flanders

flanders is an R package designed to seamlessly convert finemapping output files (e.g., from the nf-flanders pipeline) into a unified AnnData object and facilitate colocalization analysis. The package provides functions to:

• Convert multiple *finemap.rds files into a single AnnData object with credible set metadata.
• Generate an input table (coloc_input) for colocalization testing.
• Run pairwise colocalization tests, with minimal runtime overhead (typically 5–10 tests per second on standard hardware).

When processing small to moderate datasets, you can run colocalization tests on your PC or laptop. For large-scale analyses, consider using the flanders_nf_coloc Nextflow pipeline.

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Table of Contents

1. Installation
   1. Simple Installation
   2. Installation via Conda Environment
2. Quick Start
   1. Scenario 1: Starting with an existing AnnData
   2. Scenario 2: Starting with nf-flanders Finemapping Output
3. Function Reference
4. AnnData Column Specifications
5. Additional Resources
6. Acknowledgments

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Installation

Simple Installation

To install the required R packages from CRAN, BioConductor and github, you can run the following commands in your R session:

install.packages("data.table")
install.packages("dplyr")
install.packages("Matrix")
install.packages("optparse")

# You need these Bioconductor packages:
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("SingleCellExperiment")
BiocManager::install("zellkonverter")
BiocManager::install("scRNAseq")

# Install the flanders package
# You need the devtools package if not installed.
install.packages("devtools")
install.packages("anndata")
devtools::install_github("Biostatistics-Unit-HT/flanders_r")

This guide installs all dependencies from CRAN and Bioconductor for a straightforward R-based setup.

Installation via Conda Environment

For a more reproducible environment or if you need to interface with Python’s AnnData via reticulate, you can set up a Conda environment as follows:

mamba create -p <your_folder_with_conda_envs>/flanders_r \
  -c conda-forge -c bioconda -c R \
  r-base=4.4 \
  bioconductor-singlecellexperiment=1.28.0 \
  bioconductor-zellkonverter=1.16.0 \
  bioconductor-scrnaseq=2.20.0 \
  r-susier=0.12.35 \
  r-coloc=5.2.3 \
  r-data.table=1.17.0 \
  r-dplyr=1.1.4 \
  r-anndata=0.7.5.6 \
  r-matrix=1.7_3 \
  r-optparse=1.7.5

conda activate <your_folder_with_conda_envs>/flanders_r
git clone git@github.com:Biostatistics-Unit-HT/flanders_r.git
install.packages("flanders_r", repos = NULL, type = "source")

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Quick Start

Scenario 1: Starting with an existing AnnData

If you already have an AnnData:

   library(zellkonverter)
   library(SingleCellExperiment)
   library(scRNAseq)

   sce <- readH5AD("/path/to/output/my_anndata.h5ad",reader="R")
   coloc_input <- anndata2coloc_input(sce)
   coloc_results <- anndata2coloc(sce, coloc_input)

   print(coloc_results)

If you already have multiple AnnDatas:

   library(zellkonverter)
   library(SingleCellExperiment)
   library(scRNAseq)
   library(anndata)

   list_of_ads <- list("/path/to/ad1.h5ad","/path/to/output/ad2.h5ad")
   list_of_ads <- lapply(list_of_ads,read_h5ad)

   merged_ad <- concat(
     list_of_ads,
     join="outer",
     merge="first"
   )
   merged_ad <- fix_ad_var(merged_ad)
   write_h5ad(merged_ad,"/path/to/merged_ad.h5ad")

   library(zellkonverter)
   library(SingleCellExperiment)
   library(scRNAseq)

   sce <- readH5AD("/path/to/merged_ad.h5ad",reader="R")
   coloc_input <- anndata2coloc_input(sce)
   coloc_results <- anndata2coloc(sce, coloc_input)
   print(coloc_results)

Scenario 2: Starting with nf-flanders Finemapping Output

If you do not have an AnnData object yet:

1. Convert Finemapping Files to AnnData

   
    library(flanders)
    library(data.table)
    library(dplyr)
    
    finemap_folder <- "/path/to/finemap/results/"
    finemap_files <- list.files(finemap_folder, pattern = "*\.rds", full.names = TRUE)
   
