD03 varLD04 varLD05 varLD06 varA04 varA05
#> <char> <char> <char> <char> <char> <char> <char> <char>
#> 1: G A C G T A A i3
#> 2: G A C G T A A i3
#> 3: A T T C G C i3 i3
#> 4: A T T C G C i3 Ahap_figure <- plot_hap_variant(
hap,
annotation = gp,
min_hap_samples = 3,
show_reference_row = TRUE,
gene_pos_x_angle = 90,
text_size = 9
)
hap_figure
Haplotype-phenotype association
seed_weight has a designed GeneA haplotype effect.
flowering_time is a negative-control trait with the same
distribution in all four designed groups.
hap_pheno <- plot_hap_pheno(
hap,
phenotype = pheno,
traits = c("seed_weight", "flowering_time"),
min_hap_samples = 3,
facet_ncol = 2,
text_size = 9
)
#> Warning in melt.data.table(dt, id.vars = c("sample_id", "hap_id"), measure.vars
#> = traits, : 'measure.vars' [seed_weight, flowering_time, ...] are not all of
#> the same type. By order of hierarchy, the molten data value column will be of
#> type 'double'. All measure variables not of type 'double' will be coerced too.
#> Check DETAILS in ?melt.data.table for more on coercion.
hap_pheno$figure
hap_pheno$pvalue
#> trait group1 group2 p_value p_adj method
#> <char> <char> <char> <num> <num> <char>
#> 1: seed_weight Hap2 Hap1 7.740671e-05 7.740671e-05 t.test
#> 2: seed_weight Hap2 Hap3 1.344894e-14 8.069366e-14 t.test
#> 3: seed_weight Hap2 Hap4 4.343207e-13 8.686413e-13 t.test
#> 4: seed_weight Hap1 Hap3 4.343207e-13 8.686413e-13 t.test
#> 5: seed_weight Hap1 Hap4 3.540311e-11 5.310467e-11 t.test
#> 6: seed_weight Hap3 Hap4 7.740671e-05 7.740671e-05 t.test
#> 7: flowering_time Hap2 Hap1 1.000000e+00 1.000000e+00 t.test
#> 8: flowering_time Hap2 Hap3 1.000000e+00 1.000000e+00 t.test
#> 9: flowering_time Hap2 Hap4 1.000000e+00 1.000000e+00 t.test
#> 10: flowering_time Hap1 Hap3 1.000000e+00 1.000000e+00 t.test
#> 11: flowering_time Hap1 Hap4 1.000000e+00 1.000000e+00 t.test
#> 12: flowering_time Hap3 Hap4 1.000000e+00 1.000000e+00 t.testSingle-variant phenotype association
varA03 was designed so its ALT genotype is associated
with higher protein_content.
variant_pheno <- plot_variant_pheno(
vcf,
phenotype = pheno,
variant_id = "varA03",
traits = "protein_content",
genotype_mode = "code",
min_group_samples = 3,
text_size = 9
)
variant_pheno$figure
Linkage disequilibrium
varLD01 through varLD12 provide a
deterministic LD-gradient example with a six-variant perfect core and
progressively weaker downstream linkage.
