Summarise
¶ Author:
Brian M. Schilder ¶
¶ Updated:
Jan-20-2023 ¶
Source: vignettes/summarise.Rmd
summarise.Rmd## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## ⠊⠉⠡⣀⣀⠊⠉⠡⣀⣀⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠
## ⠌⢁⡐⠉⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠉⢂⡈⠑⣀⠉⢄⡈⠡⣀
## ⠌⡈⡐⢂⢁⠒⡈⡐⢂⢁⠒⡈⡐⢂⢁⠑⡈⡈⢄⢁⠡⠌⡈⠤⢁⠡⠌⡈⠤⢁⠡⠌⡈⡠⢁⢁⠊⡈⡐⢂## ## ── 🦇 🦇 🦇 e c h o l o c a t o R 🦇 🦇 🦇 ─────────────────────────────────## ## ── v2.0.3 ──────────────────────────────────────────────────────────────────────## ⠌⡈⡐⢂⢁⠒⡈⡐⢂⢁⠒⡈⡐⢂⢁⠑⡈⡈⢄⢁⠡⠌⡈⠤⢁⠡⠌⡈⠤⢁⠡⠌⡈⡠⢁⢁⠊⡈⡐⢂
## ⠌⢁⡐⠉⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠊⢂⡐⠑⣀⠉⢂⡈⠑⣀⠉⢄⡈⠡⣀
## ⠊⠉⠡⣀⣀⠊⠉⠡⣀⣀⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠
## ⓞ If you use echolocatoR or any of the echoverse subpackages, 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
## ⓞ Please report any bugs/feature requests on GitHub:
## ▶
## https://github.com/RajLabMSSM/echolocatoR/issues
## ⓞ Contributions are welcome!:
## ▶
## https://github.com/RajLabMSSM/echolocatoR/pulls## ## ────────────────────────────────────────────────────────────────────────────────Summarise
Using pre-merged data for vignette speed.
merged_DT <- echodata::get_Nalls2019_merged()
get_SNPgroup_counts()
Get the number of SNPs for each SNP group per locus.
It also prints the mean number of SNPs for each SNP group across all
loci.
NOTE: You will need to make sure to set
merge_finemapping_results(minimum_support=1) in the above
step to get accurate counts for all SNP groups.
snp_groups <- echodata::get_SNPgroup_counts(merged_DT = merged_DT)## All loci (75) :## Total.SNPs nom.sig.GWAS sig.GWAS
## 4948.16 924.68 82.36
## CS Consensus topConsensus
## 7.88 2.69 1.47
## topConsensus.leadGWAS
## 0.41## Loci with at least one Consensus SNP (69) :## Total.SNPs nom.sig.GWAS sig.GWAS
## 5019.07 911.41 84.28
## CS Consensus topConsensus
## 7.77 2.93 1.59
## topConsensus.leadGWAS
## 0.45
get_CS_counts()
County the number of tool-specific and UCS Credible Set SNPs per locus.
UCS_counts <- echodata::get_CS_counts(merged_DT = merged_DT)
knitr::kable(UCS_counts)| Locus | ABF.CS_size | SUSIE.CS_size | POLYFUN_SUSIE.CS_size | FINEMAP.CS_size | mean.CS_size | UCS.CS_size |
|---|---|---|---|---|---|---|
| GPNMB | 0 | 4 | 5 | 5 | 0 | 13 |
| MAP4K4 | 0 | 4 | 3 | 5 | 0 | 12 |
| MBNL2 | 0 | 4 | 4 | 5 | 0 | 12 |
| TMEM163 | 0 | 5 | 5 | 5 | 0 | 12 |
| CRLS1 | 0 | 3 | 4 | 5 | 0 | 11 |
| DNAH17 | 0 | 5 | 4 | 5 | 0 | 11 |
| GBF1 | 0 | 5 | 5 | 5 | 0 | 11 |
