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Pulled over a lot of the wrappers from scRNAseq.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,38 @@ | ||
| #' List available references | ||
| #' | ||
| #' List the available reference datasets and the associated versions in \pkg{celldex}. | ||
| #' | ||
| #' @param name String containing the name of the reference dataset. | ||
| #' | ||
| #' @return | ||
| #' For \code{listReferences}, a character vector containing the names of the available references. | ||
| #' | ||
| #' For \code{listVersions}, a character vector containing the names of the available versions of the \code{name} reference. | ||
| #' | ||
| #' For \code{fetchLatestVersion}, a string containing the name of the latest version. | ||
| #' | ||
| #' @author Aaron Lun | ||
| #' | ||
| #' @examples | ||
| #' listReferences() | ||
| #' listVersions("immgen") | ||
| #' fetchLatestVersion("immgen") | ||
| #' | ||
| #' @export | ||
| #' @importFrom gypsum listAssets | ||
| listReferences <- function() { | ||
| listAssets("celldex") | ||
| } | ||
|
|
||
| #' @export | ||
| #' @rdname listVersions | ||
| listVersions <- function(name) { | ||
| gypsum::listVersions("celldex", name) | ||
| } | ||
|
|
||
| #' @export | ||
| #' @rdname listVersions | ||
| #' @importFrom gypsum fetchLatest | ||
| fetchLatestVersion <- function(name) { | ||
| fetchLatest("celldex", name) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| #' Search reference metadata | ||
| #' | ||
| #' Search for reference datasets of interest based on matching text in the associated metadata. | ||
| #' | ||
| #' @param query String or a \code{gypsum.search.object}, see Examples. | ||
| #' @inheritParams surveyReferences | ||
| #' | ||
| #' @return | ||
| #' A \linkS4class{DataFrame} where each row corresponds to a dataset, containing various columns of metadata. | ||
| #' Some columns may be lists to capture 1:many mappings. | ||
| #' | ||
| #' @details | ||
| #' The returned DataFrame contains the usual suspects like the title and description for each dataset, | ||
| #' the number of rows and columns, the organisms and genome builds involved, | ||
| #' whether the dataset has any pre-computed reduced dimensions, and so on. | ||
| #' More details can be found in the Bioconductor metadata schema at \url{https://github.com/ArtifactDB/bioconductor-metadata-index}. | ||
| #' | ||
| #' @author Aaron Lun | ||
| #' | ||
| #' @examples | ||
| #' searchReferences(defineTextQuery("immun%", partial=TRUE))[,c("name", "title")] | ||
| #' searchReferences(defineTextQuery("10090", field="taxonomy_id"))[,c("name", "title")] | ||
| #' searchReferences( | ||
| #' defineTextQuery("10090", field="taxonomy_id") & | ||
| #' defineTextQuery("immun%", partial=TRUE) | ||
| #' )[,c("name", "title")] | ||
| #' | ||
| #' @seealso | ||
| #' \code{\link{surveyReferences}}, to easily obtain a listing of all available datasets. | ||
| #' @export | ||
| #' @importFrom S4Vectors DataFrame | ||
| #' @importFrom gypsum cacheDirectory fetchMetadataDatabase searchMetadataTextFilter | ||
| #' @importFrom DBI dbConnect dbDisconnect dbGetQuery | ||
| #' @importFrom RSQLite SQLite | ||
| searchReferences <- function(query, cache=cacheDirectory(), overwrite=FALSE, latest=TRUE) { | ||
| filter <- searchMetadataTextFilter(query) | ||
|
|
||
| bpath <- fetchMetadataDatabase(cache=cache, overwrite=overwrite) | ||
| con <- dbConnect(SQLite(), bpath) | ||
| on.exit(dbDisconnect(con)) | ||
|
|
||
| stmt <- "SELECT json_extract(metadata, '$') AS meta, versions.asset AS asset, versions.version AS version, path"; | ||
| if (!latest) { | ||
| stmt <- paste0(stmt, ", versions.latest AS latest") | ||
| } | ||
| stmt <- paste0(stmt, " FROM paths LEFT JOIN versions ON paths.vid = versions.vid WHERE versions.project = 'scRNAseq'") | ||
| if (latest) { | ||
| stmt <- paste0(stmt, " AND versions.latest = 1") | ||
| } | ||
| if (!is.null(filter)) { | ||
| stmt <- paste0(stmt, " AND ", filter$where) | ||
| everything <- dbGetQuery(con, stmt, params=filter$parameters) | ||
| } else { | ||
| everything <- dbGetQuery(con, stmt) | ||
| } | ||
|
|
||
| sanitize_query_to_output(everything, latest) | ||
| } | ||
|
|
||
| #' @export | ||
| gypsum::defineTextQuery |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,112 @@ | ||
| #' Survey reference metadata | ||
| #' | ||
| #' Metadata survey for all available reference datasets in the \pkg{celldex} package. | ||
| #' | ||
| #' @param cache,overwrite Arguments to pass to \code{\link{fetchMetadataDatabase}}. | ||
| #' @param latest Whether to only consider the latest version of each dataset. | ||
| #' | ||
| #' @return | ||
