susieR not installing automatically

¶

¶ Authors: Brian Schilder, Jack Humphrey, Towfique Raj ¶

¶ README updated: Jan-20-2023 ¶

`echolocatoR`: Automated statistical and functional fine-mapping

with extensive access to genome-wide datasets.

The echoverse

echolocatoR is part of the echoverse, a suite of R packages designed to facilitate different steps in genetic fine-mapping.

echolocatoR calls each of these other packages (i.e. “modules”) internally to create a unified pipeline. However, you can also use each module independently to create your own custom workflows.

echoverse dependency graph

Made with echodeps, yet another echoverse module. See here for the interactive version with package descriptions and links to each GitHub repo.

Citation

If you use echolocatoR, or any of the echoverse modules, please cite:

Brian M Schilder, Jack Humphrey, Towfique Raj (2021) echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline, Bioinformatics; btab658, https://doi.org/10.1093/bioinformatics/btab658

Installation

if(!require("remotes")) install.packages("remotes")

remotes::install_github("RajLabMSSM/echolocatoR")
library(echolocatoR)

Installation troubleshooting

Because echolocatoR now relies on many subpackages that rely on one another, sometimes errors can occur when R tries to update one R package before updating its echoverse dependencies (and thus is unable to find new functions). As echoverse stabilizes over time, this should happen less frequently. However, in the meantime the solution is to simply rerun remotes::install_github("RajLabMSSM/echolocatoR") until all subpackages are fully updates.
susieR: Sometimes an older version of susieR is installed from CRAN (e.g. 0.11.92), but echofinemap requires version >= 0.12.0. To get around this, you can install susieR directly from GitHub: devtools::install_github("stephenslab/susieR")
System dependencies can sometimes cause issues when using different packages. I’ve tried to account for as many of these as possible automatically within the code, but using the Docker/Singularity provided below can further mitigate these issues.
The R package XML (which some echoverse subpackages depend on) has some additional system dependencies that must be installed beforehand. If XML does not install automatically, try installing lbxml on your system using brew install libxml2 (MacOS), sudo apt-get install libxml2 (Linux) or conda install r-xml if you are running echolocatoR from within a conda environment.

[Optional] Docker/Singularity

echolocatoR now has its own dedicated Docker/Singularity container! This greatly reduces issues related to system dependency conflicts and provides a containerized interface for Rstudio through your web browser. See here for installation instructions.

Documentation

Website

Get started

Bugs/requests

Please report any bugs/requests on GitHub Issues.

Contributions are welcome!

All echoverse vignettes

echoverse <- c('echolocatoR','echodata','echotabix',
               'echoannot','echoconda','echoLD',
               'echoplot','catalogueR','downloadR',
               'echofinemap','echodeps', # under construction
               'echogithub')
toc <- echogithub::github_pages_vignettes(owner = "RajLabMSSM",
                                          repo = echoverse,
                                          as_toc = TRUE,
                                          verbose = FALSE)

🦇 echolocatoR
🦇 echodata
- echodata
- echolocatoR Finemapping Portal
🦇 echotabix
- echotabix
🦇 echoannot
- cell type specific epigenomics
- echoannot
🦇 echoconda
- echoconda
🦇 echoLD
- echoLD
🦇 echoplot
- echoplot
🦇 echofinemap
- echoverseTemplate
🦇 echodeps
- echoverseTemplate

Introduction

Fine-mapping methods are a powerful means of identifying causal variants underlying a given phenotype, but are underutilized due to the technical challenges of implementation. echolocatoR is an R package that automates end-to-end genomics fine-mapping, annotation, and plotting in order to identify the most probable causal variants associated with a given phenotype.

It requires minimal input from users (a GWAS or QTL summary statistics file), and includes a suite of statistical and functional fine-mapping tools. It also includes extensive access to datasets (linkage disequilibrium panels, epigenomic and genome-wide annotations, QTL).