| GCH1 | 0 | 5 | 2 | 4 | 0 | 11 |
| MIPOL1 | 0 | 2 | 4 | 5 | 0 | 11 |
| FBRSL1 | 0 | 3 | 3 | 5 | 0 | 10 |
| FYN | 0 | 3 | 2 | 5 | 0 | 10 |
| GALC | 0 | 3 | 3 | 5 | 0 | 10 |
| LCORL | 0 | 4 | 3 | 5 | 0 | 10 |
| LOC100131289 | 0 | 5 | 0 | 5 | 0 | 10 |
| MED12L | 0 | 4 | 4 | 5 | 0 | 10 |
| SLC2A13 | 0 | 5 | 5 | 5 | 0 | 10 |
| TRIM40 | 0 | 10 | 0 | 1 | 0 | 10 |
| ATG14 | 0 | 5 | 5 | 5 | 0 | 9 |
| CD19 | 0 | 5 | 5 | 5 | 0 | 9 |
| CHRNB1 | 0 | 4 | 5 | 5 | 0 | 9 |
| FAM49B | 0 | 4 | 4 | 5 | 0 | 9 |
| ITGA8 | 0 | 4 | 4 | 4 | 0 | 9 |
| ITPKB | 0 | 4 | 4 | 5 | 0 | 9 |
| MCCC1 | 0 | 5 | 5 | 5 | 0 | 9 |
| RPS6KL1 | 0 | 2 | 4 | 5 | 0 | 9 |
| SH3GL2 | 0 | 3 | 3 | 5 | 0 | 9 |
| STK39 | 0 | 5 | 5 | 5 | 0 | 9 |
| TMEM175 | 1 | 4 | 4 | 5 | 0 | 9 |
| C5orf24 | 0 | 4 | 4 | 5 | 0 | 8 |
| CLCN3 | 0 | 5 | 4 | 5 | 0 | 8 |
| CRHR1 | 0 | 5 | 5 | 5 | 0 | 8 |
| CTSB | 0 | 3 | 2 | 5 | 0 | 8 |
| DLG2 | 0 | 3 | 3 | 5 | 0 | 8 |
| DYRK1A | 0 | 2 | 3 | 5 | 0 | 8 |
| IGSF9B | 0 | 4 | 4 | 5 | 0 | 8 |
| IP6K2 | 0 | 4 | 5 | 4 | 0 | 8 |
| NOD2 | 0 | 3 | 3 | 4 | 0 | 8 |
| NUCKS1 | 0 | 5 | 5 | 5 | 0 | 8 |
| RETREG3 | 0 | 4 | 4 | 5 | 0 | 8 |
| RNF141 | 0 | 3 | 4 | 5 | 0 | 8 |
| SIPA1L2 | 0 | 3 | 3 | 5 | 0 | 8 |
| SP1 | 0 | 3 | 3 | 5 | 0 | 8 |
| SPPL2B | 0 | 4 | 3 | 4 | 0 | 8 |
| WNT3 | 0 | 4 | 4 | 5 | 0 | 8 |
| FCGR2A | 0 | 3 | 3 | 5 | 0 | 7 |
| GS1-124K5.11 | 0 | 2 | 2 | 5 | 0 | 7 |
| HIP1R | 1 | 3 | 3 | 5 | 1 | 7 |
| KCNIP3 | 0 | 3 | 3 | 5 | 0 | 7 |
| KPNA1 | 0 | 2 | 2 | 5 | 0 | 7 |
| LINC00693 | 0 | 2 | 3 | 5 | 0 | 7 |
| LRRK2 | 0 | 4 | 4 | 5 | 0 | 7 |
| SETD1A | 0 | 3 | 3 | 5 | 0 | 7 |
| SNCA | 0 | 4 | 4 | 5 | 0 | 7 |
| SYT17 | 0 | 3 | 3 | 5 | 0 | 7 |
| VAMP4 | 0 | 5 | 3 | 0 | 0 | 7 |
| VPS13C | 0 | 2 | 2 | 5 | 0 | 7 |
| ELOVL7 | 0 | 2 | 2 | 4 | 0 | 6 |
| FAM171A2 | 0 | 3 | 3 | 5 | 0 | 6 |
| FGF20 | 0 | 2 | 2 | 5 | 0 | 6 |
| HLA-DRB5 | 0 | 5 | 0 | 5 | 4 | 6 |
| INPP5F | 1 | 5 | 5 | 5 | 1 | 6 |
| KRTCAP2 | 0 | 3 | 3 | 5 | 0 | 6 |
| MEX3C | 0 | 1 | 2 | 5 | 0 | 6 |
| RIMS1 | 0 | 1 | 1 | 5 | 0 | 6 |
| RIT2 | 0 | 2 | 2 | 5 | 1 | 6 |
| RPS12 | 0 | 2 | 2 | 5 | 0 | 6 |
| SCAF11 | 0 | 2 | 2 | 5 | 0 | 6 |
| SPTSSB | 0 | 2 | 2 | 5 | 0 | 6 |
| UBAP2 | 0 | 2 | 2 | 5 | 0 | 6 |
| KCNS3 | 0 | 5 | 4 | 0 | 0 | 5 |
| CHD9 | 0 | 3 | 3 | 0 | 0 | 3 |
| FAM47E-STBD1 | 0 | 3 | 3 | 0 | 0 | 3 |
| PAM | 0 | 2 | 3 | 0 | 0 | 3 |
| SATB1 | 0 | 3 | 3 | 0 | 0 | 3 |
| LMNB1 | 0 | 0 | 0 | 1 | 0 | 1 |
Plot
- The following functions each return a list containing both the
...$plotand the...$dataused to make the plot.
