Package index • pecotmr

All functions

AnnotationMatrix-class: Genomic Annotation Matrix
AnnotationMatrix(): Create an AnnotationMatrix Object
FineMappingResult-class: Fine-Mapping Result
FineMappingResult(): Create a FineMappingResult Object
GWASSumStats-class: GWAS Summary Statistics
GWASSumStats(): Create a GWASSumStats Object
GenotypeHandle-class: Genotype Handle
H2Estimate-class: Heritability Estimate
LDBlocks-class: LD Block Definitions
LDData-class: LD Data Container
LDData(): Create an LDData Object
LDEigen-class: Eigendecomposition-Based LD Statistic
LDScore-class: LD Score-Based LD Statistic
LDStatistic-class: LD Statistic (Virtual Base Class)
RegionalData-class: Regional Association Data
RegionalData(): Create a RegionalData Object
TWASWeights-class: TWAS Weights
TWASWeights(): Create a TWASWeights Object
adjust_susie_weights(): Adjust SuSiE Weights
align_variant_names(): Align Variant Names
as.data.frame(<GWASSumStats>): Convert GWASSumStats to data.frame
auto_decision(): Process Credible Set Information and Determine Updating Strategy
b_lasso_weights(): Compute Weights Using the Bayesian LASSO (BGLR)
batch_load_twas_weights(): Split loaded twas_weights_results into batches based on maximum memory usage
bayes_a_weights(): Use t-distribution as prior.
bayes_alphabet_weights(): Extract Coefficients From Bayesian Linear Regression
bayes_b_weights(): Compute Weights Using BayesB
bayes_c_weights(): Use a rounded spike prior (low-variance Gaussian).
bayes_l_weights(): Use laplace/double exponential distribution as prior. This is equivalent to Bayesian LASSO.
bayes_n_weights(): Use Gaussian distribution as prior. Posterior means will be BLUP, equivalent to Ridge Regression.
bayes_r_weights(): Use a hierarchical Bayesian mixture model with four Gaussian components. Variances are scaled by 0, 0.0001 , 0.001 , and 0.01 .
build_twas_score_row(): Function to perform TWAS analysis for across multiple contexts. This function peforms TWAS analysis for multiple contexts for imputable genes within an LD region and summarize the twas results.
check_ld(): Check and optionally repair LD matrix quality
coloc_post_processor(): coloc_post_processor function
coloc_wrapper(): Colocalization Analysis Wrapper
colocboost_analysis(): ColocBoost analysis with optional pipeline QC
colocboost_pipeline(): Multi-trait colocalization analysis protocol pipeline
computeBlockLdCor(): Compute Block LD Correlation
computeLdScores(): Compute LD Scores
compute_LD(): Compute LD (Linkage Disequilibrium) Correlation Matrix from Genotypes
compute_cov_diag(): Compute diagonal covariance matrix
compute_cov_flash(): Compute covariance matrix using FLASH
compute_qtl_enrichment(): Implementation of enrichment analysis described in https://doi.org/10.1371/journal.pgen.1006646
compute_sldsc_M_ref(): Reference-panel SNP count (the M_ref used to standardise tau*)
compute_sldsc_annot_sd(): Compute per-annotation standard deviation, MAF-restricted
ctwas_bimfile_loader(): Load a PLINK .bim file for cTWAS
dentist(): Detect Outliers Using Dentist Algorithm
dentist_single_window(): Perform DENTIST on a single window
detect_outliers_mahalanobis(): Detect Outliers via Mahalanobis Distance
dpr_weights() dpr_vb_weights() dpr_gibbs_weights() dpr_adaptive_gibbs_weights(): Compute Weights Using Dirichlet Process Regression (RcppDPR)
enforce_design_full_rank(): Iteratively enforce full column rank on a design matrix
ensemble_weights(): Ensemble TWAS Weights via Stacked Regression
eqtl_region_example: Example eQTL Region Data (Individual-Level)
estimateH2(): Estimate SNP Heritability
estimate_sparsity(): Estimate Sparsity from mr.ash Mixture Proportions
extractBlockGenotypes(): Extract Block Genotypes
extract_cs_info(): Process Credible Sets (CS) from Finemapping Results
extract_flatten_sumstats_from_nested(): Extract Summary Statistics from Nested Data Structure
extract_top_pip_info(): Extract Information for Top Variant from Finemapping Results
filter_X_with_Y(): This function performing filters on X variants based on Y subjects for TWAS analysis. This function checks whether the absence (NA) of certain subjects would lead to monomorphic in some variants in X after removing of these subjects data from X.
filter_molecular_events(): Filter events based on provided context name pattern
filter_relatedness(): Filter related individuals from a study
filter_variants_by_ld_reference(): Filter variants by LD Reference
find_data(): Utility function to specify the path to access the target list item in a nested list, especially when some list layers in between are dynamic or uncertain.