The elimination of data gathering and preprocessing steps enables rapid fine-mapping of many loci in any phenotype, complete with locus-specific publication-ready figure generation. All results are merged into a single per-SNP summary file for additional downstream analysis and results sharing. Therefore echolocatoR drastically reduces the barriers to identifying causal variants by making the entire fine-mapping pipeline rapid, robust and scalable.

Literature

For applications of `echolocatoR` in the literature, please see:

E Navarro, E Udine, K de Paiva Lopes, M Parks, G Riboldi, BM Schilder…T Raj (2020) Dysregulation of mitochondrial and proteo-lysosomal genes in Parkinson’s disease myeloid cells. Nature Genetics. https://doi.org/10.1101/2020.07.20.212407

BM Schilder, T Raj (2021) Fine-Mapping of Parkinson’s Disease Susceptibility Loci Identifies Putative Causal Variants. Human Molecular Genetics, ddab294, https://doi.org/10.1093/hmg/ddab294

K de Paiva Lopes, G JL Snijders, J Humphrey, A Allan, M Sneeboer, E Navarro, BM Schilder…T Raj (2022) Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nature Genetics, https://doi.org/10.1038/s41588-021-00976-y

`echolocatoR` v1.0 vs. v2.0

There have been a series of major updates between echolocatoR v1.0 and v2.0. Here are some of the most notable ones (see Details):

echoverse subpackages: echolocatoR has been broken into separate subpackages, making it much easier to edit/debug each step of the full finemap_loci pipeline, and improving robustness throughout. It also provides greater flexibility for users to construct their own custom pipelines from these modules.
GITHUB_TOKEN: GitHub now requires users to create Personal Authentication Tokens (PAT) to avoid download limits. This is essential for installing echolocatoR as many resources from GitHub need to be downloaded. See here for further instructions. = echodata::construct_colmap(): Previously, users were required to input key column name mappings as separate arguments to echolocatoR::finemap_loci. This functionality has been deprecated and replaced with a single argument, colmap=. This allows users to save the construct_colmap() output as a single variable and reuse it later without having to write out each mapping argument again (and helps reduce an already crowded list of arguments).
MungeSumstats: finemap_loci now accepts the output of MungeSumstats::format_sumstats/import_sumstats as-is (without requiring colmap=, so long as munged=TRUE). Standardizing your GWAS/QTL summary stats this way greatly reduces (or eliminates) the time taken to do manual formatting.
echolocatoR::finemap_loci arguments: Several arguments have been deprecated or had their names changed to be harmonized across all the subpackages and use a unified naming convention. See ?echolocatoR::finemap_loci for details.
echoconda: The echoverse subpackage echoconda now handles all conda environment creation/use internally and automatically, without the need for users to create the conda environment themselves as a separate step. Also, the default conda env echoR has been replaced by echoR_mini, which reduces the number of dependencies to just the bare minimum (thus greatly speeding up build time and reducing potential version conflicts).
FINEMAP: More outputs from the tool FINEMAP are now recorded in the echolocatoR results (see ?echofinemap::FINEMAP or this Issue for details). Also, a common dependency conflict between FINEMAP>=1.4 and MacOS has been resolved (see this Issue for details.
echodata: All example data and data transformation functions have been moved to the echoverse subpackage echodata.
LD_reference=: In addition to the UKB, 1KGphase1/3 LD reference panels, finemap_loci() can now take custom LD panels by supplying finemap_loci(LD_reference=) with a list of paths to VCF files (.vcf / vcf.gz / vcf.bgz) or pre-computed LD matrices with RSIDs as the row/col names (.rda / .rds / .csv / .tsv. / .txt / .csv.gz / tsv.gz / txt.gz).
Expanded fine-mapping methods: “ABF”, “COJO_conditional”, “COJO_joint” “COJO_stepwise”,“FINEMAP”,“PAINTOR” (including multi-GWAS and multi-ancestry fine-mapping),“POLYFUN_FINEMAP” ,“POLYFUN_SUSIE”,“SUSIE”
FINEMAP fixed: There were a number of issues with FINEMAP due to differing output formats across different versions, system dependency conflicts, and the fact that it can produce multiple Credible Sets. All of these have been fixed and the latest version of FINEMAP can be run on all OS platforms.
Debug mode: Within finemap_loci() I use a tryCatch() when iterating across loci so that if one locus fails, the rest can continue. However this prevents using traceback feature in R, making debugging hard. Thus I now enabled debugging mode via a new argument: use_tryCatch=FALSE.

