New CiteSeq Tool Tutorial--DRAFT-WIP

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faqs/galaxy/interactive_tools_rstudio_gx_get.md

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+---
+title: Use gx_get
+area: interactive tools
+box_type: hands_on
+layout: faq
+contributors: [Camila-goclowski]
+---
+
+><tip-title>How to import datasets from Galaxy to RStudio</tip-title>
+>
+> RStudio in Galaxy comes with a `gx_get()` function. This is critical for moving datasets from your Galaxy history into Galaxy RStudio. The function outputs the file path from which you access your data via RStudio.
+>
+> To use it,  use the numbered position of the dataset you are looking to import. For example:
+>
+> If we want to find the first dataset in your history, run the following command:
+> ```r
+> gx_get(1)
+> ```
+>The result of this command will be a file path to the first dataset in your Galaxy history. Use that file path for future importing commands.
+>
+{: .tip}

topics/admin/tutorials/ansible-galaxy/tutorial.md

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topics/single-cell/tutorials/scCiteSeq-RStudio/faqs/index.md

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+---
+layout: faq-page
+---

topics/single-cell/tutorials/scCiteSeq-RStudio/preamble.md

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+# Introduction
+> <comment-title></comment-title>
+> This tutorial is significantly based on the Seurat documentation({% cite Satija2015 %}) as well as Seurat's vignette entitled [Using Seurat with multimodal data](https://satijalab.org/seurat/articles/multimodal_vignette).
+{: .comment}
+
+> <agenda-title> Getting to RStudio</agenda-title>
+>
+> In this tutorial, we will cover:
+>
+> 1. TOC
+> {:toc}
+> [Or skip ahead to analyses in RStudio](#skipahead)
+{: .agenda}
+
+# Cite-Seq Overview
+Multiomic analyses are a new and exciting way to understand the world of biology through bioinformatics! Cite-Seq ({% cite Satija&Smibert2017 %}) is one example of such multimodal technologies. Cite-Seq enables us to measure single cell transcriptomes and cell surface proteins simultaneously. Transcriptomic measurements are achieved via RNA sequencing techniques and the surface protein abundance measurements are quantified via DNA barcoded antibodies. As of current, Cite-Seq boasts its ability to tag up to 125 surface proteins at a time! 
+
+Seurat has kept up to date with the capacities of multimodal technologies such as Cite-Seq, which means once you've familiarized yourself with Seurat, you can seamlessly continue to use the package to analyze and explore many other types multimodal single-cell datasets. 
+
+><comment-title></comment-title>
+>Check out [Filter, Plot, and Explore with Seurat]({% link topics/single-cell/tutorials/scrna-case_FilterPlotandExploreRStudio %}) to start doing so in RStudio with an scRNA-seq dataset!
+{: .comment}
+
+Before we can start exploring, we'll process our transcriptomic and surface protein measurements into a Seurat object. The hardworking Galaxy programmers have kindly optimized the Seurat tool to include Cite-Seq functionality. This enables us to input our raw csv files and the tool will output Seurat objects, which are much easy to explore! 
+
+><comment-title></comment-title>
+>If you're interested in what the Seurat tool is doing behind the scenes, check out Seurat's [Using Seurat with multimodal data](https://satijalab.org/seurat/articles/multimodal_vignette) vignette. The first section of the tutorial is what the Cite-Seq enabled Seurat tool accomplishes for us. Ending with the output of a Seurat object which we can then further explore in RStudio.
+{: .comment}
+
+
+# Get Your Data
+For this tutorial, we'll use a publicly available dataset of 8,617 cord blood mononuclear cells (CBMCs) which have been sequenced for transcriptomic measurements as well as 11 surface proteins ({% cite Satija&Smibert2017 %}). 
+
+><comment-title></comment-title>
+>A quick note on nomenclature when working with Cite-Seq.
+>Antibody derived tag (ADT) refers to the cell surface protein abundance measurements
+>meanwhile RNA: represents the transcriptomic measurements
+{: .comment}
+
+First on the to-do list is importing our csv files. You can do this in a couple of ways: 
+
+
+
+## Option 1: Uploading Data via Link  
+
+> <hands-on-title></hands-on-title>
+>
+> 1. Create a new history for this tutorial
+> 2. Import the csv files object from NCBI:
+
+>  To retrive the ADT file: 
+>    ```
+>    ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE100nnn/GSE100866/suppl/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz
+>    ```
+>  To retrieve the RNA file: 
+>    ```
+>    ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE100nnn/GSE100866/suppl/GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz
+>    ```
+>    {% snippet faqs/galaxy/datasets_import_via_link.md %}
+{: .hands_on}
+
+
+## Option 2: Import a History 
+You can access [this history](https://usegalaxy.eu/u/camila-goclowski/h/citeseqseurattooltutorial) by clicking on the link provided.
+
+{% snippet faqs/galaxy/histories_import.md %}
+
+# Cite-Seq Enabled Seurat Tool 
+
+Now we'll run those csv files through the updated Seurat tool with the following parameters:
+> <hands-on-title></hands-on-title>
+> Run {% tool [Seurat](toolshed.g2.bx.psu.edu/repos/iuc/seurat/seurat/4.3.0.1+galaxy1) %} with the following parameters:
+> - *"Which Seuray method should be run"*: `Cite-seq`
+> - *"RNA counts file"*: `1: GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz`
+> - *"Protein counts file"*: `2: GSE100866_CBMC_8K_10X-ADT_umi.csv.gz`
+> - *"Minimum cells"*: `5`
+> - *"Minimum genes"*: `10`
+> - *"Low threshold for filtering cells"*: `1500`
+> - *"High threshold for filtering cells"*: `30000`
+> - *"Include violin plot and scatter plot of cell features"*: `No`
+>  - *"Output seurat object after data normalization"*: `No`
+>  - *"Include plot of variable features"*: `No`
+>  - *"Output seurat object after data scaling"*: `No`
+>  - *"Number of PCs to use in plots"*: `15`
+>  - *"Include PCA plots"*: `No`
+>  - *"Output seurat object after PCA analysis"*: `No`
+>  - *"Perplexity parameter"*: ``
+>  - *"Resolution parameter"*: `0.8`
+>  - *"Include UMAP and TSNE plots"*: `Yes`
+>  - *"Output seurat object after TSNE and UMAP analysis"*: `No`
+>  - *"Minimum percent cells"*: `0.1`
+>  - *"Log fold change threshold"*: `0.25`
+>  - *"Include heatmaps of markers"*: `Yes`
+>  - *"Output marker data"*: `Yes`
+>  - *"Output list of cite-seq markers"*: `Yes`
+>  - *"Compare specific feature's effect on protein and rna expression?"*: `No`
+>  - *"Compare top RNA and protein features graphicaly against themselves and one another"*: `No`
+{: .hands_on}
+
+><comment-title></comment-title>
+>Note that the parameters listed above are just one way you may use this super useful, one step tool. Feel free to play around with different parameters to see how it affects the data! 
+>
+>If you're hoping to follow this tutorial step by step, word for word, be aware that changing any of the above parameters may change the data you get to explore shortly in RStudio. 
+{: .comment}
+
+# Moving to RStudio
+Now that we have some explorable data (a Seurat object) in our Galaxy history, let's move into RStudio and keep investigating: 
+> <hands-on-title>Open RStudio in Galaxy</hands-on-title>
+> Run {% tool [RStudio](interactive_tool_rstudio)%}
+{: .hands_on}
+
+><comment-title>Next Step</comment-title>
+> The interactive RStudio tool should begin to load now. Make your way over to your Active Interactive Tools page (User (in the top bar of the usegalaxy page) > Active Interactive Tools > RStudio)
+> ![Interactive Tools Button](../../images/scCiteSeq-RStudio/Plot12.png "Interactive Tools")
+>
+>Alternatively, you can use the view (eye) icon in your Galaxy History to open the interactive RStudio environment.
+> ![Eye Button](../../images/scCiteSeq-RStudio/Plot13.png "Eye Button")
+{: .comment}
+
+It may be a good time to explore some of these output files that are now in your history. Take a look at some of the previews and see if you can get a grasp of what's what. If not, no worries at all, we'll start looking more closely once we've made it into RStudio!

