Fix R CMD check warnings: add inst/doc, replace XGR Rd cross-refs with \code{}

- Built and committed vignettes to inst/doc (fixes "no files in inst/doc" WARNING)
- Replaced \link[XGR]{...} with \code{XGR::...} since XGR is archived (fixes NOTE)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: bschilder

R/XGR_enrichment.R

@@ -1,6 +1,6 @@
 #' XGR enrichment
 #'
-#' Run SNP-level enrichment test with \link[XGR]{xGRviaGenomicAnno}.
+#' Run SNP-level enrichment test with \code{XGR::xGRviaGenomicAnno}.
 #'
 #' @param gr Annotations to test for enrichment with.
 #' @param merged_dat SNP-level fine-mapping results to test for enrichment with.

R/XGR_query.R

@@ -7,7 +7,7 @@
 #' For full list of libraries see
 #' \href{https://cran.r-project.org/package=XGR}{
 #'  here (XGR on CRAN).}
-#'  Passed to the \code{RData.customised} argument in \link[XGR]{xRDataLoader}.
+#'  Passed to the \code{RData.customised} argument in \code{XGR::xRDataLoader}.
 #' @param n_top Filter to only the top N annotations 
 #' that have the greatest amount of overlap with the genomic coordinates of
 #' \code{dat}. 

inst/doc/cell_type_specific_epigenomics.R

@@ -0,0 +1,15 @@
+## ----setup, echo=FALSE, include=FALSE-----------------------------------------
+pkg <- read.dcf("../DESCRIPTION", fields = "Package")[1]
+library(pkg, character.only = TRUE)
+
+## ----eval=FALSE---------------------------------------------------------------
+# dat <- echodata::BST1
+# histo_out <- echoannot::NOTT2019_epigenomic_histograms(dat = dat)
+
+## ----eval=FALSE---------------------------------------------------------------
+# knitr::kable(head(histo_out$data$raw))
+# knitr::kable(head(histo_out$data$peaks))
+
+## ----Session Info-------------------------------------------------------------
+utils::sessionInfo()
+

inst/doc/cell_type_specific_epigenomics.Rmd

@@ -0,0 +1,96 @@
+---
+title: "Cell-type-specific epigenomics" 
+author: "<h4>Author: <i>Brian M. Schilder</i></h4>" 
+date: "<h4>Updated: <i>`r format( Sys.Date(), '%b-%d-%Y')`</i></h4>"
+output:
+  BiocStyle::html_document
+vignette: >
+    %\VignetteIndexEntry{echoplot} 
+    %\usepackage[utf8]{inputenc}
+    %\VignetteEngine{knitr::rmarkdown}
+---
+
+```{r setup, echo=FALSE, include=FALSE}
+pkg <- read.dcf("../DESCRIPTION", fields = "Package")[1]
+library(pkg, character.only = TRUE)
+```
+
+```R
+library(`r pkg`)
+```
+
+## Nott2019
+
+`echoannot` includes data generated by
+["Nott2019"](https://doi.org/10.1126/science.aay0793):  
+
+> Nott A, Holtman IR, Coufal NG, ... Glass CK. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science. 2019 Nov 29;366(6469):1134-1139. doi: 10.1126/science.aay0793. Epub 2019 Nov 14. PMID: 31727856; PMCID: PMC7028213.
+
+### Import data 
+
+```R
+superenhancers <- echoannot::get_NOTT2019_superenhancer_interactome()
+enhancers_promoters <- echoannot::NOTT2019_get_promoter_interactome_data()
+peaks <- echoannot::NOTT2019_get_epigenomic_peaks() 
+```
+
+
+### Plot
+
+```{r, eval=FALSE}
+dat <- echodata::BST1
+histo_out <- echoannot::NOTT2019_epigenomic_histograms(dat = dat)
+```
+
+In addition to the plot object, 
+tables of both raw read ranges and called peaks are included in the output list.
+
+```{r, eval=FALSE}
+knitr::kable(head(histo_out$data$raw))
+knitr::kable(head(histo_out$data$peaks))
+```
+
+ 
+## Corces2020
+
+`echoannot` also includes data generated by
+["Corces2019"](https://doi.org/10.1038/s41588-020-00721-x):
+
+> Corces, M.R., Shcherbina, A., Kundu, S. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat Genet 52, 1158–1168 (2020). https://doi.org/10.1038/s41588-020-00721-x 
+
+### Import data 
+
+```R
+bulkATACseq_peaks <- echoannot::get_CORCES2020_bulkATACseq_peaks()
+cicero_coaccessibility <- echoannot::get_CORCES2020_cicero_coaccessibility()
+hichip_fithichip_loop_calls <- echoannot::get_CORCES2020_hichip_fithichip_loop_calls()
+scATACseq_celltype_peaks <- echoannot::get_CORCES2020_scATACseq_celltype_peaks()
+scATACseq_peaks <- echoannot::get_CORCES2020_scATACseq_peaks()
+```
+
+### Plot
+
+```R
+peak_dat <- echoannot::granges_overlap(
+    dat1 = dat,  
+    chrom_col.1 = "CHR",
+    start_col.1 = "POS",
+    dat2 = scATACseq_celltype_peaks, 
+    chrom_col.2 = "hg38_Chromosome",
+    start_col.2 = "hg38_Start",
+    end_col.2 = "hg38_Stop")
+ggbio::autoplot(peak_dat, 
+                ggplot2::aes(y=ExcitatoryNeurons, color=Effect)) 
+```
+
+
+# Session Info 
+
+<details> 
+
+```{r Session Info}
+utils::sessionInfo()
+```
+
+</details>  
+