Output descriptions

By default, echolocatoR::finemap_loci() returns a nested list containing grouped by locus names (e.g. $BST1, $MEX3C). The results of each locus contain the following elements:

finemap_dat: Fine-mapping results from all selected methods merged with the original summary statistics (i.e. Multi-finemap results).
locus_plot: A nested list containing one or more zoomed views of locus plots.
LD_matrix: The post-processed LD matrix used for fine-mapping.
LD_plot: An LD plot (if used).
locus_dir: Locus directory results are saved in.
arguments: A record of the arguments supplied to finemap_loci.

In addition, the following object summarizes the results from the locus-specific elements:

merged_dat: A merged data.table with all fine-mapping results from all loci.

Multi-finemap results files

The main output of echolocatoR are the multi-finemap files (for example, echodata::BST1). They are stored in the locus-specific Multi-finemap subfolders.

Column descriptions

Standardized GWAS/QTL summary statistics: e.g. SNP,CHR,POS,Effect,StdErr. See ?finemap_loci() for descriptions of each.
leadSNP: The designated proxy SNP per locus, which is the SNP with the smallest p-value by default.
<tool>.CS: The 95% probability Credible Set (CS) to which a SNP belongs within a given fine-mapping tool’s results. If a SNP is not in any of the tool’s CS, it is assigned NA (or 0 for the purposes of plotting).
<tool>.PP: The posterior probability that a SNP is causal for a given GWAS/QTL trait.
Support: The total number of fine-mapping tools that include the SNP in its CS.
Consensus_SNP: By default, defined as a SNP that is included in the CS of more than N fine-mapping tool(s), i.e. Support>1 (default: N=1).
mean.PP: The mean SNP-wise PP across all fine-mapping tools used.
mean.CS: If mean PP is greater than the 95% probability threshold (mean.PP>0.95) then mean.CS is 1, else 0. This tends to be a very stringent threshold as it requires a high degree of agreement between fine-mapping tools.

Notes

Separate multi-finemap files are generated for each LD reference panel used, which is included in the file name (e.g. UKB_LD.Multi-finemap.tsv.gz).
Each fine-mapping tool defines its CS and PP slightly differently, so please refer to the associated original publications for the exact details of how these are calculated (links provided above).

Fine-mapping tools

Fine-mapping functions are now implemented via echofinemap:

echolocatoR will automatically check whether you have the necessary columns to run each tool you selected in echolocatoR::finemap_loci(finemap_methods=...). It will remove any tools that for which there are missing necessary columns, and produces a message letting you know which columns are missing.
Note that some columns (e.g. MAF,N,t-stat) will be automatically inferred if missing.
For easy reference, we list the necessary columns here as well.
See ?echodata::construct_colmap() for descriptions of these columns.
All methods require the columns: SNP,CHR,POS,Effect,StdErr

fm_methods <- echofinemap::required_cols(add_versions = FALSE, 
                                         embed_links = TRUE,
                                         verbose = FALSE)

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

knitr::kable(x = fm_methods)

method	required	suggested	source	citation
ABF	SNP, CHR….		source	cite
COJO_conditional	SNP, CHR….	Freq, P, N	source	cite
COJO_joint	SNP, CHR….	Freq, P, N	source	cite
COJO_stepwise	SNP, CHR….	Freq, P, N	source	cite
FINEMAP	SNP, CHR….	A1, A2, ….	source	cite
PAINTOR	SNP, CHR….	MAF	source	cite
POLYFUN_FINEMAP	SNP, CHR….	MAF, N	source	cite
POLYFUN_SUSIE	SNP, CHR….	MAF, N	source	cite
SUSIE	SNP, CHR….	N	source	cite

Datasets

Datasets are now stored/retrieved via the following echoverse subpackages:

echodata: Pre-computed fine-mapping results. Also handles the semi-automated standardization of summary statistics.
echoannot: Annotates GWAS/QTL summary statistics using epigenomics, pre-compiled annotation matrices, and machine learning model predictions of variant-specific functional impacts.
catalogueR: Large compendium of fully standardized e/s/t-QTL summary statistics.