    # create a vector of phenotype ids. Each element corresponds to each file of finemap_files.
    # phenotype_id <-
   
    # create a vector of study ids. Each element corresponds to each file of finemap_files.
    # study_id <- 
    
    ad <- finemap2anndata(
      finemap_files = finemap_files,
      phenotype_id = phenotype_id,
      study_id = study_id
    )
    
    # Optionally write the AnnData object to disk
    ad$write_h5ad("/path/to/output/my_anndata.h5ad")

2. Generate Coloc Input Table. Write it if you want further run the nf-hcoloc pipeline

   library(zellkonverter)
   library(SingleCellExperiment)
   library(scRNAseq)
   sce <- readH5AD("/path/to/output/my_anndata.h5ad",reader="R")
   coloc_input <- anndata2coloc_input(sce)
   fwrite(coloc_input, file = "/path/to/coloc_guide_table.csv")

3. Perform Colocalization Analysis

    coloc_results <- anndata2coloc(sce, coloc_input)
    print(coloc_results)

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Function Reference

• finemap2anndata
  Converts a set of finemapping .rds files into a single AnnData object with credible set metadata.

• anndata2coloc_input
  Generates a data frame specifying pairs of credible sets for colocalization tests.

• anndata2coloc
  Performs colocalization tests using the provided AnnData object and trait-pair table.

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AnnData Column Specifications

var (Variables)

Column Name	Format / Content	Description
snp	chr{num}:{pos}:EA:RA	chr{num}: chromosome identifier (any string) pos: SNP position (in bp) EA: effective allele (linked to beta) RA: reference allele
chr	String (e.g. "chr{num}")	Chromosome where the SNP is located. Usually "chr{num}" format, but can be any string.
pos	Numeric (bp)	Position of the SNP in base pairs (bp) on the physical map of the genome.

Note: The row names of ad$var should be exactly equal to the values in ad$var$snp.

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obs (Observations)

Absolutely Necessary Columns

Column Name	Format / Content	Description
cs_name	chr{num}::{study_id}::{trait_id}::{snp}	chr{num}: chromosome (formatted as in var) study_id: identifier for the study trait_id: refers to the trait (equivalent to phenotype_id) snp: SNP with the highest logABF within the credible set (formatted as in ad$var$snp).
chr	String (e.g. "chr{num}")	Chromosome where the credible set is located (same format as the chr field in var).
start	Numeric (bp)	Start position (in bp) of the analyzed locus, representing the beginning of the locus used for fine mapping.
end	Numeric (bp)	End position (in bp) of the analyzed locus.
study_id	String	Identifier for the study.
phenotype_id	String	Identifier for the trait/phenotype analyzed within the corresponding study.
min_res_labf	Numeric (log-scale)	Minimal value of logABF in the locus. If logABF for all SNPs is not available, approximate using: [logsum(logABF)/coverage] - log(N_snps - N_CS_SNPs)
coverage	Numeric	requested coverage (usually 99% or 95%)
logsum(logABF)	Numeric	log of the sum of ABF for SNPs in the credible set (using a log-sum to avoid overflow from extremely large values)

Note: The row names of ad$obs should be exactly equal to the values in ad$obs$cs_name.

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Highly Advised to Have

Column Name	Format / Content	Description
min.abs.corr	Numeric	Purity metric for the credible set: minimal absolute correlation between SNPs within the credible set.
mean.abs.corr	Numeric	Mean absolute correlation among SNPs within the credible set.
median.abs.corr	Numeric	Median absolute correlation among SNPs within the credible set.

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Good to Have

Column Name	Format / Content	Description
top_pvalue	Numeric	Lowest (either nominal or conditional) P-value of genetic association in the locus (useful for filtering).
panel	String	Name of the imputation panel used.

Additional Resources

If the runtime for your colocalization tests becomes large, use the flanders_nf_coloc Nextflow pipeline for scalable colocalization analysis.

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Acknowledgments

Contributions, bug reports, and feature requests are welcome. Open an issue in case of issue, bug or feature request.