inst/doc/cell_type_specific_epigenomics.html

@@ -0,0 +1,48 @@
+## ----setup, include=TRUE------------------------------------------------------
+library(echoannot)
+
+## ----eval=FALSE---------------------------------------------------------------
+# merged_DT <- echodata::get_Nalls2019_merged()
+
+## ----eval=FALSE---------------------------------------------------------------
+# #### Only query high-confidence fine-mapping SNPs from one locus ####
+# dat <- merged_DT[Locus=="LRRK2" & Consensus_SNP==TRUE,]
+# #### Query annotations ####
+# dat_annot <- echoannot::annotate_snps(dat = dat,
+#                                       haploreg_annotation = TRUE,
+#                                       regulomeDB_annotation = TRUE,
+#                                       biomart_annotation = TRUE)
+# knitr::kable(dat_annot)
+
+## ----eval=FALSE---------------------------------------------------------------
+# gg_cs_bin <- echoannot::CS_bin_plot(merged_DT = merged_DT,
+#                                     show_plot = FALSE)
+
+## ----eval=FALSE---------------------------------------------------------------
+# gg_cs_counts <- echoannot::CS_counts_plot(merged_DT = merged_DT,
+#                                           show_plot = FALSE)
+
+## ----eval=FALSE---------------------------------------------------------------
+# gg_epi <- echoannot::peak_overlap_plot(
+#     merged_DT = merged_DT,
+#     include.NOTT2019_enhancers_promoters = TRUE,
+#     include.NOTT2019_PLACseq = TRUE,
+#     #### Omit many annotations to save time ####
+#     include.NOTT2019_peaks = FALSE,
+#     include.CORCES2020_scATACpeaks = FALSE,
+#     include.CORCES2020_Cicero_coaccess = FALSE,
+#     include.CORCES2020_bulkATACpeaks = FALSE,
+#     include.CORCES2020_HiChIP_FitHiChIP_coaccess = FALSE,
+#     include.CORCES2020_gene_annotations = FALSE)
+
+## ----eval=FALSE---------------------------------------------------------------
+# super_plot <- echoannot::super_summary_plot(merged_DT = merged_DT,
+#                                             plot_missense = FALSE)
+# 
+
+## ----Cleanup, include=FALSE, eval=FALSE---------------------------------------
+# remove(super_plot, gg_epi, gg_cs_counts, merged_DT)
+
+## ----Session Info-------------------------------------------------------------
+utils::sessionInfo()
+

inst/doc/echoannot.R

@@ -0,0 +1,104 @@
+---
+title: "Getting Started" 
+author: "<h4>Author: <i>Brian M. Schilder</i></h4>" 
+date: "<h4>Updated: <i>`r format( Sys.Date(), '%b-%d-%Y')`</i></h4>"
+output:
+  BiocStyle::html_document
+vignette: >
+    %\VignetteIndexEntry{Getting started} 
+    %\usepackage[utf8]{inputenc}
+    %\VignetteEngine{knitr::rmarkdown}
+---
+
+```{r setup, include=TRUE} 
+library(echoannot)
+```
+ 
+
+# Import data  
+
+To get the full dataset of all fine-mapped Parkinson's Disease loci, 
+you can use the following function: 
+
+```{r, eval=FALSE}
+merged_DT <- echodata::get_Nalls2019_merged()
+```
+
+# Annotate 
+
+Annotate SNP-wise fine-mapping results.
+
+Here, we're only annotating a small number of SNPs high-confidence causal SNPs
+for demo purposes.
+The more SNPs you supply to `annotate_snps`, the longer it will take to 
+query the selected databases for each SNP.
+
+```{r, eval=FALSE}
+#### Only query high-confidence fine-mapping SNPs from one locus ####
+dat <- merged_DT[Locus=="LRRK2" & Consensus_SNP==TRUE,]
+#### Query annotations ####
+dat_annot <- echoannot::annotate_snps(dat = dat,
+                                      haploreg_annotation = TRUE, 
+                                      regulomeDB_annotation = TRUE,
+                                      biomart_annotation = TRUE) 
+knitr::kable(dat_annot)
+```
+
+
+# Summary plots
+
+## Credible Set bin plot
+
+```{r, eval=FALSE}
+gg_cs_bin <- echoannot::CS_bin_plot(merged_DT = merged_DT,
+                                    show_plot = FALSE)
+```
+
+## Credible Set counts plot
+
+```{r, eval=FALSE}
+gg_cs_counts <- echoannot::CS_counts_plot(merged_DT = merged_DT, 
+                                          show_plot = FALSE)
+```
+
+## Epigenomic data
+
+```{r, eval=FALSE}
+gg_epi <- echoannot::peak_overlap_plot(
+    merged_DT = merged_DT, 
+    include.NOTT2019_enhancers_promoters = TRUE,
+    include.NOTT2019_PLACseq = TRUE,
+    #### Omit many annotations to save time ####
+    include.NOTT2019_peaks = FALSE,
+    include.CORCES2020_scATACpeaks = FALSE, 
+    include.CORCES2020_Cicero_coaccess = FALSE, 
+    include.CORCES2020_bulkATACpeaks = FALSE, 
+    include.CORCES2020_HiChIP_FitHiChIP_coaccess = FALSE,
+    include.CORCES2020_gene_annotations = FALSE)
+```
+
+## Super summary plot
+
+Creates one big merged plot using the subfunctions above.
+
+```{r, eval=FALSE} 
+super_plot <- echoannot::super_summary_plot(merged_DT = merged_DT, 
+                                            plot_missense = FALSE)
+
+```
+
+```{r Cleanup, include=FALSE, eval=FALSE}
+remove(super_plot, gg_epi, gg_cs_counts, merged_DT)
+```
+
+
+# Session Info 
+
+<details> 
+
+```{r Session Info}
+utils::sessionInfo()
+```
+
+</details>  
+