Package index • leo.gwas

Basic Utilities

Core helper functions from R/0.basic_tools.R.

across_df_na(): Across a df to count na
across_df_TF(): Across a df to count TRUE and FALSE
is_complementary() fetch_indel() fetch_non_indel() duplicated_SNP_lines() slice1_SNP_lines() fetch_same_direcrtion() any_na() get_biallelic_snp(): Helper functions for leo.gwas_qc
chisq_p_value(): Give precise p-value for chi-square test
count_duplicate_element(): Count or Identify Duplicates in a Vector
count_matching_elements(): Count or Identify Matches of a Pattern in a Vector
get_id(): Get Unique Identifier for Genetic Data

Annotation and mapping workflows from R/0.annotation.R.

add_rsid(): Convert CHR:BP to rsID (not recommended)
add_rsid_using_ref(): Add rsID based on local reference file (recommended)
add_chrpos(): Convert rsID to CHR & BP
annotate_cpg_sites(): Annotate CpG Sites with Gene Information
leo_map_GtoCP(): Map Gene Symbols to Genomic Positions
map_gene_to_chrbp_using_TxDb(): Map Gene Symbols Using Bioconductor Packages
map_gene_to_chrbp_using_gtf(): Map Gene Symbols Using GTF File
map_gene_to_chrbp_using_biomaRt(): Map Gene Symbols to Genomic Positions Using biomaRt
map_gene_to_tss_using_gtf(): Map Genes to Their TSS Positions
map_ensg_to_chrbp_using_biomaRt(): Map Ensembl Gene IDs to Genomic Positions using biomaRt
map_ensg_to_tss_using_biomaRt(): Map Ensembl Gene IDs to TSS Using biomaRt
map_ensg_to_gene_using_org.Hs.eg.db(): Map Ensembl IDs to Gene Symbols using org.Hs.eg.db
map_ensg_to_gene_using_biomaRt(): Map Ensembl Gene IDs to Gene Symbols using biomaRt
map_gene_to_ensembl(): Map Gene Symbols to Ensembl IDs
map_gene_class_using_annotables(): Map Gene Symbols to biotype & description via annotables
map_gene_class_using_biomarRt(): Map Gene Symbols to biotype & description via biomaRt (GRCh38 -> GRCh37 fallback)

Plotting and locus visualization from R/0.visual_tools.R.

leo_scale_color(): Apply Color Palette to ggplot
correlation_calculate(): Calculate Correlation between Two Vectors
correlation_draw(): Draw Correlation between Two Vectors
group_comparison_draw(): Draw Group Comparison for a Continuous Variable
ld_ps_index(): Loci_plot: Calculate the LD-matrix (LD r2) for the index SNP
locuszoomr_loc(): Loci_plot: prepare the locus data for locuszoomr
plot_gsMap(): Plot gsMap (full & highlight) with robust color mapping
plot_gsMap_color(): Build color maps for gsMap plots (helper function)
save_regional_plot(): Loci_plot: save_regional_plot

PRS modeling and ranking from R/prs_tools.R.

dr.prs() combine_rank(): DuoRank PRS (Dr.PRS)
catboost_prs() catboost_prs_target() catboost_prs_rank(): CatBoost PRS utilities
lasso_prs() lasso_prs_target() lasso_prs_rank(): Iterative Lasso PRS (iLasso) utilities
plink_clump_hla_aware(): HLA-aware PLINK clumping (Yet implemented)

MR data prep and analysis from R/tsmr_tools.R.

clump_data_local(): Perform LD Clumping Locally or via Reference Panel
extract_instruments_local(): Extract instruments locally for MR Analysis
find_proxy(): find_proxy
format_outcome(): format outcome data
is_complementary() fetch_indel() fetch_non_indel() duplicated_SNP_lines() slice1_SNP_lines() fetch_same_direcrtion() any_na() get_biallelic_snp(): Helper functions for leo.gwas_qc
filter_chr_basedonSNP_p(): Filter Chromosomes Based on SNP P-value Threshold
filter_chr_basedonSNP_p_qtltools(): Filter Chromosomes Based on SNP P-value Threshold for .qtltoolsnomi files
leo_iterator(): Leo batch iterator builder
mr_one_pair(): One-Click Perform 2SMR
mr_scatter_plot_modified(): Modified MR Scatter Plot
mrlap_one_pair(): mrlap_one_pair

SMR/BESD processing from R/smr_besd_tools.R.

combine_smr_res_chr(): Combine SMR Results for All Chromosomes
combine_smr_res_1outcome(): Combine .fdr files for one or multiple outcomes (seperately)
leo_smr_adjust(): Adjust SMR Results with FDR and Bonferroni Corrections
leo_smr_adjust_loop(): Batch Adjust SMR Results with FDR and Bonferroni Corrections
leo_smr_extract_sig_res(): Extract significant results from .all files
leo_smr_merge_shared_probes(): Merge multiple SMR files and keep only shared probes for 2 outcomes

HLA region helpers from R/hla_tools.R.

Summary statistics QC from R/summary_qc.R.

leo.gwas_qc(): GWAS summary QC pipeline (chip + imputed)
check_significant_SNP(): locate the significant SNP for conditional analysis

Cell-stratified MR (csMR) pipeline.