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Deploy preview for PR 542 🛫
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aditya-nath-sage committed Nov 21, 2024
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9 changes: 9 additions & 0 deletions pr-preview/pr-542/Dockerfile
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FROM ghcr.io/nf-osi/nfportalutils:develop

WORKDIR /app

RUN apt-get -yq install pandoc

RUN R -e "install.packages(c('rmarkdown', 'reactable', 'visNetwork'), repos='http://cran.rstudio.com/')"

ENTRYPOINT ["/bin/bash", "R", "-e", "rmarkdown::render('docs/index.Rmd')"]
119 changes: 119 additions & 0 deletions pr-preview/pr-542/custom.css
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@font-face {
font-family: DM Sans,sans-serif;
src: url(https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,100..1000;1,9..40,100..100);
}

body {
font-family: DM Sans,sans-serif;
}

div.main-container {
max-width: 1200px !important;
}

.tag {
width: 30px;
height: 30px;
border: 1px solid rgba(0, 0, 0, 0.03);
border-radius: 20%;
color: #000;
font-size: 13px;
letter-spacing: -2px;
}

.warning {
color: #000;
background-color: orange;
}

.good {
color: #000;
background-color: #50C878;
}

div.info {
padding: 1em;
margin: 1em 0;
padding-left: 80px;
background-size: 30px;
background-repeat: no-repeat;
background-image: url("info_icon.svg");
background-position: 15px center;
background-color: rgba(18,94,129,.05);

}

.term-table {
margin-top: 16px;
border: 1px solid hsl(213, 33%, 93%);
border-radius: 4px;
box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.1);
}

.template-title {
font-size: 14px;
}

.detail {
padding: 24px;
box-shadow: inset 0 1px 3px #dbdbdb;
background: hsl(213, 20%, 99%);
}

.detail-label {
margin: 20px 0 4px;
font-size: 14px;
color: rgba(0, 0, 0, 0.6);
}

.detail-header {
margin-bottom: 16px;
font-size: 20px;
font-weight: 600;
}

.detail-title {
margin-left: 18px;
font-size: 14px;
font-weight: 400;
color: rgba(0, 0, 0, 0.6);
}

/* Custom tab pills */

.nav-pills > li > a {
background-color: #fff;
border: 1px solid #e2e8f0;
border-radius: 0.5rem;
box-sizing: border-box;
color: #0d172a;
cursor: pointer;
display: inline-block;
font-family: "Basier circle",-apple-system,system-ui,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";
font-size: 1.1rem;
font-weight: 600;
line-height: 1;
padding: 1rem 1.6rem;
text-align: center;
text-decoration: none #0d172a solid;
text-decoration-thickness: auto;
transition: all .1s cubic-bezier(.1, 0, .2, 1);
user-select: none;
-webkit-user-select: none;
touch-action: manipulation;
}


.nav-pills > li.active > a:hover, .nav-pills > li.active > a {
background-color:rgb(64, 75, 99) !important;
color: #fff;
}

/* TOC */

.list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus {
z-index: 2;
color: #ffffff;
background-color: rgb(64, 75, 99);
border-color: rgb(64, 75, 99);
}
114 changes: 114 additions & 0 deletions pr-preview/pr-542/docTemplate.R
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read_properties <- function(file = "../modules/props.yaml") {
props <- yaml::read_yaml(file)$slots
# props <- rbindlist(props, fill = TRUE, idcol = "Property")
props
}

# The range of prop `assay` is anything of class `Assay` --
# However, the json-ld does not make this so conceptually concise for props, instead listing all possible values
# In the docs, we don't want to enumerate all values and instead want to create a _link_ to a class that defines the range
# To do this, we can infer class by look up the class of the first listed enum for that prop
# The range could also be inferred to be a boolean or string/integer rather than a class
summarize_range <- function(prop_id, schema, return_labels = FALSE) {

enums <- nfportalutils::get_by_prop_from_json_schema(id = prop_id,
prop = "schema:rangeIncludes",
schema = schema,
return_labels = FALSE)

