Add plot_granges

DESCRIPTION

@@ -3,24 +3,30 @@ Title: Accessing AlphaMissense Data Resources in R
 Version: 1.1.5
 Authors@R:
     c(person(
-        "Martin", "Morgan", role = c("aut", "cre"),
-        email = "mtmorgan.xyz@gmail.com",
-        comment = c(ORCID = "0000-0002-5874-8148")
+	"Martin", "Morgan", role = c("aut", "cre"),
+	email = "mtmorgan.xyz@gmail.com",
+	comment = c(ORCID = "0000-0002-5874-8148")
     ), person(
-        "Tram", "Nguyen",
-        role = "aut"
+	"Tram", "Nguyen",
+	role = "aut"
     ), person(
-        "Chan Zuckerberg Initiative DAF CZF2019-002443",
-        role = "fnd"
+	"Tyrone", "Lee",
+	role = "ctb"
     ), person(
-        "NIH NCI ITCR U24CA180996",
-        role = "fnd"
+	"Nitesh", "Turaga",
+	role = "ctb"
     ), person(
-        "NIH NCI IOTN U24CA232979",
-        role = "fnd"
+	"Chan Zuckerberg Initiative DAF CZF2019-002443",
+	role = "fnd"
     ), person(
-        "NIH NCI ARTNet U24CA274159",
-        role = "fnd"
+	"NIH NCI ITCR U24CA180996",
+	role = "fnd"
+    ), person(
+	"NIH NCI IOTN U24CA232979",
+	role = "fnd"
+    ), person(
+	"NIH NCI ARTNet U24CA274159",
+	role = "fnd"
     ))
 Description:
     The AlphaMissense publication
@@ -51,7 +57,7 @@ Suggests:
     ensembldb,
     httr,
     tidyr,
-    r3dmol, bio3d, shiny,
+    r3dmol, bio3d, shiny, shiny.gosling,
     colorspace,
     knitr,
     rmarkdown,
@@ -60,6 +66,6 @@ biocViews: SNP, Annotation, FunctionalGenomics, StructuralPrediction,
     Transcriptomics, VariantAnnotation, GenePrediction, ImmunoOncology
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 VignetteBuilder: knitr
 Config/testthat/edition: 3

NAMESPACE

@@ -17,7 +17,11 @@ export(db_disconnect_all)
 export(db_range_join)
 export(db_tables)
 export(db_temporary_table)
+export(plot_granges)
 export(to_GPos)
+import(GenomicRanges)
+import(shiny)
+import(shiny.gosling)
 importFrom(BiocBaseUtils,isCharacter)
 importFrom(BiocBaseUtils,isScalarCharacter)
 importFrom(BiocBaseUtils,isScalarLogical)

