metadata.yaml

---
name: single-cell
type: use
topic_type: technology
title: Single Cell

learning_path_ctas:
  For Beginners: intro_single_cell
  For Intermediate Users: beyond_single_cell
  For Coding Enthusiasts: reloaded_single_cell


# Using the community CTAs feature opts you into managing your own requirements list here a bit manually
community_ctas:
  - description: |
      **Pre-requisites:** If you've never used Galaxy before, first try the:
    link: https://training.galaxyproject.org/training-material/topics/introduction/
    link_text: Introduction to Galaxy
    icon: tutorial
    width: 3
    primary_color: true
  - description: |
      The Single Cell & Galaxy learners are on Slack
    link: https://gxy.io/gtn-slack
    link_text: GTN Slack Workspace
    icon: comment
    width: 3
    primary_color: false
  - description: |
      Come chat Single Cell with us on Matrix
    link: https://matrix.to/#/#spoc3:matrix.org
    link_text: Community Matrix Chat
    icon: comment
    width: 3
    primary_color: false
  - description: |
      What else do you want to see? Let us know!
    link: https://docs.google.com/spreadsheets/d/15hqgqA-RMDhXR-ylKhRF-Dab9Ij2arYSKiEVoPl2df4/edit?usp=sharing
    link_text: Tool/Tutorial Requests
    icon: tool
    width: 3
    primary_color: false

gitter: Galaxy-Training-Network_galaxy-single-cell:gitter.im

summary: |
  Training material and practicals for all kinds of single cell analysis (particularly scRNA-seq!).

# What else do you want to see? You can submit tool or tutorial requests, or upvote other requests, on our <i class="fa fa-wrench" aria-hidden="true"></i> [Single-Cell & Spatial Omics Tool Request Spreadsheet]().
#  {: .alert.alert-success}

#docker_image: "quay.io/galaxy/transcriptomics-training"

requirements:
  -
    type: "internal"
    topic_name: introduction

editorial_board:
  - pavanvidem
  - MarisaJL
  - Nilchia
  - dianichj
  - heylf

toc: true  # create table of contents for the subtopics
subtopics:
  - id: scintroduction
    title: "Introduction"
    description: "Start here if you are new to single cell analysis and want to learn the concepts."
  - id: firstsc
    title: "Your first analysis"
    description: "Start here if you are new to single cell analysis in Galaxy and want to try analysing data."
  - id: single-cell-CS
    title: "Case study"
    description: "These tutorials take you from raw scRNA sequencing reads to inferred trajectories to replicate a published analysis. The data is messy. The decisions are tough. The interpretation is meaningful. Come here to advance your single cell skills! Note that you get two options for inferring trajectories."
  - id: single-cell-CS-code
    title: "Case study: Reloaded"
    description: "These tutorials let you follow the same case study analysis of real, messy data but in a programming environment, hosted on Galaxy. So if you want more flexibility, but the same guided steps as the Case Study, you can skip the Case Study and start here instead. Alternatively, try these after completing the Case Study for an easier jump to a coding environment."
  - id: end-to-end
    title: "End-to-end scRNA-seq Analyses"
    description: "These tutorials use different methods to analyse scRNA-seq samples"
  - id: deconvo
    title: "Deconvolution"
    description: "These tutorials infer cell compositions from bulk RNA-seq data using a scRNA-seq reference"
  - id: scmultiomics
    title: "Multiomic Analyses"
    description: "This section lets you build on mere scRNA analyses into a multiomic future!"
  - id: tricks
    title: "Tips, tricks & other hints"
    description: "These tutorials cover helpful skills for scRNA-seq analysis"
  - id: datamanipulation
    title: "Changing data formats & preparing objects"
    description: "These tutorials cover a range of needs for importing data from different sources, to changing data into different formats to move from one analysis to the other."
  - id: exploratory
    title: "Exploratory Analyses"
    description: "You have clusters and you have genes. What can you do now? Come here to explore your results more!"
  - id: spatial
    title: "Spatial Transcriptomics"
    description: "Tutorials for analysing spatially resolved gene expression data, integrating molecular measurements with tissue coordinates, morphology, spatial neighbourhoods, and cell-state annotations."


references:

  - 
    authors: "Loach, Marisa; Naghsh Nilchi, Amirhossein; Chiang, Diana; Howells, Morgan; Heyl, Florian; Rasche, Helena; Jakiela, Julia; Tekman, Mehmet; Gamal, Menna; Moreno, Pablo; Hiltemann, Saskia; Schlegel, Timon; Grüning, Björn; Backofen, Rolf; Videm, Pavankumar; Bacon, Wendi"
    title: "Galaxy single-cell & spatial omics community update: Navigating new frontiers in 2025"
    link: "https://doi.org/10.1016/j.xgen.2025.101005"
    summary: ""

  - 
    authors: "Tekman, Mehmet and Batut, Bérénice; Ostrovsky, Alexander; Antoniewski, Christophe; Clements, Dave; Ramirez, Fidel; Etherington, Graham J; Hotz, Hans-Rudolf; Scholtalbers, Jelle; Manning, Jonathan R; Bellenger, Lea; Doyle, Maria A; Heydarian, Mohammad; Huang, Ni; Soranzo, Nicola; Moreno, Pablo; Mautner, Stefan; Papatheodorou, Irene; Nekrutenko, Anton; Taylor, James; Blankenberg, Daniel; Backofen, Rolf; Grüning, Björn;"
    title: "A single-cell RNA-sequencing training and analysis suite using the Galaxy framework"
    link: "https://doi.org/10.1093/gigascience/giaa102"
    summary: ""
  -
    authors: "Pablo Moreno, Ni Huang, Jonathan R Manning, Suhaib Mohammed, Andrey Solovyev, Krzysztof Polanski, Wendi Bacon, Ruben Chazarra, Carlos Talavera-López, Maria A Doyle, Guilhem Marnier, Björn Grüning, Helena Rasche, Nancy George, Silvie Korena Fexova, Mohamed Alibi, Zhichao Miao, Yasset Perez-Riverol, Maximilian Haeussler, Alvis Brazma, Sarah Teichmann, Kerstin B Meyer, Irene Papatheodorou;"
    title: "User-friendly, scalable tools and workflows for single-cell RNA-seq analysis"
    link: "https://www.nature.com/articles/s41592-021-01102-w"
    summary: ""