# handle how enums are presented
if(is.null(enums)) return("")
if(length(enums) < 5) return(paste(gsub("bts:", "", enums), collapse = ","))
if("bts:Yes" %in% enums) return("Y/N")

enum1 <- enums[1]

# additional lookup class
class <- nfportalutils::get_by_prop_from_json_schema(enum1,
prop = "rdfs:subClassOf",
schema = schema,
return_labels = FALSE)[[1]]
if(length(class) > 1) warning(enum1, " has multiple parent classes")
class <- sub("bts:", "", class[1]) # use first but warn
class <- paste0("#", class)
class
}

#' @param prop_id Namespaced id, e.g. "bts:tumorType"
summarize_range_linkml <- function(prop_id, props) {
prop_id <- sub("^bts:", "", prop_id)

# union ranges
if(!is.null(props[[prop_id]]$any_of)) {
paste0("#", unlist(props[[prop_id]]$any_of, use.names = F), collapse = "|")

} else {
class <- props[[prop_id]]$range
if(is.null(range)) class <- ""
paste0("#", class)
}
}


#' Generate template documentation
#'
#' Basically tries to present a template in a conventional format similar to:
#' 1. [GDC viewer](https://docs.gdc.cancer.gov/Data_Dictionary/viewer/#?view=table-definition-view&id=aligned_reads)
#' 2. [Bioschema profile](https://bioschemas.org/profiles/ComputationalWorkflow/1.0-RELEASE)
#' 3. [FAIRplus example](https://fairplus.github.io/the-fair-cookbook/content/recipes/interoperability/transcriptomics-metadata.html#assay-metadata)
#' 4. [Immport template doc](https://www.immport.org/shared/templateDocumentation?tab=1&template=bioSamples.txt)
#' 5. [LINCS template doc](https://lincsproject.org/LINCS/files//2020_exp_meta_stand/General_Proteomics.pdf)
#'
#' In general it looks like a table with one row per property and informational columns for:
#' - [x] controlled values (valid values for schematic) / range of property
#' - [ ] marginality (required vs. recommended vs. optional)
#' - [ ] cardinality (one or many values allowed)
#' - [x] notes / comments
#'
#' Currently, schematic templates allow modeling more on the simplistic side and
#' don't formally express all these, so only a few are checked.
#' Currently, the jsonld version loses some information when translated from the csv source
#' (mainly the summary Range definition corresponding to https://www.w3.org/TR/rdf-schema/#ch_range and EditorNote).
#'
#' @param templates Named vector of templates to process,
#' where names corresponds to id without prefix (currently whatever follows "bts:"),
#' and value is the real internal ID (in .ID).
#' @param schema Schema list object parsed from a schematic jsonld.
#' @param prefix Namespace prefix.
#' @param savedir Directory where template representations will be outputted.
#' @param verbose Whether to be verbose about what's going on.
docTemplate <- function(templates,
schema,
prefix = "bts:",
savedir = "templates/",
verbose = TRUE) {


for(x in names(templates)) { # e.g. x <- "GenomicsAssayTemplate"
# For template, parse DependsOn to get all props present in manifest
prop_ids <- nfportalutils::get_dependency_from_json_schema(paste0(prefix, x),
schema = schema,
return_labels = FALSE)


prop_ref <- read_properties()

sms <- Filter(function(x) x$`@id` %in% prop_ids, schema)
sms <- lapply(sms, function(x) {
list(Field = x$`sms:displayName`,
Description = if(!is.null(x$`rdfs:comment`)) x$`rdfs:comment` else " ",
Required = if(!is.null(x$`sms:required`)) sub("sms:", "", x$`sms:required`) else "?",
ValidRange = summarize_range_linkml(prop_id = x$`@id`, props = prop_ref))
})
tt <- rbindlist(sms)