For more detailed information about each dataset, use ?:

### Examples ###

library(echoannot)   
?NOTT_2019.interactome # epigenomic annotations
library(echodata) 
?BST1 # fine-mapping results

`MungeSumstats`:

You can search, import, and standardize any GWAS in the Open GWAS database via MungeSumstats, specifically the functions find_sumstats and import_sumstats.

`catalogueR`: QTLs

eQTL Catalogue: `catalogueR::eQTL_Catalogue.query()`

API access to full summary statistics from many standardized e/s/t-QTL datasets.
Data access and colocalization tests facilitated through the catalogueR R package.

`echodata`: fine-mapping results

echolocatoR Fine-mapping Portal: pre-computed fine-mapping results

You can visit the echolocatoR Fine-mapping Portal to interactively visualize and download pre-computed fine-mapping results across a variety of phenotypes.
This data can be searched and imported programmatically using echodata::portal_query().

`echoannot`: Epigenomic & genome-wide annotations

Nott et al. (2019): `echoannot::NOTT2019_*()`

Data from this publication contains results from cell type-specific (neurons, oligodendrocytes, astrocytes, microglia, & peripheral myeloid cells) epigenomic assays (H3K27ac, ATAC, H3K4me3) from ex vivo pediatric human brain tissue.

Corces et al.2020: `echoannot::CORCES2020_*()`

Data from this publication contains results from single-cell and bulk chromatin accessibility assays ([sc]ATAC-seq) and chromatin interactions ( FitHiChIP) from postmortem adult human brain tissue.

XGR: `echoannot::XGR_download_and_standardize()`

API access to a diverse library of cell type/line-specific epigenomic (e.g. ENCODE) and other genome-wide annotations.

Roadmap: `echoannot::ROADMAP_query()`

API access to cell type-specific epigenomic data.

biomaRt: `echoannot::annotate_snps()`

API access to various genome-wide SNP annotations (e.g. missense, nonsynonmous, intronic, enhancer).

HaploR: `echoannot::annotate_snps()`

API access to known per-SNP QTL and epigenomic data hits.

Enrichment tools

Annotation enrichment functions are now implemented via echoannot:

Implemented

XGR: `echoannot::XGR_enrichment()`

Binomial enrichment tests between customisable foreground and background SNPs.

motifbreakR: `echoannot::MOTIFBREAKR()`

Identification of transcript factor binding motifs (TFBM) and prediction of SNP disruption to said motifs.
Includes a comprehensive list of TFBM databases via MotifDB (9,900+ annotated position frequency matrices from 14 public sources, for multiple organisms).

regioneR: `echoannot::test_enrichment()`

Iterative pairwise permutation testing of overlap between all combinations of two GRangesList objects.

Under construction

GARFIELD

Genomic enrichment with LD-informed heuristics.

GoShifter

LD-informed iterative enrichment analysis.

S-LDSC

Genome-wide stratified LD score regression.
Inlccles 187-annotation baseline model from Gazal et al. 2018.
You can alternatively supply a custom annotations matrix.

LD reference panels

LD reference panels are now queried/processed by echoLD, specifically the function get_LD():

Custom LD panel:

From user-supplied VCFs

Custom LD panel

From user-supplied precomputed LD matrices

Plotting

Plotting functions are now implemented via:

echoplot: Multi-track locus plots with GWAS, fine-mapping results, and functional annotations (plot_locus()). Can also plot multi-GWAS/QTL and multi-ancestry results (plot_locus_multi()).
echoannot: Study-level summary plots showing aggregted info across many loci at once (super_summary_plot()).
echoLD: Plot an LD matrix using one of several differnt plotting methods (plot_LD()).

