Nothing
R/slinky_sumexp.R
In slinky: Putting the fun in LINCS L1000 data analysis
#' as("Slinky", "SummarizedExperiment")
#'
#' Create SummarizedExperiment object from Slinky object. Data will be
#' loaded from the GCTX file, combined with metadata from the info file,
#' and wrapped in a SummarizedExperiment object. Note that this may take
#' a long time for the entire data set from the L1000 project. For most
#' use cases, a subset will be desired (e.g.
#' \code{as(sl[1:987,1:50], SummarizedExperiment)}. See the
#' \code{\link{loadL1K}} method for a more flexible way to create a
#' SummarizedExperiment object based on various query parameters.
#' @name coerce
#' @rdname coerce
#' @aliases coerce,Slinky,SummarizedExperiment-method
#' @exportMethod coerce
#' @examples
#'
#' # for build/demo only. You MUST use your own key when using the slinky
#' # package.
#' user_key <- httr::content(httr::GET('https://api.clue.io/temp_api_key'),
#' as='parsed')$user_key
#' sl <- Slinky(user_key,
#' system.file('extdata', 'demo.gctx',
#' package='slinky'),
#' system.file('extdata', 'demo_inst_info.txt',
#' package = 'slinky'))
#' sumex <- as(sl[, 1:20], "SummarizedExperiment")
#'
#' @seealso \code{\link{loadL1K}}
#' @importFrom SummarizedExperiment SummarizedExperiment
setAs("Slinky", "SummarizedExperiment", function(from) {
data <- readGCTX(from)
ix <- match(base::colnames(data), from@metadata$inst_id)
info <- from@metadata[ix, ]
rownames(info) <- info$inst_id
info$distil_id <- info$inst_id
# this should never happen, but just a sanity check...
if (!all.equal(base::rownames(info),
as.vector(base::colnames(data)))) {
stop("Rownames of metadata and colnames of expression data ",
"did not match.")
}
SummarizedExperiment::SummarizedExperiment(
assays = list(exprs = data),
colData = info)
})
#' loadL1K
#'
#' Convert data from gctx file to SummarizedExperiment, pulling metadata
#' from various sources. Specifying the subset of data you want can be
#' done is various ways. The simplest example is
#' with explicit subsetting
#' (e.g. \code{data <- loadL1K(sl[1:978, 1:10])}, which is equivalent to
#' \code{data <- as(sl[1:978, 1:10], "SummarizedExperiment")}). However,
#' more sophisticated data loading can be achieved, for example by
#' specifing a specific perturbagen (\code{pert}), or even an explicit
#' \code{where_clause} which will be passed directly to the clue.io API.
#' Thus, this function supports users with varying degrees of familiarity
#' with the structure and content of the L1000 metadata. All arguments
#' are optional.
#' @param x A slinky object
#' @param ids distil_ids to include in the Expression Set.
#' @param pert name (pert_iname) of perturbation for which data is desired.
#' Supercedes value for \code{pert_iname} specified in \code{where_clause}
#' (if any).
#' @param cell_line name (cell_id) of cell line for which data is desired.
#' Supercedes value for \code{cell_id} specified in \code{where_clause}
#' (if any).
#' @param type Optional type (pert_type) of perturbation for which data is desired,
#' should be one of \code{c("trt_cp", "trt_sh", "trt_oe")}
#' or NULL (default)
#' @param where_clause Rather than specifying above terms, an explicit
#' where_clause may be provided to identify the data to be loaded from
#' the gctx file. This will be passed directly the the
#' \href{https://clue.io/API#perts}{/pert} endpoint of
#' the clue.io API, and full documentation of the query options can be
#' reviewed at the above link.
#' @param inferred Should the inferred (non-landmark) genes be
#' included in the analysis? Default is TRUE. Ignored if index is
#' specified.
#' @param gold Should we limit to instances classified as "gold" by the L1000
#' project (by virtue of their low inter-replicate variability)? Default
#' is \code{TRUE}.
#' @param fields Fields to include in the expression set's
#' phenodata. Default is all available.
#' @param controls Should same-plate controls be identified and
#' included? Default is FALSE.
#' @param cl Optional cluster object to speed up data retrieval from clue.io.