R/plot_shinygosling.R

@@ -0,0 +1,226 @@
+#' @rdname plot_shinygosling
+#'
+#' @title Plot a GRanges object using Shiny and Gosling
+#'
+#' @description This function creates a Shiny app that displays a
+#'     Gosling plot of a given GRanges object.  It visualizes genomic
+#'     ranges with both rectangle and point representations, and
+#'     allows for customization of the plot title and subtitle.
+
+#'     The function creates two tracks: a reference track with
+#'     rectangles and an alternative track with points. It assumes
+#'     that the GRanges object has 'am_class' and 'ALT' metadata
+#'     columns. The 'am_class' column is used for coloring the points,
+#'     while 'ALT' is used for the text of the points.
+
+#' @param gr A GRanges object containing the genomic ranges to
+#'     be plotted.
+#' @param title Character string. The title of the plot. Default is
+#'     "GRanges Plot".
+#' @param subtitle Character string. The subtitle of the plot. Default
+#'     is "Stacked nucleotide example".
+#' @param plot_type Character string. Select the type of gosling plot. 
+#' Default is "bar"
+#'     - "bars": Stacked bar plot with height based on pathogenicity score
+#'     - "lolipop": variation of a bar chart where the bar is replaced with a 
+#'     line and a dot at the end to show mutation variations.
+#'
+#' @return A Shiny app object that, when run, displays the Gosling
+#'     plot.
+#'
+#' @note This function requires the shiny, shiny.gosling, and GenomicRanges
+#'     packages to be installed.
+#'
+#' @examples
+#' if (requireNamespace("GenomicRanges")) {
+#'
+#' ## Create a sample GRanges object from AlphamissenseR
+#' gpos <-
+#'    am_data("hg38") |>
+#'    filter(uniprot_id == "Q1W6H9") |>
+#'    to_GPos()
+#'
+#' ## Plot the GRanges object
+#' plot_granges(gpos, mode = "bar", title = "Q1W6H9 track", subtitle 
+#' = "bar plot example")
+#' }
+#'
+#'
+#' @export
+plot_granges <-
+    function(gr,
+             title = "GRanges Plot",
+             subtitle = "Stacked nucleotide example",
+             plot_type = "bars")
+{
+    ## Validate input
+    stopifnot(
+        "Input must be a GRanges or GPos object" = 
+            inherits(gr, c("GRanges", "GPos"))
+    )
+    stopifnot(
+        "Title must be a scalar string" = 
+            is.character(title) && length (title) == 1 && !is.na(title) && 
+            nzchar(title)
+        )
+    stopifnot(
+        "Subtitle must be a scalar string" = 
+            is.character(subtitle) && length (subtitle) == 1 && !is.na(subtitle)
+            && nzchar(subtitle)
+    )
+    stopifnot(
+        "Type must be a scalar string" = 
+            is.character(plot_type) && length (plot_type) == 1 && !is.na(plot_type)
+        && nzchar(plot_type)
+    )
+
+    ## Define categories and color mapping
+    categories <- c("likely_benign", "ambiguous", "likely_pathogenic")
+    colormapping <- c("#89d5f5", "gray", "#f56c6c")
+
+    ## Get range from GRanges object
+    r <- range(gr)
+
+    ## Prepare track data
+    track_data <- track_data_gr(
+        gr,
+        chromosomeField = "seqnames",
+        genomicFields = c("start", "end")
+    )
+
+    ## This fixes the bug if .gosling directory does not already exist
+    cache_dir <- file.path(tools::R_user_dir("AlphaMissenseR", which = "cache"), ".gosling")
+    if (!dir.exists(cache_dir))
+        ## TODO: check return value to ensure directory is created successfully
+        dir.create(cache_dir, recursive = TRUE)
+
+    ## trigger the option for bars or lolipop        
+    if (plot_type =="bars"){
+        #define single track
+        track_bar <- add_single_track(
+            width = 800,
+            height = 180,
+            data = track_data,
+            mark = "bar",
+            x = visual_channel_x(
+                field = "start", type = "genomic", axis = "bottom"
+            ),
+            xe = visual_channel_x(field = "end", type = "genomic"),
+            y = visual_channel_y(
+                field = "am_pathogenicity", type = "quantitative", axis = "right"
+            ),
+            color = visual_channel_color(
+                field = "am_pathogenicity",
+                type = "quantitative"
+            ),
+            tooltip = visual_channel_tooltips(
+                visual_channel_tooltip(field = "REF", type = "nominal",
+                                       alt = "Reference"),
+                visual_channel_tooltip(field = "ALT", type = "nominal",
+                                       alt = "Alternative / Mutation"),
+                visual_channel_tooltip(
+                    field = "am_pathogenicity",
+                    type = "quantitative",
+                    alt = "AM_Pathogenicity Score",
+                    format = "0.2"
+                ) ),
+            size = list(value = 5)
+        )
+
+        composed_view <- compose_view(
+            layout = "linear",
+            xDomain = list(chromosome = as.character(seqnames(r)),
+                           interval = c(start(r), end(r))),
+            tracks = track_bar
+
+        )
+
+    ## other track    
+    } else if (plot_type == "lolipop"){
+
+    ## Define multi tracks
+    track_ref <- add_single_track(
+        data = track_data,
+        mark = "rect",
+        x = visual_channel_x(field = "start", type = "genomic", axis = "top"),
+        xe = visual_channel_x(field = "end", type = "genomic"),
+        size = list(value = 50),
+        stroke = "lightgrey",
+        strokeWidth = list(value = 1),
+        opacity = list(value = 0.3)
+    )
+
+    track_alt <- add_single_track(
+        data = track_data,
+        mark = "point",
+        x = visual_channel_x(field = "start", type = "genomic", axis = "top"),
+        xe = visual_channel_x(field = "end", type = "genomic"),
+        y = visual_channel_y(field = "am_class", type="nominal", 
+                       domain= categories, axis = "left",baseline = "ambiguous" ),
+        text = list(field = "ALT", type = "nominal"),
+        size = list(value = 5),
+        tooltip = visual_channel_tooltips(
+            visual_channel_tooltip(field = "REF", type = "nominal",
+                                   alt = "Reference"),
+            visual_channel_tooltip(field = "ALT", type = "nominal",
+                                   alt = "Alternative / Mutation"),
+            visual_channel_tooltip(
+                field = "am_pathogenicity",
+                type = "quantitative",
+                alt = "AM_Pathogenicity Score",
+                format = "0.2"
+            ) )
+    )
+
+    ## Compose view
+    composed_view <- compose_view(
+        width = 800,
+        height = 180,
+        multi = TRUE,
+        layout = "linear",
+        xDomain = list(
+            chromosome = as.character(seqnames(r)),
+            interval = c(start(r), end(r))
+        ),
+        alignment = "overlay",
+        color = visual_channel_color(
+            field = "am_class",
+            type = "nominal",
+            domain = categories,
+            baseline = "ambiguous",
+            range = colormapping,
+            legend = TRUE
+        ),
+        tracks = add_multi_tracks(track_ref, track_alt)
+    )
+
+    } 
+    ## trigger check
+    else {
+        stop("Invalid plot_type. Use 'bars' or 'lolipop'")
+    }
+    ## Arrange into view
+    arranged_view3 <- arrange_views(
+        title = title,
+        subtitle = subtitle,
+        views = composed_view
+    )
+
+    ## Create Shiny app
+    ui <- fluidPage(
+        use_gosling(clear_files = FALSE),
+        goslingOutput("gosling_plot")
+    )
+
+    server <- function(input, output, session) {
+        output$gosling_plot <- renderGosling({
+            gosling(
+                component_id = "component_3",
+                arranged_view3
+            )
+        })
+    }
+
+    ## Return the Shiny app
+    shinyApp(ui, server)
+}

