Introduction

nf-core/eqtl is a bioinformatics best-practice analysis pipeline for eqtl analysis.

This pipeline is running TensorQTL and/or LIMIX on bulk and single cell RNA seq datasets and assessed the overlap of the eGenes identified by both methodologies. While TensorQTL is very fast, this methodology uses linear regression which may not be capable in adequately represent the underlying population structure and other covariates, whereas Limix, while very computationally intensive is based on the linear mixed models (LMM) where the kinship matrices can be provided and hence accounting for random effects in a better manner.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Pipeline summary

1. Genotype preperation, filtering and subsetting (bcftools)
2. Genotype conversion to PLINK format and filtering (PLINK2)
3. Genotype kinship matrix calculation (PLINK2)
4. Genotype and Phenotype PC calculation and QTL mapping with various number of PCs (PLINK2)
5. LIMIX eqtl mapping (LIMIX)
6. TensorQTL eqtl mapping (TensorQTL)

Quick Start

1. Install Nextflow (>=21.04.0)

2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs)

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run nf-core/eqtl -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>

   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Prepeare the input.nf parameters file:

   params{
       method= 'single_cell' //or a [bulk | single_cell] (if single cell used the *phenotype_file* is a h5ad file)
       input_vcf ='/path/to/genotype/vcf/file.vcf'
       genotype_phenotype_mapping_file = '' //if bulk RNA seq data is fed in then need a tsv file with 3 columns - [Genotype	RNA	Sample_Category]
       annotation_file = './assets/annotation_file.txt'
       phenotype_file = 'path/to/adata.h5ad' //this should point to h5ad file in a single cell experiments or a star counts matrices for the bulk rna seq data
       aggregation_collumn='Azimuth:predicted.celltype.l2' // for scRNA seq data since we feed in the h5ad file we specify here which obs entry to account for for aggregating data.
   }

   example genotype_phenotype_mapping_file

   Genotype	RNA	Sample_Category
   HPSI0713i-aehn_22	MM_oxLDL7159503	M0_Ctrl
   HPSI0713i-aehn_22	MM_oxLDL7159504	M0_oxLDL
   HPSI0713i-aehn_22	MM_oxLDL7159505	M1_oxLDL

5. Start running your own analysis!

   nextflow run /path/to/cloned/eqtl -profile sanger -resume -c input.nf

Documentation

The nf-core/eqtl pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

nf-core/eqtl was originally written by Matiss Ozols with contributions from Anna Cuomo, Marc Jan Bonder, Hannes Ponstingl, Tobi Alegbe.

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don't hesitate to get in touch on the Slack #eqtl channel (you can join with this invite).

Citations

Currently pipeline has not been published but we would really appreciate if you could please acknowlage the use of this pipeline in your work:

Ozols, M. et al. 2023. eqtl (Quantitative Trait Loci mapping pipeline): GitHub. https://github.com/wtsi-hgi/eqtl.

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.