find_duplicate_variants(): Filter a vector based on a correlation matrix
find_valid_file_path(): Find Valid File Path
fine_mr(): Fine-mapping-based Mendelian Randomization
fit_mash_contrast(): Compute pairwise contrasts from mash posterior
format_finemapping_output(): Format Fine-mapping Post-processing for Protocol Output
fsusie_get_cs(): @title Create Sets Similar to SuSiE Output from fSuSiE Object
fsusie_wrapper(): Wrapper for fsusie Function with Automatic Post-Processing
getBaseline(): Get Baseline Annotations
getBlockMetadata(): Get Block Metadata
getCS(): Get Credible Sets
getCandidates(): Get Candidate Annotations
getCorrelation(): Get LD Correlation Matrix
getEffects(): Get Per-Effect Fine-Mapping Summary
getEnrichment(): Get Enrichment Estimates
getGenotypes(): Get Genotype Matrix
getLBF(): Get Log Bayes Factors
getLocal(): Get Local Estimates
getMaf(): Get Minor Allele Frequencies
getN(): Get Sample Sizes
getPIP(): Get PIP Values
getResidualX(): Get Residualized Genotypes
getResidualXScalar(): Get Residual X Scalar
getResidualY(): Get Residualized Phenotypes
getResidualYScalar(): Get Residual Y Scalar
getScoreStats(): Get Score Statistics
getVarY(): Get Phenotype Variance
getVariantIds(): Get Variant IDs
getVariantInfo(): Get Variant GRanges
getWeights(): Get TWAS Weight Matrices
getZ(): Get Z-scores
get_ctwas_meta_data(): Load cTWAS LD meta-data
get_ref_variant_info(): Get variant information from any LD reference source.
get_susie_result(): Extract SuSiE Results from Finemapping Data
gwas_finemapping_example: Example GWAS Fine-Mapping Results (SuSiE)
gwas_sumstats_example: Example GWAS Summary Statistics
h2estimate_to_sldsc_trait(): Convert H2Estimate to S-LDSC Trait Format
harmonize_twas(): Function to perform allele flip QC and harmonization on the weights and GWAS against LD for a region. FIXME: GWAS loading function from Haochen for both tabix & column-mapping yml application
hasGenotypes(): Check Genotype Availability
invert_minmax_scaling(): Invert min-max [0,2] scaling to recover the original U matrix.
is_binary_sldsc_annot(): Detect whether each annotation is binary or continuous
l0learn_rss_weights(): Compute L0-Penalized Weights from Summary Statistics
l0learn_weights(): Compute Weights Using L0Learn
lassosum_rss(): Lassosum RSS: LASSO on summary statistics with LD reference
lassosum_rss_weights(): Extract weights from lassosum_rss with shrinkage grid search
lbf_to_alpha(): Applies the 'lbf_to_alpha_vector' function row-wise to a matrix of log Bayes factors to convert them to Single Effect PIP values.
ld_clump_by_score(): LD clumping by a per-variant score using bigsnpr
ld_loader(): Create an LD loader for on-demand block-wise LD retrieval
ld_mismatch_qc(): Detect LD-Summary Statistic Mismatches
ld_prune_by_correlation(): Prune columns by pairwise correlation (LD-style prune)
load_LD_matrix(): Load and Process Linkage Disequilibrium (LD) Matrix
load_genotype_region(): Load genotype data for a specific region
load_multicontext_sumstats(): Load and Align Summary Statistics for a Given Gene and Condition
load_multitask_regional_data(): This function loads a mixture data sets for a specific region, including individual-level data (genotype, phenotype, covariate data) or summary statistics (sumstats, LD). Run load_regional_univariate_data and load_rss_data multiple times for different datasets
load_regional_association_data(): Load regional association data
load_regional_functional_data(): Load Regional Functional Association Data
load_regional_multivariate_data(): Load and Preprocess Regional Multivariate Data
load_regional_regression_data(): Load Regional Data for Regression Modeling
load_regional_univariate_data(): Load Regional Univariate Association Data
load_rss_data(): Load summary statistic data
load_study_LD(): Load LD for a study, supporting single or mixture panels.