#' Please use caution...a large cluster might produce requests to the
#' API at an obnoxious rate.
#' @param verbose Do you want to know how things are going?
#' Default is FALSE
#' @return Object of type
#' \code{\link{SummarizedExperiment}} containing
#' expression and meta data.
#' #' @name loadL1K
#' @examples
#'
#' # for build/demo only. You MUST use your own key when using the slinky
#' # package.
#' user_key <- httr::content(httr::GET('https://api.clue.io/temp_api_key'),
#' as='parsed')$user_key
#' sl <- Slinky(user_key,
#' system.file('extdata', 'demo.gctx',
#' package='slinky'),
#' system.file('extdata', 'demo_inst_info.txt',
#' package = 'slinky'))
#' amox_gold <- clueInstances(sl, where_clause = list("pert_type" = "trt_cp",
#' "pert_iname" = "amoxicillin",
#' "cell_id" = "MCF7",
#' "is_gold" = TRUE),
#' poscon = "omit")
#' ids.ctrl <- controls(sl, ids = amox_gold)$distil_id
#' amox_and_control <- loadL1K(sl, ids = c(amox_gold,
#' ids.ctrl))
#' str(SummarizedExperiment::assays(amox_and_control)[[1]])
#'
#' @rdname loadL1K
setGeneric("loadL1K",
function(x,
ids = character(),
pert = character(),
cell_line = character(),
type = NULL,
where_clause = character(),
gold = TRUE,
inferred = TRUE,
fields = character(),
controls = FALSE,
cl = NULL,
verbose = FALSE) {
standardGeneric("loadL1K")
}
)
#' @rdname loadL1K
#' @aliases loadL1K,Slinky-method
#' @exportMethod loadL1K
setMethod("loadL1K", signature(x = "Slinky"),
function(x,
ids = character(),
pert = character(),
cell_line = character(),
type = NULL,
where_clause = character(),
gold = FALSE,
inferred = TRUE,
fields = character(),
controls = FALSE,
cl = NULL,
verbose = FALSE)
{
if (length(ids) && length(where_clause)) {
stop("loadL1K function cannot be called with both ids and ",
"where_clause.")
}
if (!length(ids) &&
!length(pert) &&
!length(type) &&
!length(cell_line) &&
!length(where_clause) &&
!gold) {
if (verbose) message("Loading expression data...")
if (inferred) {
data <- readGCTX(x)
} else {
data <- readGCTX(x[seq_len(978),])
}
} else {
if (length(where_clause)) {
if (verbose)
message("Querying and loading expression data...")
if (length(pert)) where_clause$pert_iname <- pert
if (length(cell_line)) where_clause$cell_id <- cell_line
if (length(type)) where_clause$pert_type <- type
if (gold) where_clause$is_gold = TRUE
ids <- clueInstances(x, where_clause = where_clause)
}
coln <- colnames(x)
ix <- which(coln %in% ids)
if (!length(ix)) {
warning("Specified where_clause or ids returned no results. ",
"Please verify your query/ids.")
return(NULL)
}
if (inferred) {
data <- readGCTX(x[, ix])
} else {
data <- readGCTX(x[seq_len(978), ix])
}
if (ncol(data) != length(ids)) {
message(length(ids), " instances requested, but only ",
ncol(data), " instances present in gctx file.")
}
rm(coln)
}
if (controls) {
if (verbose) message("Loading control data...")
ids <- controls(x, ids = base::colnames(data),
verbose = verbose,
cl = cl)
ix = which(colnames(x) %in% ids$distil_id)
if (inferred) {
ctrl <- readGCTX(x[, ix])
} else {
ctrl <- readGCTX(x[seq_len(978), ix])
}
data <- cbind(data, ctrl)
}
ix <- match(base::colnames(data), metadata(x)$inst_id)
info <- metadata(x[,ix])
rownames(info) <- info$inst_id
info$distil_id <- info$inst_id
if (length(fields)) {
info <- info[, which(base::colnames(info) %in% fields)]
}
if (!all.equal(base::rownames(info),
as.vector(base::colnames(data)))) {
stop("Rownames of metadata and colnames of expression data ",
"did not match. Aborting.")
}
SummarizedExperiment::SummarizedExperiment(
assays = list(exprs = data),
colData = info)
})
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slinky documentation built on Nov. 8, 2020, 10:58 p.m.