Tabix queries

All queries of tabix-indexed files (for rapid data subset extraction) are implemented via echotabix.

echotabix::convert_and_query() detects whether the GWAS summary statistics file you provided is already tabix-indexed, and it not, automatically performs all steps necessary to convert it (sorting, bgzip-compression, indexing) across a wide variety of scenarios.
echotabix::query() contains many different methods for making tabix queries (e.g. Rtracklayer,echoconda,VariantAnnotation,seqminer), each of which fail in certain circumstances. To avoid this, query() automatically selects the method that will work for the particular file being queried and your machine’s particular versions of R/Bioconductor/OS, taking the guesswork and troubleshooting out of tabix queries.

Downloads

Single- and multi-threaded downloads are now implemented via downloadR.

Multi-threaded downloading is performed using axel, and is particularly useful for speeding up downloads of large files.
axel is installed via the official echoverse conda environment: “echoR_mini”. This environment is automatically created by the function echoconda::yaml_to_env() when needed.

Developer

Brian M. Schilder, Bioinformatician II
Raj Lab
Department of Neuroscience, Icahn School of Medicine at Mount Sinai

Session info

utils::sessionInfo()

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2                  reticulate_1.27            
##   [3] R.utils_2.12.2              tidyselect_1.2.0           
##   [5] RSQLite_2.2.20              AnnotationDbi_1.60.0       
##   [7] htmlwidgets_1.6.1           grid_4.2.1                 
##   [9] BiocParallel_1.32.5         echogithub_0.99.1          
##  [11] XGR_1.1.8                   munsell_0.5.0              
##  [13] codetools_0.2-18            interp_1.1-3               
##  [15] DT_0.27                     colorspace_2.0-3           
##  [17] OrganismDbi_1.40.0          Biobase_2.58.0             
##  [19] filelock_1.0.2              highr_0.10                 
##  [21] knitr_1.41                  supraHex_1.36.0            
##  [23] rstudioapi_0.14             stats4_4.2.1               
##  [25] DescTools_0.99.47           gitcreds_0.1.2             
##  [27] MatrixGenerics_1.10.0       GenomeInfoDbData_1.2.9     
##  [29] mixsqp_0.3-48               bit64_4.0.5                
##  [31] echoconda_0.99.9            rprojroot_2.0.3            
##  [33] basilisk_1.10.2             vctrs_0.5.1                
##  [35] generics_0.1.3              xfun_0.36                  
##  [37] biovizBase_1.46.0           BiocFileCache_2.6.0        
##  [39] R6_2.5.1                    GenomeInfoDb_1.34.6        
##  [41] AnnotationFilter_1.22.0     bitops_1.0-7               
##  [43] cachem_1.0.6                reshape_0.8.9              
##  [45] DelayedArray_0.24.0         assertthat_0.2.1           
##  [47] BiocIO_1.8.0                scales_1.2.1               
##  [49] nnet_7.3-18                 rootSolve_1.8.2.3          
##  [51] gtable_0.3.1                ggbio_1.46.0               
##  [53] lmom_2.9                    ensembldb_2.22.0           
##  [55] rlang_1.0.6                 echodata_0.99.16           
##  [57] splines_4.2.1               lazyeval_0.2.2             
##  [59] rtracklayer_1.58.0          dichromat_2.0-0.1          
##  [61] hexbin_1.28.2               checkmate_2.1.0            
##  [63] reshape2_1.4.4              BiocManager_1.30.19        
##  [65] yaml_2.3.6                  backports_1.4.1            
##  [67] snpStats_1.48.0             GenomicFeatures_1.50.3     
##  [69] ggnetwork_0.5.10            Hmisc_4.7-2                
##  [71] RBGL_1.74.0                 tools_4.2.1                
##  [73] ggplot2_3.4.0               ellipsis_0.3.2             
##  [75] RColorBrewer_1.1-3          proxy_0.4-27               
##  [77] BiocGenerics_0.44.0         coloc_5.1.0.1              
##  [79] Rcpp_1.0.9                  