ld <- compute_ld_block(
vcf,
chrom = "chr1",
start = 12342620,
end = 12355500,
variant_type = "snp",
method = "r2",
verbose = FALSE
)
ld$data
#> variant_i variant_j index_i index_j pos_i pos_j distance_bp n_samples
#> <char> <char> <int> <int> <int> <int> <int> <int>
#> 1: varLD01 varLD02 1 2 12342620 12342710 90 36
#> 2: varLD01 varLD03 1 3 12342620 12342805 185 36
#> 3: varLD01 varLD04 1 4 12342620 12342920 300 36
#> 4: varLD01 varLD05 1 5 12342620 12343040 420 36
#> 5: varLD01 varLD06 1 6 12342620 12343180 560 36
#> 6: varLD01 varLD07 1 7 12342620 12352500 9880 36
#> 7: varLD01 varLD08 1 8 12342620 12353100 10480 36
#> 8: varLD01 varLD09 1 9 12342620 12353700 11080 36
#> 9: varLD01 varLD10 1 10 12342620 12354300 11680 36
#> 10: varLD01 varLD11 1 11 12342620 12354900 12280 36
#> 11: varLD01 varLD12 1 12 12342620 12355500 12880 36
#> 12: varLD02 varLD03 2 3 12342710 12342805 95 36
#> 13: varLD02 varLD04 2 4 12342710 12342920 210 36
#> 14: varLD02 varLD05 2 5 12342710 12343040 330 36
#> 15: varLD02 varLD06 2 6 12342710 12343180 470 36
#> 16: varLD02 varLD07 2 7 12342710 12352500 9790 36
#> 17: varLD02 varLD08 2 8 12342710 12353100 10390 36
#> 18: varLD02 varLD09 2 9 12342710 12353700 10990 36
#> 19: varLD02 varLD10 2 10 12342710 12354300 11590 36
#> 20: varLD02 varLD11 2 11 12342710 12354900 12190 36
#> 21: varLD02 varLD12 2 12 12342710 12355500 12790 36
#> 22: varLD03 varLD04 3 4 12342805 12342920 115 36
#> 23: varLD03 varLD05 3 5 12342805 12343040 235 36
#> 24: varLD03 varLD06 3 6 12342805 12343180 375 36
#> 25: varLD03 varLD07 3 7 12342805 12352500 9695 36
#> 26: varLD03 varLD08 3 8 12342805 12353100 10295 36
#> 27: varLD03 varLD09 3 9 12342805 12353700 10895 36
#> 28: varLD03 varLD10 3 10 12342805 12354300 11495 36
#> 29: varLD03 varLD11 3 11 12342805 12354900 12095 36
#> 30: varLD03 varLD12 3 12 12342805 12355500 12695 36
#> 31: varLD04 varLD05 4 5 12342920 12343040 120 36
#> 32: varLD04 varLD06 4 6 12342920 12343180 260 36
#> 33: varLD04 varLD07 4 7 12342920 12352500 9580 36
#> 34: varLD04 varLD08 4 8 12342920 12353100 10180 36
#> 35: varLD04 varLD09 4 9 12342920 12353700 10780 36
#> 36: varLD04 varLD10 4 10 12342920 12354300 11380 36
#> 37: varLD04 varLD11 4 11 12342920 12354900 11980 36
#> 38: varLD04 varLD12 4 12 12342920 12355500 12580 36
#> 39: varLD05 varLD06 5 6 12343040 12343180 140 36
#> 40: varLD05 varLD07 5 7 12343040 12352500 9460 36
#> 41: varLD05 varLD08 5 8 12343040 12353100 10060 36
#> 42: varLD05 varLD09 5 9 12343040 12353700 10660 36
#> 43: varLD05 varLD10 5 10 12343040 12354300 11260 36
#> 44: varLD05 varLD11 5 11 12343040 12354900 11860 36
#> 45: varLD05 varLD12 5 12 12343040 12355500 12460 36
#> 46: varLD06 varLD07 6 7 12343180 12352500 9320 36
#> 47: varLD06 varLD08 6 8 12343180 12353100 9920 36
#> 48: varLD06 varLD09 6 9 12343180 12353700 10520 36
#> 49: varLD06 varLD10 6 10 12343180 12354300 11120 36
#> 50: varLD06 varLD11 6 11 12343180 12354900 11720 36
#> 51: varLD06 varLD12 6 12 12343180 12355500 12320 36
#> 52: varLD07 varLD08 7 8 12352500 12353100 600 36
#> 53: varLD07 varLD09 7 9 12352500 12353700 1200 36
#> 54: varLD07 varLD10 7 10 12352500 12354300 1800 36
#> 55: varLD07 varLD11 7 11 12352500 12354900 2400 36
#> 56: varLD07 varLD12 7 12 12352500 12355500 3000 36
#> 57: varLD08 varLD09 8 9 12353100 12353700 600 36
#> 58: varLD08 varLD10 8 10 12353100 12354300 1200 36
#> 59: varLD08 varLD11 8 11 12353100 12354900 1800 36
