update docs to markdown

R/CITEseq.R

@@ -144,51 +144,40 @@
 #'   available datasets associated to the package.
 #' @author Dario Righelli
 #' @details CITEseq data are a combination of single cell transcriptomics and
-#'     about a hundread of cell surface proteins.
+#'   about a hundread of cell surface proteins.
+#'   Available datasets are:
+#'   * cord_blood: a dataset of single cells of cord blood as
+#'   provided in Stoeckius et al. (2017).
+#'      * scRNA_Counts - Stoeckius scRNA-seq gene count matrix
+#'      * scADT - Stoeckius antibody-derived tags (ADT) data
+#'   * peripheral_blood: a dataset of single cells of peripheral
+#'   blood as provided in Mimitou et al. (2019). We provide two different
+#'   conditions controls (CTRL) and Cutaneous T-cell Limphoma (CTCL). Just build
+#'   appropriate `modes` regex for subselecting the dataset modes.
+#'      * scRNA - Mimitou scRNA-seq gene count matrix
+#'      * scADT - Mimitou antibody-derived tags (ADT) data
+#'      * scHTO - Mimitou Hashtag Oligo (HTO) data
+#'      * TCRab - Mimitou T-cell Receptors (TCR) alpha and beta
+#'      available through the object metadata.
+#'      * TCRgd - Mimitou T-cell Receptors (TCR) gamma and delta
+#'      available through the object metadata.
 #'
-#'     Available datasets are:
-#'     \itemize{
-#'         \item{cord_blood:} a dataset of single cells of cord blood as
-#'         provided in Stoeckius et al. (2017).
-#'          \itemize{
-#'             \item{scRNA_Counts} - Stoeckius scRNA-seq gene count matrix
-#'             \item{scADT} - Stoeckius antibody-derived tags (ADT) data
-#'             }
-#'      }
-#'      \itemize{
-#'         \item{peripheral_blood:} a dataset of single cells of peripheral
-#'         blood as provided in Mimitou et al. (2019).
-#'         We provide two different conditions controls (CTRL) and
-#'         Cutaneous T-cell Limphoma (CTCL).
-#'         Just build appropriate \code{modes} regex for subselecting the
-#'         dataset modes.
-#'          \itemize{
-#'             \item{scRNA} - Mimitou scRNA-seq gene count matrix
-#'             \item{scADT} - Mimitou antibody-derived tags (ADT) data
-#'             \item{scHTO} - Mimitou Hashtag Oligo (HTO) data
-#'             \item{TCRab} - Mimitou T-cell Receptors (TCR) alpha and beta
-#'             available through the object metadata.
-#'             \item{TCRgd} - Mimitou T-cell Receptors (TCR) gamma and delta
-#'             available through the object metadata.
-#'             }
-#'      }
-#'
-#' @param DataType character(1) indicating the identifier of the dataset to
+#' @param DataType `character(1)` indicating the identifier of the dataset to
 #'     retrieve.  (default "cord_blood")
 #'
-#' @param modes character() The assay types or modes of data to obtain these
+#' @param modes `character()` The assay types or modes of data to obtain these
 #'     include scADT and scRNA-seq data by default.
 #'
-#' @param version character(1) Either version '1.0.0' depending on
+#' @param version `character(1)` Either version '1.0.0' depending on
 #'     data version required.
-#' @param dry.run logical(1) Whether to return the dataset names before actual
-#'     download (default TRUE)
-#' @param filtered logical(1) indicating if the returned dataset needs to
+#' @param dry.run `logical(1)` Whether to return the dataset names before actual
+#'     download (default `TRUE`)
+#' @param filtered `logical(1)` indicating if the returned dataset needs to
 #'     have filtered cells.
 #'     See Details for additional information about the filtering process.
 #'
-#' @param verbose logical(1) Whether to show the dataset currently being
-#'     (down)loaded (default TRUE)
+#' @param verbose `logical(1)` Whether to show the dataset currently being
+#'     (down)loaded (default `TRUE`)
 #'
 #' @param ... Additional arguments passed on to the
 #'     \link[ExperimentHub]{ExperimentHub-class} constructor