# Sort to show by required, then alphabetically
tt <- tt[order(-Required, Field), ]

template_id <- templates[x]
filepath <- paste0(savedir, template_id, ".csv")
write.csv(tt, file = filepath, row.names = F)
}
}


102 changes: 102 additions & 0 deletions pr-preview/pr-542/graph.R
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library(visNetwork)

#-------------------------------------------------------------------------------#

# Reads a main extended schematic .csv file and its extended definitions
# (which are separate files named `ext_classes.csv` and `ext_relations.csv`,
# respectively)
# usage example:
# schema <- readExtSchema("NF.csv")
readExtSchema <- function(schema_csv, ext_classes_csv = "ext_classes.csv") {
schema <- read.csv(schema_csv) %>%
dplyr::select(label = Attribute, id = .ID, Root = .Root, SubOf = .SubOf)

# Extended class definitions
ext_classes <- read.csv(ext_classes_csv) %>%
select(label = Attribute, id = .ID, Root = .Root, SubOf = .SubOf)

ext_schema <- rbind(schema, ext_classes)
ext_schema
}

#' Make required node and edge data for specified cluster
#' Usage:
#' assay <- getNodesEdges(schema, "Assay", "A",
#' nodes = list(color = list(A = "plum", C = "indigo")))
#' template <- getNodesEdges(schema, "Template", "T", use_id = T,
#' nodes = list(color = list(A = "pink", C = "firebrick")))
getNodesEdges <- function(schema, cluster_root,
prefix, use_id = F,
nodes = list(color = list(A = "black", C = "black"),
font.color = list(A = "white", C = "white"))
) {
cluster <- schema %>%
dplyr::filter(Root == cluster_root)

# Namespaces for cluster ancestor vs Children
A <- paste(prefix, "A", sep = "_")
C <- paste(prefix, "C", sep = "_")
nodes <- cluster %>%
select(id, label, SubOf) %>%
mutate(group = ifelse(SubOf %in% c(cluster_root, ""), A, C),
color = ifelse(group == A, nodes$color$A, nodes$color$C),
font.color = ifelse(group == A, nodes$font.color$A, nodes$font.color$C))

if(use_id) {
nodes <- nodes %>%
mutate(label = id)
}

edges <- cluster %>%
filter(SubOf != "") %>% # Remove root from edges
select(from = id, to = SubOf)

return(list(nodes = nodes, edges = edges))
}

# Convenience wrapper to extract and combine two clusters into a graph df
# given schema and extensions
# usage example:
# c2Cluster(assay, template)
c2Cluster <- function(cluster_1, cluster_2, connect_by,
ext_relations_csv = "ext_relations.csv",
viz = list(color = "firebrick", width = 4)) {

# Configure between-cluster relations
relations <- read.csv(ext_relations_csv, header = T)
edges <- relations %>%
dplyr::filter(property == connect_by)
relations$color <- viz$color
relations$width <- viz$width

# Concatenate clusters
g_nodes <- rbind(cluster_1$nodes, cluster_2$nodes)
g_edges <- rbind(cluster_1$edges, cluster_2$edges)
g_edges$color <- "gray" # non-configurable default for now
g_edges$width <- 1 # non-configurable default for now
g_edges <- rbind(g_edges, relations[, c("from", "to", "color", "width")])
return(list(nodes = g_nodes, edges = g_edges))
}

# Generate default graph
defaultGraph <- function(graph, height = 800) {
visNetwork(graph$nodes, graph$edges, height = height) %>%
visEdges(arrows = "To") %>%
visIgraphLayout() %>%
visNodes(shape = "box",
font = list(size = 30),
shadow = list(enabled = TRUE),
physics = F) %>%
visOptions(nodesIdSelection = TRUE,
highlightNearest = list(enabled = T, hover = T)
)
}

# Simple graph function meant for checking an extracted portion of graph
basicGraph <- function(g) {
visNetwork(g$nodes, g$edges) %>%
visEdges(arrows = "To") %>%
visIgraphLayout()
}

# ------------------------------------------------------------------------------#
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