#> 60: varLD08 varLD12 8 12 12353100 12355500 2400 36
#> 61: varLD09 varLD10 9 10 12353700 12354300 600 36
#> 62: varLD09 varLD11 9 11 12353700 12354900 1200 36
#> 63: varLD09 varLD12 9 12 12353700 12355500 1800 36
#> 64: varLD10 varLD11 10 11 12354300 12354900 600 36
#> 65: varLD10 varLD12 10 12 12354300 12355500 1200 36
#> 66: varLD11 varLD12 11 12 12354900 12355500 600 36
#> variant_i variant_j index_i index_j pos_i pos_j distance_bp n_samples
#> <char> <char> <int> <int> <int> <int> <int> <int>
#> r r2 D Dprime_signed Dprime p_i p_j
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 2: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 3: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 4: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 5: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 6: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 7: 0.7777778 0.60493827 0.38888889 1.0000000 1.0000000 0.5000000 0.5000000
#> 8: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> 9: 0.5007734 0.25077399 0.25000000 1.0000000 1.0000000 0.5000000 0.5277778
#> 10: 0.3333333 0.11111111 0.16666667 0.6666667 0.6666667 0.5000000 0.5000000
#> 11: 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.5000000 0.5000000
#> 12: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 13: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 14: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 15: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 16: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 17: 0.7777778 0.60493827 0.38888889 1.0000000 1.0000000 0.5000000 0.5000000
#> 18: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> 19: 0.5007734 0.25077399 0.25000000 1.0000000 1.0000000 0.5000000 0.5277778
#> 20: 0.3333333 0.11111111 0.16666667 0.6666667 0.6666667 0.5000000 0.5000000
#> 21: 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.5000000 0.5000000
#> 22: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 23: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 24: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 25: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 26: 0.7777778 0.60493827 0.38888889 1.0000000 1.0000000 0.5000000 0.5000000
#> 27: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> 28: 0.5007734 0.25077399 0.25000000 1.0000000 1.0000000 0.5000000 0.5277778
#> 29: 0.3333333 0.11111111 0.16666667 0.6666667 0.6666667 0.5000000 0.5000000
#> 30: 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.5000000 0.5000000
#> 31: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 32: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 33: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 34: 0.7777778 0.60493827 0.38888889 1.0000000 1.0000000 0.5000000 0.5000000
#> 35: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> 36: 0.5007734 0.25077399 0.25000000 1.0000000 1.0000000 0.5000000 0.5277778
#> 37: 0.3333333 0.11111111 0.16666667 0.6666667 0.6666667 0.5000000 0.5000000
#> 38: 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.5000000 0.5000000
#> 39: 1.0000000 1.00000000 0.50000000 1.0000000 1.0000000 0.5000000 0.5000000
#> 40: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 41: 0.7777778 0.60493827 0.38888889 1.0000000 1.0000000 0.5000000 0.5000000
#> 42: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> 43: 0.5007734 0.25077399 0.25000000 1.0000000 1.0000000 0.5000000 0.5277778
#> 44: 0.3333333 0.11111111 0.16666667 0.6666667 0.6666667 0.5000000 0.5000000