R/GTseq.R

@@ -20,45 +20,42 @@
 #' @details G&T-seq is a combination of Picoplex amplified gDNA sequencing
 #'   (genome) and SMARTSeq2 amplified cDNA sequencing (transcriptome) of the
 #'   same cell. For more information, see Macaulay et al. (2015).
-#'   \itemize{
-#'       \item{mouse_embryo_8_cell:}
-#'       this dataset was filtered for bad cells as specified in Macaulay
-#'       et al. (2015).
-#'       \itemize{
-#'           \item{genomic} - integer copy numbers as detected from scDNA-seq
-#'           \item{transcriptomic} - raw read counts as quantified from scRNA-seq
-#'       }
-#'   }
+#'     * mouse_embryo_8_cell:
+#'     this dataset was filtered for bad cells as specified in Macaulay
+#'     et al. (2015).
+#'         * genomic - integer copy numbers as detected from scDNA-seq
+#'         * transcriptomic - raw read counts as quantified from scRNA-seq
 #'
 #' @section metadata:
 #'   The `MultiAssayExperiment` metadata includes the original function call
 #'   that saves the function call and the data version requested.
 #'
-#' @param DataType character(1) Indicates study that produces this type of
+#' @param DataType `character(1)` Indicates study that produces this type of
 #'   data (default: 'mouse_embryo_8_cell')
 #'
-#' @param modes character() A wildcard / glob pattern of modes, such as
-#'   \code{"*omic"}. A wildcard of \code{"*"} will return all modes including
+#' @param modes `character()` A wildcard / glob pattern of modes, such as
+#'   `"*omic"`. A wildcard of `"*"` will return all modes including
 #'   copy numbers ("genomic") and RNA-seq read counts ("transcriptomic"),
 #'   which is the default.
 #'
-#' @param version character(1). Currently, only version '1.0.0'.
+#' @param version `character(1)` Currently, only version '1.0.0'.
 #'
-#' @param dry.run logical(1) Whether to return the dataset names before actual
-#'   download (default TRUE)
+#' @param dry.run `logical(1)` Whether to return the dataset names before actual
+#'   download (default `TRUE`)
 #'
-#' @param verbose logical(1) Whether to show the dataset currently being
-#'   (down)loaded (default TRUE)
+#' @param verbose `logical(1)` Whether to show the dataset currently being
+#'   (down)loaded (default `TRUE`)
 #'
 #' @param ... Additional arguments passed on to the
 #'   [ExperimentHub][ExperimentHub::ExperimentHub-class] constructor
 #'
 #' @seealso SingleCellMultiModal-package
 #'
-#' @return A single cell multi-modal \linkS4class{MultiAssayExperiment} or
-#'     informative `data.frame` when `dry.run` is `TRUE`
+#' @return A single cell multi-modal
+#'   [MultiAssayExperiment][MultiAssayExperiment::MultiAssayExperiment-class] or
+#'   informative `data.frame` when `dry.run` is `TRUE`
 #'
-#' @source \url{https://www.ebi.ac.uk/ena/browser/view/PRJEB9051}
+#' @source <https://www.ebi.ac.uk/ena/browser/view/PRJEB9051>
 #'
 #' @references
 #'   Macaulay et al. (2015) G&T-seq: parallel sequencing of single-cell
@@ -67,8 +64,6 @@
 #'   Macaulay et al. (2016) Separation and parallel sequencing of the genomes
 #'   and transcriptomes of single cells using G&T-seq. Nat Protoc, 11:2081–103.
 #'
-#' @md
-#'
 #' @examples
 #'
 #' GTseq()