vignettes/alphafold.Rmd

@@ -241,6 +241,32 @@ r3dmol::r3dmol() |>
     r3dmol::m_zoom_to()
 ```
 
+# Visualizing sequence tracks
+
+The variant prediction data can also be visualized on a genome browser. Mis-sense point mutations can be tracked alongside pathogenicity on multiple scales by utilizing Gosling, a declarative genomics visualization library.
+
+First, create a GPos object for a protein of interest.
+```{r mk_gpos}
+library(GenomicRanges)
+
+tbl <-
+    am_data("hg38") |>
+    filter(uniprot_id == "Q1W6H9")
+
+gpos <-
+    tbl |>
+    to_GPos()
+gp <- as(gpos, "GRanges")
+gp
+
+```
+
+The function `plot_granges` serves as a wrapper that processes the alphamissenseR formatted GPos object into a multiscale lolipop plot in [shiny.gosling][]. The resulting plot is a shinyapp object that can be embedded in Rmarkdown or Quarto.
+```{r plot_example}
+AlphaMissenseR::plot_granges(gr, title = "Test track : Q1W6H9", subtitle = "Multiscale plot in Shiny.Gosling")
+```
+
+
 # Finally
 
 Remember to disconnect and shutdown all managed DuckDB connections.

vignettes/introduction.Rmd

@@ -1,6 +1,7 @@
 ---
 title: "A. Introduction"
 output: rmarkdown::html_vignette
+runtime: shiny
 vignette: >
   %\VignetteIndexEntry{A. Introduction}
   %\VignetteEngine{knitr::rmarkdown}