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RajLabMSSM / echolocatoR

Automated statistical and functional fine-mapping pipeline with extensive API access to datasets.
https://rajlabmssm.github.io/echolocatoR
MIT License
30 stars 11 forks source link
bioinformatics colocalization echoverse finemap gwas linkage-disequilibrium qtl variant-annotation

readme





R build status License: GPL-3

¶ 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

``` r 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) ```

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:

  1. 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
  2. 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
  3. 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](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token) 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`](https://github.com/neurogenomics/MungeSumstats) 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](https://github.com/RajLabMSSM/echofinemap/issues/7) for details). Also, a common dependency conflict between `FINEMAP`\>=1.4 and MacOS has been resolved (see [this Issue](https://github.com/RajLabMSSM/echofinemap/issues/9) for details. - **`echodata`**: All example data and data transformation functions have been moved to the *echoverse* subpackage [`echodata`](https://github.com/RajLabMSSM/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. - **\.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). - **\.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:

For more detailed information about each dataset, use ?:

### Examples ###

library(echoannot)   
?NOTT_2019.interactome # epigenomic annotations
library(echodata) 
?BST1 # fine-mapping results
### [**`MungeSumstats`**](https://github.com/neurogenomics/MungeSumstats): - You can search, import, and standardize any GWAS in the [*Open GWAS*](https://gwas.mrcieu.ac.uk/) database via [`MungeSumstats`](https://github.com/neurogenomics/MungeSumstats), specifically the functions `find_sumstats` and `import_sumstats`. ### [`catalogueR`](https://github.com/RajLabMSSM/catalogueR): QTLs #### [eQTL Catalogue](https://www.ebi.ac.uk/eqtl/): `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`](https://github.com/RajLabMSSM/catalogueR) R package. ### [`echodata`](https://github.com/RajLabMSSM/catalogueR): fine-mapping results #### [***echolocatoR Fine-mapping Portal***](https://rajlab.shinyapps.io/Fine_Mapping_Shiny): 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`](https://github.com/RajLabMSSM/echoannot): Epigenomic & genome-wide annotations #### [Nott et al. (2019)](https://science.sciencemag.org/content/366/6469/1134.abstract): `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](https://doi.org/10.1038/s41588-020-00721-x): `echoannot::CORCES2020_*()` - Data from this publication contains results from single-cell and bulk chromatin accessibility assays (\[sc\]ATAC-seq) and chromatin interactions ( [`FitHiChIP`](https://ay-lab.github.io/FitHiChIP/)) from *postmortem* adult human brain tissue. #### [XGR](http://xgr.r-forge.r-project.org): `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](http://www.roadmapepigenomics.org): `echoannot::ROADMAP_query()` - API access to cell type-specific epigenomic data. #### [biomaRt](https://bioconductor.org/packages/release/bioc/html/biomaRt.html): `echoannot::annotate_snps()` - API access to various genome-wide SNP annotations (e.g. missense, nonsynonmous, intronic, enhancer). #### [HaploR](https://cran.r-project.org/web/packages/haploR/vignettes/haplor-vignette.html): `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](http://xgr.r-forge.r-project.org): `echoannot::XGR_enrichment()` - Binomial enrichment tests between customisable foreground and background SNPs. #### [motifbreakR](https://github.com/Simon-Coetzee/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](https://bioconductor.org/packages/release/bioc/html/MotifDb.html) (9,900+ annotated position frequency matrices from 14 public sources, for multiple organisms). #### [regioneR](http://bioconductor.org/packages/release/bioc/html/regioneR.html): `echoannot::test_enrichment()` - Iterative pairwise permutation testing of overlap between all combinations of two [`GRangesList`](https://biodatascience.github.io/compbio/bioc/GRL.html) objects. ### Under construction #### [GARFIELD](https://www.bioconductor.org/packages/release/bioc/html/garfield.html) - Genomic enrichment with LD-informed heuristics. #### [GoShifter](https://github.com/immunogenomics/goshifter) - LD-informed iterative enrichment analysis. #### [S-LDSC](https://www.nature.com/articles/ng.3954) - Genome-wide stratified LD score regression. - Inlccles 187-annotation baseline model from [Gazal et al. 2018](https://www.nature.com/articles/s41588-018-0231-8). - 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():

### [UK Biobank](https://www.ukbiobank.ac.uk) ### [1000 Genomes Phase 1](https://www.internationalgenome.org) ### [1000 Genomes Phase 3](https://www.internationalgenome.org) ### Custom LD panel: - From user-supplied VCFs ### Custom LD panel - From user-supplied precomputed LD matrices

Plotting

Plotting functions are now implemented via:

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`](https://github.com/axel-download-accelerator/axel), and is particularly useful for speeding up downloads of large files. - `axel` is installed via the official *echoverse* [conda](https://docs.conda.io/en/latest/) 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

``` r 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 ```