load_tsv_region(): Load and filter tabular data with optional region subsetting
load_twas_weights(): Load, Validate, and Consolidate TWAS Weights from Multiple RDS Files
make_pairwise_contrast_col(): Create a pairwise contrast column
match_ref_panel() allele_qc(): Match target data alleles against a reference panel
mcp_rss_weights(): Compute MCP-Penalized Weights from Summary Statistics
mcp_weights(): Compute Weights Using MCP-Penalized Regression
merge_sumstats_matrices(): Merge a List of Matrices or Data Frames with Optional Allele Flipping
merge_variant_info(): Merge variant info from two sources with allele-flip-aware matching
meta_analysis_per_cell(): Random-Effects Meta-Analysis of Mash Pairwise Contrasts
meta_sldsc_random(): Random-effects meta-analysis of S-LDSC quantities across traits
mr_analysis(): Mendelian Randomization (MR)
mr_ash_rss_weights(): Extract weights from mr.ash.rss (susieR)
mr_format(): MR Format Function
mrash_weights(): Compute Weights Using mr.ash Shrinkage
mrmash_weights(): Compute mr.mash TWAS weights
mrmash_wrapper(): Mr.Mash Wrapper
multivariate_analysis_pipeline(): Multivariate Analysis Pipeline
mvsusie_weights(): Compute mvSuSiE TWAS weights
nSnps(): Get Number of SNPs
normalize_variant_id(): Normalize variant IDs to canonical format
otters_association(): TWAS association testing with omnibus combination (OTTERS Stage II)
otters_weights(): Train eQTL weights using multiple RSS methods (OTTERS Stage I)
parse_cs_corr(): Parse Credible Set Correlations from extract_cs_info() Output
parse_variant_id(): Parse variant IDs into a data frame
penalized_rss(): Penalized Regression on RSS (Summary Statistics) Objective
postprocess_finemapping_fits(): Post-process Fine-mapping Fits
prs_cs(): PRS-CS: a polygenic prediction method that infers posterior SNP effect sizes under continuous shrinkage (CS) priors
prs_cs_weights(): Extract weights from prs_cs function
qc_individual_data(): Run reusable individual-level QC
qtl_finemapping_example: Example QTL Fine-Mapping Results (SuSiE)
raiss(): Impute Summary Statistics Using LD (RAISS)
readAnnotations(): Read Annotations
readGenotypes(): Read Genotype Data
readSumstats(): Read GWAS Summary Statistics
read_afreq(): Read a PLINK2 allele frequency file (.afreq or .afreq.zst)
read_sldsc_trait(): Read S-LDSC outputs from polyfun for one trait/run
region_data_to_colocboost_input(): Convert loaded regional data to ColocBoost inputs
region_data_to_ind_input(): Convert loaded regional data to individual-level inputs
region_data_to_rss_input(): Convert loaded regional data to RSS inputs
region_to_df(): Utility function to convert LD region_ids to `region of interest` dataframe
rescale_cov_w0(): Re-normalize mrmash weight w0 to have total weight sum to 1
robust_mahalanobis(): Robust Mahalanobis Distance
rss_analysis_pipeline(): RSS Analysis Pipeline
rss_basic_qc(): Preprocess input data for RSS analysis
rss_to_gwas_sumstats(): Convert load_rss_data Output to GWASSumStats
sanitize_mash_data(): Sanitize NaN/Inf values in mash data
scad_rss_weights(): Compute SCAD-Penalized Weights from Summary Statistics
scad_weights(): Compute Weights Using SCAD-Penalized Regression
sdpr(): SDPR (Summary-Statistics-Based Dirichelt Process Regression for Polygenic Risk Prediction)
sdpr_weights(): Extract weights from sdpr function
slalom(): Slalom Function for Summary Statistics QC for Fine-Mapping Analysis
sldsc_postprocessing_pipeline(): End-to-end S-LDSC post-processing across traits, single + joint in one pass
slice_mash_data(): Subset mash data matrices to specific SNPs and conditions
standardise_sumstats_columns(): Standardize GWAS summary statistics column names
standardize_sldsc_trait(): Standardize tau and compute EnrichStat for one polyfun run
subsetChr(): Subset by Chromosome
summary_stats_qc(): Perform Quality Control on Summary Statistics
susie_ash_weights(): Compute SuSiE-ASH TWAS weights
susie_inf_weights(): Compute SuSiE-inf TWAS weights
susie_rss_pipeline(): Run the SuSiE RSS pipeline
susie_weights(): Compute SuSiE TWAS weights
top_loci_to_granges(): Convert top_loci to GRanges
trim_ctwas_variants(): Function to select variants for ctwas weights input
twas_analysis(): TWAS Analysis
twas_joint_z(): Multi-condition TWAS joint test
twas_multivariate_weights_pipeline(): TWAS Multivariate Weights Pipeline
twas_predict(): Predict outcomes using TWAS weights
twas_weights(): Run multiple TWAS weight methods
twas_weights_cv(): Cross-Validation for weights selection in Transcriptome-Wide Association Studies (TWAS)
twas_weights_pipeline(): TWAS Weights Pipeline
twas_z(): Calculate TWAS z-score and p-value
univariate_analysis_pipeline(): Univariate Analysis Pipeline
update_mash_model_cov(): Subset a fitted mash model to a subset of conditions
writeSumstatsVcf(): Write summary statistics or fine-mapping results to VCF/BCF
xgboost_imputation(): XGBoost-based iterative imputation of missing values
xqtl_enrichment_wrapper(): xQTL GWAS Enrichment Analysis
z_to_pvalue(): Convert Z-scores to P-values