- Where available,
snp_filterallows user to use any filtering argument (supplied as a string) to subset the data they want to use in the plot/data.
Colocalization results
If you ran colocalization tests with echolocatoR (via
catalogueR) you can use those results to come up with a top
QTL nominated gene for each locus (potentially implicating that gene in
your phenotype).
coloc_res <- echodata::get_Nalls2019_coloc() Super summary plot
super_plot <- echoannot::super_summary_plot(merged_DT = merged_DT,
coloc_results = coloc_res,
plot_missense = FALSE)## + SUMMARISE:: Nominating genes by top colocalized eQTL eGenes## Importing previously downloaded files: /github/home/.cache/R/echoannot/NOTT2019_epigenomic_peaks.rds## ++ NOTT2019:: 634,540 ranges retrieved.## Converting dat to GRanges object.## 113 query SNP(s) detected with reference overlap.## ++ NOTT2019:: Getting regulatory regions data.## Importing Astrocyte enhancers ...## Importing Astrocyte promoters ...## Importing Neuronal enhancers ...## Importing Neuronal promoters ...## Importing Oligo enhancers ...## Importing Oligo promoters ...## Importing Microglia enhancers ...## Importing Microglia promoters ...## Converting dat to GRanges object.
## Converting dat to GRanges object.## 48 query SNP(s) detected with reference overlap.## ++ NOTT2019:: Getting interaction anchors data.## Importing Microglia interactome ...## Importing Neuronal interactome ...## Importing Oligo interactome ...## Converting dat to GRanges object.## 52 query SNP(s) detected with reference overlap.## Converting dat to GRanges object.## 44 query SNP(s) detected with reference overlap.## CORCES2020:: Extracting overlapping cell-type-specific scATAC-seq peaks## Converting dat to GRanges object.## 13 query SNP(s) detected with reference overlap.## CORCES2020:: Annotating peaks by cell-type-specific target genes## CORCES2020:: Extracting overlapping bulkATAC-seq peaks from brain tissue## Converting dat to GRanges object.## 4 query SNP(s) detected with reference overlap.## CORCES2020:: Annotating peaks by bulk brain target genes## Converting dat to GRanges object.## 70 query SNP(s) detected with reference overlap.## Converting dat to GRanges object.## 72 query SNP(s) detected with reference overlap.## + CORCES2020:: Found 142 hits with HiChIP_FitHiChIP coaccessibility loop anchors.## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## ℹ The deprecated feature was likely used in the echoannot package.
## Please report the issue at <https://github.com/RajLabMSSM/echoannot/issues>.## Found more than one class "simpleUnit" in cache; using the first, from namespace 'hexbin'## Also defined by 'ggbio'## Warning: Removed 83 rows containing missing values (`position_stack()`).-1.png)
Session info
utils::sessionInfo()## R Under development (unstable) (2023-01-11 r83598)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] echolocatoR_2.0.3 BiocStyle_2.27.0