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#' as("Slinky", "SummarizedExperiment")
#'
#' Create SummarizedExperiment object from Slinky object. Data will be
#' loaded from the GCTX file, combined with metadata from the info file,
#' and wrapped in a SummarizedExperiment object. Note that this may take
#' a long time for the entire data set from the L1000 project. For most
#' use cases, a subset will be desired (e.g.
#' \code{as(sl[1:987,1:50], SummarizedExperiment)}. See the
#' \code{\link{loadL1K}} method for a more flexible way to create a
#' SummarizedExperiment object based on various query parameters.
#' @name coerce
#' @rdname coerce
#' @aliases coerce,Slinky,SummarizedExperiment-method
#' @exportMethod coerce
#' @examples
#'
#' # for build/demo only. You MUST use your own key when using the slinky
#' # package.
#' user_key <- httr::content(httr::GET('https://api.clue.io/temp_api_key'),
#' as='parsed')$user_key
#' sl <- Slinky(user_key,
#' system.file('extdata', 'demo.gctx',
#' package='slinky'),
#' system.file('extdata', 'demo_inst_info.txt',
#' package = 'slinky'))
#' sumex <- as(sl[, 1:20], "SummarizedExperiment")
#'
#' @seealso \code{\link{loadL1K}}
#' @importFrom SummarizedExperiment SummarizedExperiment
setAs("Slinky", "SummarizedExperiment", function(from) {
data <- readGCTX(from)
ix <- match(base::colnames(data), from@metadata$inst_id)
info <- from@metadata[ix, ]
rownames(info) <- info$inst_id
info$distil_id <- info$inst_id
# this should never happen, but just a sanity check...
if (!all.equal(base::rownames(info),
as.vector(base::colnames(data)))) {
stop("Rownames of metadata and colnames of expression data ",
"did not match.")
}
SummarizedExperiment::SummarizedExperiment(
assays = list(exprs = data),
colData = info)
})
#' loadL1K
#'
#' Convert data from gctx file to SummarizedExperiment, pulling metadata
#' from various sources. Specifying the subset of data you want can be
#' done is various ways. The simplest example is
#' with explicit subsetting
#' (e.g. \code{data <- loadL1K(sl[1:978, 1:10])}, which is equivalent to
#' \code{data <- as(sl[1:978, 1:10], "SummarizedExperiment")}). However,
#' more sophisticated data loading can be achieved, for example by
#' specifing a specific perturbagen (\code{pert}), or even an explicit
#' \code{where_clause} which will be passed directly to the clue.io API.
#' Thus, this function supports users with varying degrees of familiarity
#' with the structure and content of the L1000 metadata. All arguments
#' are optional.
#' @param x A slinky object
#' @param ids distil_ids to include in the Expression Set.
#' @param pert name (pert_iname) of perturbation for which data is desired.
#' Supercedes value for \code{pert_iname} specified in \code{where_clause}
#' (if any).
#' @param cell_line name (cell_id) of cell line for which data is desired.
#' Supercedes value for \code{cell_id} specified in \code{where_clause}
#' (if any).
#' @param type Optional type (pert_type) of perturbation for which data is desired,
#' should be one of \code{c("trt_cp", "trt_sh", "trt_oe")}
#' or NULL (default)
#' @param where_clause Rather than specifying above terms, an explicit
#' where_clause may be provided to identify the data to be loaded from
#' the gctx file. This will be passed directly the the
#' \href{https://clue.io/API#perts}{/pert} endpoint of
#' the clue.io API, and full documentation of the query options can be
#' reviewed at the above link.
#' @param inferred Should the inferred (non-landmark) genes be
#' included in the analysis? Default is TRUE. Ignored if index is
#' specified.
#' @param gold Should we limit to instances classified as "gold" by the L1000
#' project (by virtue of their low inter-replicate variability)? Default
#' is \code{TRUE}.
#' @param fields Fields to include in the expression set's
#' phenodata. Default is all available.
#' @param controls Should same-plate controls be identified and
#' included? Default is FALSE.
#' @param cl Optional cluster object to speed up data retrieval from clue.io.
#' Please use caution...a large cluster might produce requests to the
#' API at an obnoxious rate.
