docs/2multi.md

In SGDDNB/ShinyCell: Shiny Interactive Web Apps for Single-Cell Data

title: | | Tutorial for creating a ShinyCell app containing several single-cell datasets author: | | John F. Ouyang date: "Feb 2021" output: pdf_document: default html_document: toc: true toc_depth: 2 toc_float: collapsed: false fontsize: 12pt pagetitle: "2multi"

Users might want to include multiple single-cell datasets into a single Shiny app and ShinyCell provides this functionality. We will demonstrate how to create a shiny app containing two single-cell datasets. A live version of the shiny app generated here can be found at shinycell2.ddnetbio.com.

To further change the aesthetics and ordering of metadata, please refer to the Tutorial for changing ShinyCell aesthetics and other settings.

Load data

For the first example dataset, we will use scRNA-seq data (Seurat object) containing intermediates collected during reprogramming of human fibroblast into induced pluripotent stem cells using the RSeT media condition, which can be downloaded here. For the second example dataset, we will use scRNA-seq of day 21 reprogramming intermediates from the same publication, which can be downloaded here. After downloading the data, we will begin by loading the required libraries.

library(Seurat)
library(ShinyCell)
getExampleData("multi")      # Download multiple example datasets (~400 MB)

Create ShinyCell configuration and configure settings for dataset 1

To create a multi-dataset Shiny app, we need to configure the settings for each dataset separately. We will do so for the first dataset as follows. A ShinyCell configuration scConf1 is created, followed by modifying various aspects of the Shiny app e.g. removing excessive metadata, modifying the display names of metadata and modifying the colour palettes. For a more detailed explanation on how to customise the shiny app, refer to Tutorial for customising ShinyCell aesthetics and other settings. We then run makeShinyFiles() to generate the files related to the first dataset. Notice that we specified shiny.prefix = "sc1" and this prefix is used to identify that the files containing single-cell data related to the first dataset. The remaining arguments are the same as explained in the Tutorial for customising ShinyCell aesthetics and other settings.

seu <- readRDS("readySeu_rset.rds")
scConf1 = createConfig(seu)
scConf1 = delMeta(scConf1, c("orig.ident", "RNA_snn_res.0.5"))
scConf1 = modMetaName(scConf1, meta.to.mod = c("nUMI", "nGene", "pctMT", "pctHK"), 
                      new.name = c("No. UMIs", "No. detected genes",
                                   "% MT genes", "% HK genes"))
scConf1 = modColours(scConf1, meta.to.mod = "library", 
                     new.colours= c("black", "darkorange", "blue", "pink2"))
makeShinyFiles(seu, scConf1, gex.assay = "RNA", gex.slot = "data",
               gene.mapping = TRUE, shiny.prefix = "sc1",
               shiny.dir = "shinyAppMulti/",
               default.gene1 = "NANOG", default.gene2 = "DNMT3L",
               default.multigene = c("ANPEP","NANOG","ZIC2","NLGN4X","DNMT3L",
                                     "DPPA5","SLC7A2","GATA3","KRT19"),
               default.dimred = c("UMAP_1", "UMAP_2"))

Create ShinyCell configuration and configure settings for dataset 2

We then repeat the same procedure for the second dataset to generate the files required for the Shiny app. Notice that we used a different prefix here shiny.prefix = "sc2".

seu <- readRDS("readySeu_d21i.rds")
scConf2 = createConfig(seu)
scConf2 = delMeta(scConf2, c("orig.ident", "RNA_snn_res.0.5"))
scConf2 = modMetaName(scConf2, meta.to.mod = c("nUMI", "nGene", "pctMT", "pctHK"), 
                      new.name = c("No. UMIs", "No. detected genes",
                                   "% MT genes", "% HK genes"))
scConf2 = modColours(scConf2, meta.to.mod = "library", 
                     new.colours= c("black", "blue", "purple"))
makeShinyFiles(seu, scConf2, gex.assay = "RNA", gex.slot = "data",
               gene.mapping = TRUE, shiny.prefix = "sc2",
               shiny.dir = "shinyAppMulti/",
               default.gene1 = "GATA3", default.gene2 = "DNMT3L",
               default.multigene = c("ANPEP","NANOG","ZIC2","NLGN4X","DNMT3L",
                                     "DPPA5","SLC7A2","GATA3","KRT19"),
               default.dimred = c("UMAP_1", "UMAP_2"))

Generate code for Shiny app

We can the proceed to the final part where we generate the code for the Shiny app using the makeShinyCodesMulti() function. To specify that two datasets will be included in this Shiny app, we input the prefixes of the two datasets shiny.prefix = c("sc1", "sc2"). Also, users need to specify section headers for each dataset via the shiny.headers argument. The remaining arguments are the same as explained in the Tutorial for changing ShinyCell aesthetics and other settings.

citation = list(
  author  = "Liu X., Ouyang J.F., Rossello F.J. et al.",
  title   = "",
  journal = "Nature",
  volume  = "586",
  page    = "101-107",
  year    = "2020", 
  doi     = "10.1038/s41586-020-2734-6",
  link    = "https://www.nature.com/articles/s41586-020-2734-6")
makeShinyCodesMulti(
  shiny.title = "Multi-dataset Tutorial", shiny.footnotes = citation,
  shiny.prefix = c("sc1", "sc2"),
  shiny.headers = c("RSeT reprogramming", "Day 21 intermediates"), 
  shiny.dir = "shinyAppMulti/") 

Now, we have both the data and code for the Shiny app and we can run the Shiny app. Each dataset can be found in their corresponding tabs and clicking on the tab creates a dropdown to change the type of plot to display on the Shiny app. This tutorial can be easily expanded to include three or more datasets. Users simply have to create the corresponding data files for each dataset and finally generate the code for the Shiny app. A live version of the shiny app can be found at shinycell2.ddnetbio.com.

SGDDNB/ShinyCell documentation built on Jan. 25, 2024, 3:19 p.m.