#> 45: 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.5000000 0.5000000
#> 46: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 47: 0.7777778 0.60493827 0.38888889 1.0000000 1.0000000 0.5000000 0.5000000
#> 48: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> 49: 0.5007734 0.25077399 0.25000000 1.0000000 1.0000000 0.5000000 0.5277778
#> 50: 0.3333333 0.11111111 0.16666667 0.6666667 0.6666667 0.5000000 0.5000000
#> 51: 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.5000000 0.5000000
#> 52: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 53: 0.7777778 0.60493827 0.38888889 1.0000000 1.0000000 0.5000000 0.5000000
#> 54: 0.6120564 0.37461300 0.30555556 1.0000000 1.0000000 0.5000000 0.5277778
#> 55: 0.4444444 0.19753086 0.22222222 0.8888889 0.8888889 0.5000000 0.5000000
#> 56: 0.1111111 0.01234568 0.05555556 0.2222222 0.2222222 0.5000000 0.5000000
#> 57: 0.8888889 0.79012346 0.44444444 1.0000000 1.0000000 0.5000000 0.5000000
#> 58: 0.7233393 0.52321981 0.36111111 1.0000000 1.0000000 0.5000000 0.5277778
#> 59: 0.5555556 0.30864198 0.27777778 1.0000000 1.0000000 0.5000000 0.5000000
#> 60: 0.2222222 0.04938272 0.11111111 0.4444444 0.4444444 0.5000000 0.5000000
#> 61: 0.8346223 0.69659443 0.41666667 1.0000000 1.0000000 0.5000000 0.5277778
#> 62: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> 63: 0.3333333 0.11111111 0.16666667 0.6666667 0.6666667 0.5000000 0.5000000
#> 64: 0.8346223 0.69659443 0.41666667 1.0000000 1.0000000 0.5277778 0.5000000
#> 65: 0.5007734 0.25077399 0.25000000 1.0000000 1.0000000 0.5277778 0.5000000
#> 66: 0.6666667 0.44444444 0.33333333 1.0000000 1.0000000 0.5000000 0.5000000
#> r r2 D Dprime_signed Dprime p_i p_j
#> <num> <num> <num> <num> <num> <num> <num>
#> ld method
#> <num> <char>
#> 1: 1.00000000 r2
#> 2: 1.00000000 r2
#> 3: 1.00000000 r2
#> 4: 1.00000000 r2
#> 5: 1.00000000 r2
#> 6: 0.79012346 r2
#> 7: 0.60493827 r2
#> 8: 0.44444444 r2
#> 9: 0.25077399 r2
#> 10: 0.11111111 r2
#> 11: 0.00000000 r2
#> 12: 1.00000000 r2
#> 13: 1.00000000 r2
#> 14: 1.00000000 r2
#> 15: 1.00000000 r2
#> 16: 0.79012346 r2
#> 17: 0.60493827 r2
#> 18: 0.44444444 r2
#> 19: 0.25077399 r2
#> 20: 0.11111111 r2
#> 21: 0.00000000 r2
#> 22: 1.00000000 r2
#> 23: 1.00000000 r2
#> 24: 1.00000000 r2
#> 25: 0.79012346 r2
#> 26: 0.60493827 r2
#> 27: 0.44444444 r2
#> 28: 0.25077399 r2
#> 29: 0.11111111 r2
#> 30: 0.00000000 r2
#> 31: 1.00000000 r2
#> 32: 1.00000000 r2
#> 33: 0.79012346 r2
#> 34: 0.60493827 r2
#> 35: 0.44444444 r2
#> 36: 0.25077399 r2
#> 37: 0.11111111 r2
#> 38: 0.00000000 r2
#> 39: 1.00000000 r2
#> 40: 0.79012346 r2
#> 41: 0.60493827 r2
#> 42: 0.44444444 r2
#> 43: 0.25077399 r2
#> 44: 0.11111111 r2
#> 45: 0.00000000 r2
#> 46: 0.79012346 r2
#> 47: 0.60493827 r2
#> 48: 0.44444444 r2
#> 49: 0.25077399 r2
#> 50: 0.11111111 r2
#> 51: 0.00000000 r2
#> 52: 0.79012346 r2
#> 53: 0.60493827 r2
#> 54: 0.37461300 r2
#> 55: 0.19753086 r2
#> 56: 0.01234568 r2
#> 57: 0.79012346 r2
#> 58: 0.52321981 r2
#> 59: 0.30864198 r2
#> 60: 0.04938272 r2
#> 61: 0.69659443 r2
#> 62: 0.44444444 r2
#> 63: 0.11111111 r2
#> 64: 0.69659443 r2
#> 65: 0.25077399 r2
#> 66: 0.44444444 r2
#> ld method
#> <num> <char>ld <- plot_ld_block(
ld,
annotation = gp,
show_region = TRUE,
show_variant_labels = FALSE,
font = 9
)
ld$figure
The GeneT interval on chromosome 2 contains exactly two
variants and is retained as the stable two-variant LD geometry case.