R/SCoPE2.R

@@ -4,39 +4,35 @@
 #'   a
 #'   [`MultiAssayExperiment`][MultiAssayExperiment::MultiAssayExperiment-class]
 #'   container. The `DataType` argument provides access to the `SCoPE2` dataset
-#'   as provided by Specht et al. (2020; DOI: http://dx.doi.org/10.1101/665307).
-#'   The article provides more information about the data acquisition and
-#'   pre-processing.
+#'   as provided by Specht et al. (2020; DOI:
+#'   <http://dx.doi.org/10.1101/665307>). The article provides more information
+#'   about the data acquisition and pre-processing.
 #'
 #' @details The SCoPE2 study combines scRNA-seq (transcriptome) and
 #'   single-cell proteomics.
 #'
-#'   \itemize{
-#'       \item{macrophage_differentiation:} the cells are monocytes that undergo
+#'   * macrophage_differentiation: the cells are monocytes that undergo
 #'   macrophage differentiation. No annotation is available for the
 #'   transcriptome data, but batch and cell type annotations are
 #'   available for the proteomics data in the `celltype` `colData` column.
 #'   The transcriptomics and proteomics data were not measured from the same
 #'   cells but from a distinct set of cell cultures.
 #'   This dataset provides already filtered bad quality cells.
-#'       \itemize{
-#'           \item{scRNAseq1} - single-cell transcriptome (batch 1)
-#'           \item{scRNAseq2} - single-cell transcriptome (batch 2)
-#'           \item{scp} - single-cell proteomics
-#'       }
-#'   }
+#'       * scRNAseq1 - single-cell transcriptome (batch 1)
+#'       * scRNAseq2 - single-cell transcriptome (batch 2)
+#'       * scp - single-cell proteomics
 #'
 #' @inheritParams scNMT
 #'
-#' @param DataType character(1) Indicates study that produces this type of
+#' @param DataType `character(1)` Indicates study that produces this type of
 #'   data (default: 'macrophage_differentiation')
 #'
-#' @param modes character() A wildcard / glob pattern of modes, such as
+#' @param modes `character()` A wildcard / glob pattern of modes, such as
 #'   `"rna"`. A wildcard of `"*"` will return all modes, that are
 #'   transcriptome ("rna") or proteome ("protein") which is the
 #'   default.
 #'
-#' @param version character(1), currently only version '1.0.0' is
+#' @param version `character(1)`, currently only version '1.0.0' is
 #'   available
 #'
 #' @return A single cell multi-modal
@@ -46,7 +42,7 @@
 #' @seealso SingleCellMultiModal-package
 #'
 #' @source All files are linked from the slavovlab website
-#'     \url{https://scope2.slavovlab.net/docs/data}
+#'     <https://scope2.slavovlab.net/docs/data>
 #'
 #' @references
 #'   Specht, Harrison, Edward Emmott, Aleksandra A. Petelski, R.
@@ -55,8 +51,6 @@
 #'   Proteomic and Transcriptomic Analysis of Macrophage
 #'   Heterogeneity.” bioRxiv. https://doi.org/10.1101/665307.
 #'
-#' @md
-#'
 #' @examples
 #'
 #' SCoPE2(DataType = "macrophage_differentiation",

R/SingleCellMultiModal.R

@@ -27,13 +27,13 @@
 #'
 #' @inheritParams scNMT
 #'
-#' @param DataTypes character() A vector of data types as indicated in each
+#' @param DataTypes `character()` A vector of data types as indicated in each
 #'     individual function by the `DataType` parameter. These can be any of
 #'     the following: "mouse_gastrulation", "pbmc_10x",
 #'     "macrophage_differentiation", "cord_blood", "peripheral_blood",
 #'     "mouse_visual_cortex", "mouse_embryo_8_cell"
 #'
-#' @param versions character() A vector of versions for each DataType. By
+#' @param versions `character()` A vector of versions for each DataType. By
 #'     default, version `1.0.0` is obtained for all data types.
 #'
 #' @param modes list() A list or CharacterList of modes for each data type
@@ -47,8 +47,6 @@
 #'     a `call_map` which provides traceability of technology functions to
 #'     `DataType` prefixes, and lastly, R version information as `version`.
 #'
-#' @md
-#'
 #' @examples
 #'
 #' SingleCellMultiModal(c("mouse_gastrulation", "pbmc_10x"),