topics/single-cell/tutorials/scCiteSeq-RStudio/tutorial.bib

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+# This is the bibliography file for your tutorial.
+#
+# To add bibliography (bibtex) entries here, follow these steps:
+#  1) Find the DOI for the article you want to cite
+#  2) Go to https://doi2bib.org and fill in the DOI
+#  3) Copy the resulting bibtex entry into this file
+#
+# To cite the example below, in your tutorial.md file
+# use {% cite Batut2018 %}
+#
+# If you want to cite an online resourse (website etc)
+# you can use the 'online' format (see below)
+#
+# You can remove the examples below
+
+@article{Satija2015,
+  doi = {10.1038/nbt.3192},
+  url = {https://doi.org/10.1038/nbt.3192},
+  year = {2015},
+  month = May,
+  publisher = {Nature Anerica Inc. All rights reserved},
+  volume = {33},
+  number = {5},
+  author = {Rahul Satija and Jeffrey A. Farrell and David Gennert and Alexander F Schier and Aviv Regev},
+  title = {Spatial reconstruction of single-cell gene expression data},
+  journal = {Nature Biotechnology}
+}
+
+
+@article{Satija&Smibert2017, 
+  doi = {10.1038/NMETH.4380},
+  url = {https://doi.org/10.1038/nmeth.4380}, 
+  year = {2017},
+  month = {July},
+
+  publisher = {Nature America Inc.},
+  volume = {14},
+  number = {9},
+  author = {Marlon Stoeckius and Cristoph Hafemeister and William Stephenson and Brian Houck-Loomis and Pratip K Chattopadhyay and Harols Swerdlow and Rahul Satija and Peter Smibert},
+  title = {Simultaneous epitope and transcriptome measurement in single cells},
+  journal = {Nature Methods}
+  }