##
## loaded via a namespace (and not attached):
## [1] ProtGenerics_1.31.0 fs_1.5.2
## [3] matrixStats_0.63.0 bitops_1.0-7
## [5] httr_1.4.4 RColorBrewer_1.1-3
## [7] Rgraphviz_2.43.0 tools_4.3.0
## [9] backports_1.4.1 utf8_1.2.2
## [11] R6_2.5.1 DT_0.27
## [13] lazyeval_0.2.2 withr_2.5.0
## [15] prettyunits_1.1.1 GGally_2.1.2
## [17] gridExtra_2.3 cli_3.6.0
## [19] Biobase_2.59.0 textshaping_0.3.6
## [21] labeling_0.4.2 ggbio_1.47.0
## [23] sass_0.4.4 mvtnorm_1.1-3
## [25] readr_2.1.3 proxy_0.4-27
## [27] pkgdown_2.0.7 mixsqp_0.3-48
## [29] Rsamtools_2.15.1 systemfonts_1.0.4
## [31] foreign_0.8-84 R.utils_2.12.2
## [33] dichromat_2.0-0.1 maps_3.4.1
## [35] BSgenome_1.67.3 readxl_1.4.1
## [37] susieR_0.12.27 pals_1.7
## [39] rstudioapi_0.14 RSQLite_2.2.20
## [41] httpcode_0.3.0 generics_0.1.3
## [43] BiocIO_1.9.1 echoconda_0.99.9
## [45] dplyr_1.0.10 zip_2.2.2
## [47] Matrix_1.5-3 interp_1.1-3
## [49] fansi_1.0.3 DescTools_0.99.47
## [51] S4Vectors_0.37.3 catalogueR_1.0.1
## [53] R.methodsS3_1.8.2 lifecycle_1.0.3
## [55] yaml_2.3.6 SummarizedExperiment_1.29.1
## [57] BiocFileCache_2.7.1 echoplot_0.99.6
## [59] grid_4.3.0 blob_1.2.3
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## [63] lattice_0.20-45 GenomicFeatures_1.51.2
## [65] mapproj_1.2.11 KEGGREST_1.39.0
## [67] pillar_1.8.1 knitr_1.41
## [69] GenomicRanges_1.51.4 rjson_0.2.21
## [71] osfr_0.2.9 boot_1.3-28.1
## [73] gld_2.6.6 codetools_0.2-18
## [75] glue_1.6.2 data.table_1.14.6
## [77] coloc_5.1.0.1 vctrs_0.5.1
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## [85] dnet_1.1.7 xfun_0.36
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## [93] filelock_1.0.2 GenomeInfoDb_1.35.12
## [95] rprojroot_2.0.3 bslib_0.4.2
## [97] irlba_2.3.5.1 rpart_4.1.19
## [99] colorspace_2.0-3 BiocGenerics_0.45.0
## [101] DBI_1.1.3 Hmisc_4.7-2
## [103] nnet_7.3-18 Exact_3.2
## [105] tidyselect_1.2.0 bit_4.0.5
## [107] compiler_4.3.0 curl_5.0.0
## [109] graph_1.77.1 htmlTable_2.4.1
## [111] expm_0.999-7 basilisk.utils_1.11.1
## [113] xml2_1.3.3 desc_1.4.2
## [115] DelayedArray_0.25.0 bookdown_0.32
## [117] rtracklayer_1.59.1 checkmate_2.1.0
## [119] scales_1.2.1 hexbin_1.28.2
## [121] echoLD_0.99.9 RBGL_1.75.0
## [123] RCircos_1.2.2 rappdirs_0.3.3
## [125] stringr_1.5.0 supraHex_1.37.0
## [127] digest_0.6.31 piggyback_0.1.4
## [129] rmarkdown_2.20 basilisk_1.11.2
## [131] XVector_0.39.0 htmltools_0.5.4
## [133] pkgconfig_2.0.3 jpeg_0.1-10
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## [137] echodata_0.99.16 highr_0.10
## [139] ensembldb_2.23.1 dbplyr_2.3.0
## [141] fastmap_1.1.0 rlang_1.0.6
## [143] htmlwidgets_1.6.1 farver_2.1.1
## [145] echofinemap_0.99.5 jquerylib_0.1.4
## [147] jsonlite_1.8.4 BiocParallel_1.33.9
## [149] R.oo_1.25.0 VariantAnnotation_1.45.0
## [151] RCurl_1.98-1.9 magrittr_2.0.3
## [153] Formula_1.2-4 GenomeInfoDbData_1.2.9
## [155] ggnetwork_0.5.10 patchwork_1.1.2
## [157] munsell_0.5.0 Rcpp_1.0.9
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## [163] stringi_1.7.12 rootSolve_1.8.2.3
## [165] zlibbioc_1.45.0 MASS_7.3-58.1
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## [169] ggrepel_0.9.2 snpStats_1.49.0
## [171] lmom_2.9 deldir_1.0-6
## [173] echoannot_0.99.10 Biostrings_2.67.0
## [175] splines_4.3.0 hms_1.1.2
## [177] igraph_1.3.5 reshape2_1.4.4
## [179] biomaRt_2.55.0 stats4_4.3.0
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## [183] evaluate_0.20 latticeExtra_0.6-30
## [185] biovizBase_1.47.0 BiocManager_1.30.19
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## [189] purrr_1.0.1 reshape_0.8.9
## [191] ggplot2_3.4.0 echotabix_0.99.9
## [193] restfulr_0.0.15 AnnotationFilter_1.23.0
## [195] e1071_1.7-12 downloadR_0.99.6
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## [203] AnnotationDbi_1.61.0 GenomicAlignments_1.35.0
## [205] IRanges_2.33.0 cluster_2.1.4