R/cache.R

@@ -14,35 +14,33 @@
 #' @section scmmCache:
 #' Get the directory location of the cache. It will prompt the user to create
 #' a cache if not already created. A specific directory can be used via
-#' \code{setCache}.
+#' `setCache`.
 #'
 #' @section setCache:
 #' Specify the directory location of the data cache. By default, it will
 #' go into the user's home and package name directory as given by
-#' \link[tools:userdir]{R_user_dir} (default: varies by system e.g., for Linux:
+#' [R_user_dir][tools::R_user_dir] (default: varies by system e.g., for Linux:
 #' '$HOME/.cache/R/SingleCellMultiModal').
 #'
 #' @section removeCache:
 #' Some files may become corrupt when downloading, this function allows
 #' the user to delete the tarball associated with a study number in the
 #' cache.
 #'
-#' @param directory character(1) The file location where the cache is located.
+#' @param directory `character(1)` The file location where the cache is located.
 #' Once set, future downloads will go to this folder. See `setCache` section
 #' for details.
 #'
 #' @param verbose Whether to print descriptive messages
 #'
-#' @param ask logical(1) (default TRUE when `interactive()`) Confirm the file
+#' @param ask `logical(1)` (default TRUE when `interactive()`) Confirm the file
 #' location of the cache directory
 #'
-#' @param accession character(1) A single string indicating the accession number
+#' @param accession `character(1)` A single string indicating the accession number
 #' of the study
 #'
 #' @param ... For `scmmCache`, arguments passed to `setCache`
 #'
-#' @md
-#'
 #' @examples
 #' getOption("scmmCache")
 #' scmmCache()

R/cellGating.R

@@ -1,14 +1,14 @@
 
 #' addCTLabels
 #'
-#' @param cd the \code{colData} \code{DataFrame}
-#' @param out list data structure returned by \code{getCellGroups}
+#' @param cd the `colData` `DataFrame`
+#' @param out list data structure returned by `getCellGroups`
 #' @param outname character indicating the name of the out data structure
-#' @param ct character indicating the celltype to assign in the \code{ctcol}
+#' @param ct character indicating the celltype to assign in the `ctcol`
 #' @param mkrcol character indicating the cd column to store the markers
-#' indicated by \code{outname} (default is markers)
+#' indicated by `outname` (default is markers)
 #' @param ctcol character indicating the column in cd to store the cell type
-#' indicated by \code{ct} (default is celltype)
+#' indicated by `ct` (default is celltype)
 #' @param overwrite logical indicating if the cell types have to be overwritten
 #' without checking if detected barcodes were already assigned to other celltypes
 #' @param verbose logical for having informative messages during the execution

R/scMultiome.R

@@ -82,30 +82,27 @@
 
 #' Single-cell Multiome ATAC + Gene Expression
 #'
-#' @description 10x Genomics Multiome technology enables simultaneous profiling 
-#' of the transcriptome (using 3’ gene expression) and epigenome 
+#' @description 10x Genomics Multiome technology enables simultaneous profiling
+#' of the transcriptome (using 3’ gene expression) and epigenome
 #' (using ATAC-seq) from single cells to
 #' deepen our understanding of how genes are expressed and regulated across
 #' different cell types. Data prepared by Ricard Argelaguet.
 #'
 #' @details Users are able to choose from either an `MTX` or `HDF5` file format
-#'     as the internal data representation. The `MTX` (Matrix Market)
-#'     format allows users to load a sparse `dgCMatrix` representation.
-#'     Choosing `HDF5` gives users a sparse `HDF5Array` class object.
-#'     \itemize{pbmc_10x:} 10K Peripheral Blood Mononuclear Cells provided by
+#'   as the internal data representation. The `MTX` (Matrix Market) format
+#'   allows users to load a sparse `dgCMatrix` representation. Choosing `HDF5`
+#'   gives users a sparse `HDF5Array` class object.
+#'     * pbmc_10x: 10K Peripheral Blood Mononuclear Cells provided by
 #' [10x Genomics website](https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets)
-#'      Cell quality control filters are available in the object `colData`
-#'      together with the `celltype` annotation labels.
-#'     
+#'     Cell quality control filters are available in the object `colData`
+#'     together with the `celltype` annotation labels.
 #'
 #' @inheritParams scNMT
 #'
-#' @param format Either MTX or HDF5 data format (default MTX)
+#' @param format `character(1)` Either MTX or HDF5 data format (default MTX)
 #'
 #' @return A 10X PBMC `MultiAssayExperiment` object
 #'
-#' @md
-#'
 #' @examples
 #'
 #' scMultiome(DataType = "pbmc_10x", modes = "*", dry.run = TRUE)