| #' A \linkS4class{DataFrame} where each row corresponds to a dataset, containing various columns of metadata. | ||
| #' Some columns may be lists to capture 1:many mappings. | ||
| #' | ||
| #' @details | ||
| #' The returned DataFrame contains the usual suspects like the title and description for each dataset, | ||
| #' the number of samples and types of labels, the organisms and genome builds involved, and so on. | ||
| #' More details can be found in the Bioconductor metadata schema at \url{https://github.com/ArtifactDB/bioconductor-metadata-index}. | ||
| #' | ||
| #' @author Aaron Lun | ||
| #' | ||
| #' @examples | ||
| #' surveyReferences() | ||
| #' | ||
| #' @seealso | ||
| #' \code{\link{searchReferences}}, to search on the metadata for specific datasets. | ||
| #' | ||
| #' @export | ||
| #' @importFrom S4Vectors DataFrame | ||
| #' @importFrom gypsum cacheDirectory fetchMetadataDatabase | ||
| #' @importFrom DBI dbConnect dbDisconnect dbGetQuery | ||
| #' @importFrom RSQLite SQLite | ||
| surveyReferences <- function(cache=cacheDirectory(), overwrite=FALSE, latest=TRUE) { | ||
| bpath <- fetchMetadataDatabase(cache=cache, overwrite=overwrite) | ||
| con <- dbConnect(SQLite(), bpath) | ||
| on.exit(dbDisconnect(con)) | ||
|
|
||
| stmt <- "SELECT json_extract(metadata, '$') AS meta, versions.asset AS asset, versions.version AS version, path"; | ||
| if (!latest) { | ||
| stmt <- paste0(stmt, ", versions.latest AS latest") | ||
| } | ||
| stmt <- paste0(stmt, " FROM paths LEFT JOIN versions ON paths.vid = versions.vid WHERE versions.project = 'celldex'") | ||
| if (latest) { | ||
| stmt <- paste0(stmt, " AND versions.latest = 1") | ||
| } | ||
| everything <- dbGetQuery(con, stmt) | ||
|
|
||
| sanitize_query_to_output(everything, latest) | ||
| } | ||
|
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| #' @importFrom S4Vectors DataFrame | ||
| #' @importFrom jsonlite fromJSON | ||
| sanitize_query_to_output <- function(results, latest, meta.name="meta") { | ||
| path <- results$path | ||
| has.slash <- grepl("/", path) | ||
| path[!has.slash] <- NA_character_ | ||
| path[has.slash] <- sub("/[^/]+$", "", path[has.slash]) | ||
| df <- DataFrame(name = results$asset, version = results$version, path = path) | ||
| if (!latest) { | ||
| df$latest <- results$latest == 1 | ||
| } | ||
|
|
||
| all_meta <- lapply(results[[meta.name]], fromJSON, simplifyVector=FALSE) | ||
| df$title <- extract_atomic_from_json(all_meta, function(x) x$title, "character") | ||
| df$description <- extract_atomic_from_json(all_meta, function(x) x$title, "character") | ||
| df$taxonomy_id <- extract_charlist_from_json(all_meta, function(x) x$taxonomy_id) | ||
| df$genome <- extract_charlist_from_json(all_meta, function(x) x$genome) | ||
|
|
||
| df$samples <- extract_atomic_from_json(all_meta, function(x) x$applications$takane$summarized_experiment$columns, "integer") | ||
| df$labels <- extract_charlist_from_json(all_meta, function(x) x$applications$takane$summarized_experiment$column_annotations) | ||
|
|
||
| df$bioconductor_version < extract_atomic_from_json(all_meta, function(x) x$bioconductor_version, "character") | ||
| df$maintainer_name < extract_atomic_from_json(all_meta, function(x) x$maintainer_name, "character") | ||
| df$maintainer_email < extract_atomic_from_json(all_meta, function(x) x$maintainer_email, "character") | ||
|
|
||
| sources <- vector("list", length(all_meta)) | ||
| for (i in seq_along(all_meta)) { | ||
| cursources <- all_meta[[i]]$sources | ||
| if (is.null(cursources)) { | ||
| sources[[i]] <- DataFrame(provider=character(0), id=character(0), version=character(0)) | ||
| } else { | ||
| sources[[i]] <- DataFrame( | ||
| provider = extract_atomic_from_json(cursources, function(x) x$provider, "character"), | ||
| id = extract_atomic_from_json(cursources, function(x) x$id, "character"), | ||
| version = extract_atomic_from_json(cursources, function(x) x$version, "character") | ||
| ) | ||
| } | ||
| } | ||
| df$sources <- as(sources, "CompressedList") | ||
|
|
||
| df | ||
| } | ||
|
|
||
| extract_atomic_from_json <- function(metadata, extract, type) { | ||
| vapply(metadata, function(y) { | ||
| x <- extract(y) | ||
| if (is.null(x)) { | ||
| as(NA, type) | ||
| } else { | ||
| x | ||
| } | ||
| }, vector(type, 1)) | ||
| } | ||
|
|
||
| extract_charlist_from_json <- function(metadata, extract) { | ||
| output <- lapply(metadata, function(y) { | ||
| x <- extract(y) | ||
| if (is.null(y)) { | ||
| character(0) | ||
| } else { | ||
| x | ||
| } | ||
| }) | ||
| as(output, "CompressedList") | ||
| } |
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