ld_pair <- compute_ld_block(
vcf,
chrom = "chr2",
start = 16995001,
end = 17006000,
method = "r2",
verbose = FALSE
)
ld_pair_figure <- plot_ld_block(ld_pair, return_object = FALSE, font = 9)
ld_pair_figure
Phenotype-guided haplotype refinement
refined <- refine_haplotype(
hap,
phenotype = pheno,
traits = "protein_content",
min_hap_samples = 3,
effect_threshold = 0.5
)
refined$refined_haplotypes
#> hap_id original_hap_n original_hap_ids sample_n
#> <char> <int> <char> <int>
#> 1: RHap1 2 Hap1;Hap2 18
#> 2: RHap2 2 Hap3;Hap4 18
#> samples
#> <char>
#> 1: S10;S11;S12;S13;S14;S15;S16;S17;S18;S01;S02;S03;S04;S05;S06;S07;S08;S09
#> 2: S19;S20;S21;S22;S23;S24;S25;S26;S27;S28;S29;S30;S31;S32;S33;S34;S35;S36
#> varA01 varA02 varA03 varLD01 varLD02 varLD03 varLD04 varLD05 varLD06 varA04
#> <char> <char> <char> <char> <char> <char> <char> <char> <char> <char>
#> 1: A mixed C G A C G T A A
#> 2: G mixed G A T T C G C i3
#> varA05
#> <char>
#> 1: i3
#> 2: mixedrefined_pheno <- plot_refined_hap_pheno(
refined,
phenotype = pheno,
traits = "protein_content",
min_hap_samples = 3,
text_size = 9
)
refined_pheno$figure
refined_pheno$pvalue
#> trait group1 group2 p_value p_adj method
#> <char> <char> <char> <num> <num> <char>
#> 1: protein_content RHap1 RHap2 2.008652e-37 2.008652e-37 t.testThe refined haplotype-variant plot returns a figure directly:
refined_variant_figure <- plot_refined_hap_variant(
refined,
annotation = gp,
min_hap_samples = 3,
text_size = 9
)
refined_variant_figure
Variant-effect prioritization
effect_res <- plot_variant_effect(
hap,
phenotype = pheno,
traits = "protein_content",
min_group_samples = 3,
x_axis = "position",
text_size = 9
)
effect_res$figure
effect_res$effect
#> variant_id trait group_n sample_n effect abs_effect low_group
#> <char> <char> <int> <int> <num> <num> <char>
#> 1: varA01 protein_content 2 36 6 6 A
#> 2: varA02 protein_content 2 36 0 0 C
#> 3: varA03 protein_content 2 36 6 6 C
#> 4: varLD01 protein_content 2 36 -6 6 A
#> 5: varLD02 protein_content 2 36 6 6 A
#> 6: varLD03 protein_content 2 36 6 6 C
#> 7: varLD04 protein_content 2 36 -6 6 C
#> 8: varLD05 protein_content 2 36 -6 6 G
#> 9: varLD06 protein_content 2 36 6 6 A
#> 10: varA04 protein_content 2 36 6 6 A
#> 11: varA05 protein_content 2 36 -4 4 A
#> high_group low_group_mean high_group_mean p_value test_method
#> <char> <num> <num> <num> <char>
#> 1: G 38 44 2.008652e-37 t.test
#> 2: T 41 41 1.000000e+00 t.test
#> 3: G 38 44 2.008652e-37 t.test
#> 4: G 44 38 2.008652e-37 t.test
#> 5: T 38 44 2.008652e-37 t.test
#> 6: T 38 44 2.008652e-37 t.test
#> 7: G 44 38 2.008652e-37 t.test
#> 8: T 44 38 2.008652e-37 t.test
#> 9: C 38 44 2.008652e-37 t.test
#> 10: i3 38 44 2.008652e-37 t.test
#> 11: i3 44 40 1.190766e-07 t.test
#> group_summary p_adj variant_label chrom pos
