diff --git a/NAMESPACE b/NAMESPACE index 88148a3..481c133 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,7 +1,9 @@ # Generated by roxygen2: do not edit by hand export(glue_initials) -export(iSEEfier) +export(iSEEconfigviewer) +export(iSEEinit) +export(iSEEnetworkviewer) export(iSEEnrich) export(view_initial_network) export(view_initial_tiles) diff --git a/R/iSEEfier.R b/R/iSEEfier.R index 1668a04..97787cc 100644 --- a/R/iSEEfier.R +++ b/R/iSEEfier.R @@ -1,6 +1,6 @@ -#' iSEEfier: Create an initial state of an iSEE instance for gene expression visualization +#' iSEEinit: Create an initial state of an iSEE instance for gene expression visualization #' -#' `iSEEfier()` defines the initial setup of an iSEE instance, recommending tailored visual elements to effortlessly illustrate the expression of a gene list in a single view. +#' `iSEEinit()` defines the initial setup of an iSEE instance, recommending tailored visual elements to effortlessly illustrate the expression of a gene list in a single view. #' #' @param sce SingleCellExperiment object #' @param feature.list A character vector containing a list of genes @@ -9,7 +9,7 @@ #' @param groups A character string of the groups/conditions...(as it appears in the colData of the sce) #' #' @return A list of "Panel" objects specifying the initial state of iSEE instance -#' @export iSEEfier +#' @export #' @importFrom methods new #' @importFrom SummarizedExperiment colData #' @importClassesFrom iSEE ColumnDataPlot @@ -28,8 +28,8 @@ #' gene_list <- c("ENSMUSG00000026581", "ENSMUSG00000005087", "ENSMUSG00000015437") #' cluster <- "stimulus" #' group <- "single cell quality" -#' initial <- iSEEfier(sce = sce, feature.list = gene_list, clusters = cluster, groups = group) -iSEEfier <- function(sce, +#' initial <- iSEEinit(sce = sce, feature.list = gene_list, clusters = cluster, groups = group) +iSEEinit <- function(sce, feature.list, reddim.type = "TSNE", clusters = colnames(colData(sce))[1], diff --git a/man/iSEEfier-pkg.Rd b/man/iSEEfier-pkg.Rd index 8120c52..cc246c9 100644 --- a/man/iSEEfier-pkg.Rd +++ b/man/iSEEfier-pkg.Rd @@ -2,6 +2,7 @@ % Please edit documentation in R/iSEEfier-pkg.R \docType{package} \name{iSEEfier-pkg} +\alias{iSEEfier} \alias{iSEEfier-package} \alias{iSEEfier-pkg} \title{iSEEfier: a very convenient way to fire up your iSEE instance} diff --git a/man/iSEEfier.Rd b/man/iSEEinit.Rd similarity index 83% rename from man/iSEEfier.Rd rename to man/iSEEinit.Rd index 315a902..927b87e 100644 --- a/man/iSEEfier.Rd +++ b/man/iSEEinit.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/iSEEfier.R -\name{iSEEfier} -\alias{iSEEfier} -\title{iSEEfier: Create an initial state of an iSEE instance for gene expression visualization} +\name{iSEEinit} +\alias{iSEEinit} +\title{iSEEinit: Create an initial state of an iSEE instance for gene expression visualization} \usage{ -iSEEfier( +iSEEinit( sce, feature.list, reddim.type = "TSNE", @@ -27,7 +27,7 @@ iSEEfier( A list of "Panel" objects specifying the initial state of iSEE instance } \description{ -\code{iSEEfier()} defines the initial setup of an iSEE instance, recommending tailored visual elements to effortlessly illustrate the expression of a gene list in a single view. +\code{iSEEinit()} defines the initial setup of an iSEE instance, recommending tailored visual elements to effortlessly illustrate the expression of a gene list in a single view. } \examples{ sce <- scRNAseq::RichardTCellData() @@ -37,5 +37,5 @@ sce <- scater::runTSNE(sce) gene_list <- c("ENSMUSG00000026581", "ENSMUSG00000005087", "ENSMUSG00000015437") cluster <- "stimulus" group <- "single cell quality" -initial <- iSEEfier(sce = sce, feature.list = gene_list, clusters = cluster, groups = group) +initial <- iSEEinit(sce = sce, feature.list = gene_list, clusters = cluster, groups = group) } diff --git a/tests/testthat/test_iSEEfier.R b/tests/testthat/test_iSEEfier.R index 5ddbcb4..b121c4c 100644 --- a/tests/testthat/test_iSEEfier.R +++ b/tests/testthat/test_iSEEfier.R @@ -1,9 +1,9 @@ -test_that("test iSEEfier",{ +test_that("test iSEEinit",{ sce <- scRNAseq::ReprocessedAllenData(assays = "tophat_counts") sce <- scuttle::logNormCounts(sce, exprs_values="tophat_counts") sce <- scater::runPCA(sce) sce <- scater::runTSNE(sce) - initial <- iSEEfier(sce = sce, + initial <- iSEEinit(sce = sce, feature.list = c("IL2rb", "Klre1"), clusters = "Primary.Type", diff --git a/vignettes/iSEEfier_userguide.Rmd b/vignettes/iSEEfier_userguide.Rmd index b3335ba..5f4afb7 100644 --- a/vignettes/iSEEfier_userguide.Rmd +++ b/vignettes/iSEEfier_userguide.Rmd @@ -1,6 +1,17 @@ --- title: > The `iSEEfier` User's Guide +author: +- name: Najla Abassi + affiliation: + - Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Mainz + email: najla.abassi@uni-mainz.de +- name: Federico Marini + affiliation: + - Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Mainz + - Research Center for Immunotherapy (FZI), Mainz + email: marinif@uni-mainz.de +date: "`r BiocStyle::doc_date()`" output: BiocStyle::html_document: toc: true @@ -9,6 +20,7 @@ vignette: > %\VignetteIndexEntry{iSEEfier_userguide} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} + %\VignettePackage{iSEEfier} --- ```{r, include = FALSE} @@ -48,12 +60,12 @@ Once installed, the package can be loaded and attached to the current workspace library("iSEEfier") ``` -# Create an initial state for gene expression visualization using `iSEEfier()` +# Create an initial state for gene expression visualization using `iSEEinit()` When we have all input elements ready, we can create an iSEE initial state by running: ```{r runfunc, eval=FALSE} -iSEEfier(sce = sce_obj, +iSEEinit(sce = sce_obj, feature.list = feature_list, reddim.type = reduced_dim, clusters = cluster, @@ -123,33 +135,33 @@ cluster <- "label" #the name should match what's in the colData names group <- "strain" #the name should match what's in the colData names ``` -At this point, all the elements are ready to be transferred into `iSEEfier()` +At this point, all the elements are ready to be transferred into `iSEEinit()` ```{r initial1} -initial1 <- iSEEfier(sce = sce, +initial1 <- iSEEinit(sce = sce, feature.list = gene_list, clusters = cluster, groups = group) ``` -Now we are one step away from visualizing our list of genes of interest. All that's left to do is to run `iSEE` with the initial state created with `iSEEfier` +Now we are one step away from visualizing our list of genes of interest. All that's left to do is to run `iSEE` with the initial state created with `iSEEinit` ```{r iSEEviz1, eval=FALSE} library("iSEE") iSEE(sce, initial= initial1) ``` -This instance, generated with `iSEEfier()`, returns a combination of panels, linked to each other, with the goal of visualizing the expression of certain marker genes in each cell population/group: +This instance, generated with `iSEEinit()`, returns a combination of panels, linked to each other, with the goal of visualizing the expression of certain marker genes in each cell population/group: - A `ReducedDimensionPlot`, `FeatureAssayPlot` and `RowDataTable` for each single gene. -- A `ComplexHeatmapPlot` +- A `ComplexHeatmapPlot` with all genes in `feature.list` -- `DynamicMarkerTable` +- A `DynamicMarkerTable` that identifies marker genes from a sample selection. -- `ColumnDataPlot` +- A `ColumnDataPlot` -- `MarkdownBoard``` +- A `MarkdownBoard` # Create an initial state for feature sets exploration using `iSEEnrich()` @@ -189,7 +201,7 @@ iSEE(results$sce1, initial = results$initial) Previously, we successfully generated two distinct initial configurations for iSEE. However, understanding the expected content of our iSEE instances is not always straightforward. That's when we can use `iSEEconfigviewer()`. -We only need as an input the initial configuration to obtain a graphical visualization of the expected the corresponding iSEE insantance: +We only need as an input the initial configuration to obtain a graphical visualization of the expected the corresponding iSEE instance: ```{r panelgraph} library(ggplot2)