plyr_1.8.8                 
##  [81] base64enc_0.1-3             progress_1.2.2             
##  [83] zlibbioc_1.44.0             purrr_1.0.1                
##  [85] RCurl_1.98-1.9              basilisk.utils_1.10.0      
##  [87] prettyunits_1.1.1           rpart_4.1.19               
##  [89] deldir_1.0-6                viridis_0.6.2              
##  [91] S4Vectors_0.36.1            cluster_2.1.4              
##  [93] SummarizedExperiment_1.28.0 ggrepel_0.9.2              
##  [95] fs_1.5.2                    here_1.0.1                 
##  [97] crul_1.3                    magrittr_2.0.3             
##  [99] data.table_1.14.6           echotabix_0.99.8           
## [101] dnet_1.1.7                  openxlsx_4.2.5.1           
## [103] gh_1.3.1                    mvtnorm_1.1-3              
## [105] ProtGenerics_1.30.0         matrixStats_0.63.0         
## [107] patchwork_1.1.2             hms_1.1.2                  
## [109] evaluate_0.20               rworkflows_0.99.5          
## [111] XML_3.99-0.13               jpeg_0.1-10                
## [113] readxl_1.4.1                IRanges_2.32.0             
## [115] gridExtra_2.3               testthat_3.1.6             
## [117] compiler_4.2.1              biomaRt_2.54.0             
## [119] tibble_3.1.8                crayon_1.5.2               
## [121] R.oo_1.25.0                 htmltools_0.5.4            
## [123] echoannot_0.99.10           tzdb_0.3.0                 
## [125] Formula_1.2-4               tidyr_1.2.1                
## [127] expm_0.999-7                Exact_3.2                  
## [129] DBI_1.1.3                   dbplyr_2.3.0               
## [131] MASS_7.3-58.1               rappdirs_0.3.3             
## [133] boot_1.3-28.1               dlstats_0.1.6              
## [135] Matrix_1.5-3                badger_0.2.2               
## [137] readr_2.1.3                 piggyback_0.1.4            
## [139] brio_1.1.3                  cli_3.6.0                  
## [141] R.methodsS3_1.8.2           parallel_4.2.1             
## [143] echofinemap_0.99.4          igraph_1.3.5               
## [145] GenomicRanges_1.50.2        pkgconfig_2.0.3            
## [147] rvcheck_0.2.1               GenomicAlignments_1.34.0   
## [149] dir.expiry_1.6.0            RCircos_1.2.2              
## [151] foreign_0.8-84              osfr_0.2.9                 
## [153] xml2_1.3.3                  XVector_0.38.0             
## [155] yulab.utils_0.0.6           echoLD_0.99.8              
## [157] stringr_1.5.0               VariantAnnotation_1.44.0   
## [159] digest_0.6.31               graph_1.76.0               
## [161] httpcode_0.3.0              Biostrings_2.66.0          
## [163] rmarkdown_2.19              cellranger_1.1.0           
## [165] htmlTable_2.4.1             gld_2.6.6                  
## [167] restfulr_0.0.15             curl_5.0.0                 
## [169] Rsamtools_2.14.0            rjson_0.2.21               
## [171] lifecycle_1.0.3             nlme_3.1-161               
## [173] jsonlite_1.8.4              desc_1.4.2                 
## [175] viridisLite_0.4.1           BSgenome_1.66.2            
## [177] fansi_1.0.3                 downloadR_0.99.5           
## [179] pillar_1.8.1                susieR_0.12.27             
## [181] GGally_2.1.2                lattice_0.20-45            
## [183] KEGGREST_1.38.0             fastmap_1.1.0              
## [185] httr_1.4.4                  survival_3.5-0             
## [187] glue_1.6.2                  zip_2.2.2                  
## [189] png_0.1-8                   bit_4.0.5                  
## [191] Rgraphviz_2.42.0            class_7.3-20               
## [193] stringi_1.7.12              blob_1.2.3                 
## [195] latticeExtra_0.6-30         memoise_2.0.1              
## [197] dplyr_1.0.10                irlba_2.3.5.1              
## [199] e1071_1.7-12                ape_5.6-2