#' @param verbose Do you want to know how things are going?
#' Default is FALSE
#' @return Object of type
#' \code{\link{SummarizedExperiment}} containing
#' expression and meta data.
#' #' @name loadL1K
#' @examples
#'
#' # for build/demo only. You MUST use your own key when using the slinky
#' # package.
#' user_key <- httr::content(httr::GET('https://api.clue.io/temp_api_key'),
#' as='parsed')$user_key
#' sl <- Slinky(user_key,
#' system.file('extdata', 'demo.gctx',
#' package='slinky'),
#' system.file('extdata', 'demo_inst_info.txt',
#' package = 'slinky'))
#' amox_gold <- clueInstances(sl, where_clause = list("pert_type" = "trt_cp",
#' "pert_iname" = "amoxicillin",
#' "cell_id" = "MCF7",
#' "is_gold" = TRUE),
#' poscon = "omit")
#' ids.ctrl <- controls(sl, ids = amox_gold)$distil_id
#' amox_and_control <- loadL1K(sl, ids = c(amox_gold,
#' ids.ctrl))
#' str(SummarizedExperiment::assays(amox_and_control)[[1]])
#'
#' @rdname loadL1K
setGeneric("loadL1K",
function(x,
ids = character(),
pert = character(),
cell_line = character(),
type = NULL,
where_clause = character(),
gold = TRUE,
inferred = TRUE,
fields = character(),
controls = FALSE,
cl = NULL,
verbose = FALSE) {
standardGeneric("loadL1K")
}
)
#' @rdname loadL1K
#' @aliases loadL1K,Slinky-method
#' @exportMethod loadL1K
setMethod("loadL1K", signature(x = "Slinky"),
function(x,
ids = character(),
pert = character(),
cell_line = character(),
type = NULL,
where_clause = character(),
gold = FALSE,
inferred = TRUE,
fields = character(),
controls = FALSE,
cl = NULL,
verbose = FALSE)
{
if (length(ids) && length(where_clause)) {
stop("loadL1K function cannot be called with both ids and ",
"where_clause.")
}
if (!length(ids) &&
!length(pert) &&
!length(type) &&
!length(cell_line) &&
!length(where_clause) &&
!gold) {
if (verbose) message("Loading expression data...")
if (inferred) {
data <- readGCTX(x)
} else {
data <- readGCTX(x[seq_len(978),])
}
} else {
if (length(where_clause)) {
if (verbose)
message("Querying and loading expression data...")
if (length(pert)) where_clause$pert_iname <- pert
if (length(cell_line)) where_clause$cell_id <- cell_line
if (length(type)) where_clause$pert_type <- type
if (gold) where_clause$is_gold = TRUE
ids <- clueInstances(x, where_clause = where_clause)
}
coln <- colnames(x)
ix <- which(coln %in% ids)
if (!length(ix)) {
warning("Specified where_clause or ids returned no results. ",
"Please verify your query/ids.")
return(NULL)
}
if (inferred) {
data <- readGCTX(x[, ix])
} else {
data <- readGCTX(x[seq_len(978), ix])
}
if (ncol(data) != length(ids)) {
message(length(ids), " instances requested, but only ",
ncol(data), " instances present in gctx file.")
}
rm(coln)
}
if (controls) {
if (verbose) message("Loading control data...")
ids <- controls(x, ids = base::colnames(data),
verbose = verbose,
cl = cl)
ix = which(colnames(x) %in% ids$distil_id)
if (inferred) {
ctrl <- readGCTX(x[, ix])
} else {
ctrl <- readGCTX(x[seq_len(978), ix])
}
data <- cbind(data, ctrl)
}
ix <- match(base::colnames(data), metadata(x)$inst_id)
info <- metadata(x[,ix])
rownames(info) <- info$inst_id
info$distil_id <- info$inst_id
if (length(fields)) {
info <- info[, which(base::colnames(info) %in% fields)]
}
if (!all.equal(base::rownames(info),
as.vector(base::colnames(data)))) {
stop("Rownames of metadata and colnames of expression data ",
"did not match. Aborting.")
}
SummarizedExperiment::SummarizedExperiment(
assays = list(exprs = data),
colData = info)
})
Try the slinky package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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