#> <char> <num> <char> <char> <int>
#> 1: A:n=18,mean=38;G:n=18,mean=44 2.455019e-37 varA01 chr1 12340250
#> 2: C:n=18,mean=41;T:n=18,mean=41 1.000000e+00 varA02 chr1 12340600
#> 3: C:n=18,mean=38;G:n=18,mean=44 2.455019e-37 varA03 chr1 12342550
#> 4: A:n=18,mean=44;G:n=18,mean=38 2.455019e-37 varLD01 chr1 12342620
#> 5: A:n=18,mean=38;T:n=18,mean=44 2.455019e-37 varLD02 chr1 12342710
#> 6: C:n=18,mean=38;T:n=18,mean=44 2.455019e-37 varLD03 chr1 12342805
#> 7: C:n=18,mean=44;G:n=18,mean=38 2.455019e-37 varLD04 chr1 12342920
#> 8: G:n=18,mean=44;T:n=18,mean=38 2.455019e-37 varLD05 chr1 12343040
#> 9: A:n=18,mean=38;C:n=18,mean=44 2.455019e-37 varLD06 chr1 12343180
#> 10: A:n=18,mean=38;i3:n=18,mean=44 2.455019e-37 varA04 chr1 12344500
#> 11: A:n=9,mean=44;i3:n=27,mean=40 1.309843e-07 varA05 chr1 12351050
#> variant_index plot_effect plot_log10_padj x_value
#> <int> <num> <num> <num>
#> 1: 1 6 36.609945 12340250
#> 2: 2 0 0.000000 12340600
#> 3: 3 6 36.609945 12342550
#> 4: 4 6 36.609945 12342620
#> 5: 5 6 36.609945 12342710
#> 6: 6 6 36.609945 12342805
#> 7: 7 6 36.609945 12342920
#> 8: 8 6 36.609945 12343040
#> 9: 9 6 36.609945 12343180
#> 10: 10 6 36.609945 12344500
#> 11: 11 4 6.882781 12351050The deterministic model is intended to make documentation examples and regression tests share the same biological and statistical expectations rather than maintaining separate example datasets for each module.
Session information
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] GeneTrackR_0.99.2 rmarkdown_2.32
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 jsonlite_2.0.0 crayon_1.5.3
#> [4] compiler_4.6.1 Biostrings_2.81.9 bitops_1.1-0
#> [7] Rsamtools_2.29.0 GenomicRanges_1.65.4 parallel_4.6.1
#> [10] jquerylib_0.1.4 IRanges_2.47.5 Seqinfo_1.3.2
#> [13] scales_1.4.0 BiocParallel_1.47.0 yaml_2.3.12
#> [16] fastmap_1.2.0 XVector_0.53.0 ggplot2_4.0.3
#> [19] R6_2.6.1 patchwork_1.3.2 labeling_0.4.3
#> [22] generics_0.1.4 knitr_1.52 BiocGenerics_0.59.12
#> [25] maketools_1.3.2 bslib_0.12.0 RColorBrewer_1.1-3
#> [28] rlang_1.3.0 cachem_1.1.0 xfun_0.61
#> [31] sass_0.4.10 sys_3.4.3 S7_0.2.2
#> [34] otel_0.2.0 cli_3.6.6 withr_3.0.3
#> [37] digest_0.6.39 grid_4.6.1 lifecycle_1.0.5
#> [40] S4Vectors_0.51.10 vctrs_0.7.3 evaluate_1.0.5
#> [43] glue_1.8.1 data.table_1.18.6.1 farver_2.1.2
#> [46] codetools_0.2-20 buildtools_1.0.0 stats4_4.6.1
#> [49] tools_4.6.1 htmltools_0.5.9- Introduction
- Installation
- Load the canonical demo inputs
- Object contracts used in the workflow
- Annotation and feature retrieval
- Variant retrieval
- GeneA haplotypes
- Haplotype-phenotype association
- Single-variant phenotype association
- Linkage disequilibrium
- Phenotype-guided haplotype refinement
- Variant-effect prioritization
- Session information