	# library(susieR)
	# SUSIE's authors "merge[d] susie_ss and susie_bhat to susie_suff_stat" in 11/2019.
	susie_version <- utils::packageVersion("susieR")
	if(length(find("susie_bhat"))==0){
	printer("+ SUSIE:: Using `susie_suff_stat()` from susieR",paste0("v",susie_version),v=verbose)
	susie_func <- get("susie_suff_stat", asNamespace("susieR"))
	} else {
	printer("+ SUSIE:: Using `susie_bhat()` from susieR",paste0("v",susie_version),v=verbose)
	susie_func <- get("susie_bhat", asNamespace("susieR"))
	}

rajlabmssm / echolocator Goto Github PK

echolocator's Introduction

¶ Authors: Brian Schilder, Jack Humphrey, Towfique Raj ¶

¶ README updated: Jan-20-2023 ¶

echolocatoR: Automated statistical and functional fine-mapping

The echoverse

echoverse dependency graph

Citation

Installation

Installation troubleshooting

[Optional] Docker/Singularity

Documentation

All echoverse vignettes

🦇 echolocatoR

🦇 echodata

🦇 echotabix

🦇 echoannot

🦇 echoconda

🦇 echoLD

🦇 echoplot

🦇 echofinemap

🦇 echodeps

Introduction

Literature

For applications of echolocatoR in the literature, please see:

echolocatoR v1.0 vs. v2.0

Output descriptions

Multi-finemap results files

Column descriptions

Notes

Fine-mapping tools

Datasets

MungeSumstats:

catalogueR: QTLs

eQTL Catalogue: catalogueR::eQTL_Catalogue.query()

echodata: fine-mapping results

echolocatoR Fine-mapping Portal: pre-computed fine-mapping results

echoannot: Epigenomic & genome-wide annotations

Nott et al. (2019): echoannot::NOTT2019_*()

Corces et al.2020: echoannot::CORCES2020_*()

XGR: echoannot::XGR_download_and_standardize()

Roadmap: echoannot::ROADMAP_query()

biomaRt: echoannot::annotate_snps()

HaploR: echoannot::annotate_snps()

Enrichment tools

Implemented

XGR: echoannot::XGR_enrichment()

motifbreakR: echoannot::MOTIFBREAKR()

regioneR: echoannot::test_enrichment()

Under construction

LD reference panels

Custom LD panel:

Custom LD panel

Plotting

Tabix queries

Downloads

Developer

Session info

echolocator's People

Contributors

Stargazers

Watchers

Forkers

echolocator's Issues

Package Plan

Session Info

Issue

Solution

Proposed modules:

Extra modules

Problem

Solution attempts

Issue

Solution

Issue:

Potential source

Potential solution:

Slurm error message:

Expected behaviour

Issue

`echolocatoR`: Automated statistical and functional fine-mapping

For applications of `echolocatoR` in the literature, please see:

`echolocatoR` v1.0 vs. v2.0

`MungeSumstats`:

`catalogueR`: QTLs

eQTL Catalogue: `catalogueR::eQTL_Catalogue.query()`

`echodata`: fine-mapping results

`echoannot`: Epigenomic & genome-wide annotations

Nott et al. (2019): `echoannot::NOTT2019_*()`

Corces et al.2020: `echoannot::CORCES2020_*()`

XGR: `echoannot::XGR_download_and_standardize()`

Roadmap: `echoannot::ROADMAP_query()`

biomaRt: `echoannot::annotate_snps()`

HaploR: `echoannot::annotate_snps()`

XGR: `echoannot::XGR_enrichment()`

motifbreakR: `echoannot::MOTIFBREAKR()`

regioneR: `echoannot::test_enrichment()`