Single Cell

📅 Galaxy Training Academy 2026

Delphine Lariviere — 2026-01-21T17:14:51+00:00

The Galaxy Training Academy is a self-paced online training event for beginners and advanced learners who want to improve their data analysis skills in Galaxy and/or in popular fields in bioinformatics. Over the course of one week, we offer a diverse selection of learning tracks for you.

🎥 Recording of Pseudobulk Analysis with Decoupler and EdgeR

2025-05-02T00:00:00+00:00

A 50M long recording is now available.

🛠️ Filter plot and explore single-cell RNA-seq data with Scanpy (imported from uploaded file)

2025-04-23T08:54:05+00:00

Workflow for this training: https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.html

🛠️ LOCKED | Combining single cell datasets after pre-processing

2025-04-22T14:30:06+00:00

Workflow associated with this training: https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.html

🛠️ LOCKED | Generating a single cell matrix using Alevin

2025-04-22T14:30:06+00:00

This workflow is for the training material: https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_alevin/tutorial.html

🎥 Recording of Clustering 3K PBMCs with Seurat

2025-04-15T00:00:00+00:00

A 2H11M long recording is now available.

📅 From Data to Discovery: Metagenomics, RNA-Seq - NGS Bioinformatics with Galaxy

Daniela Schneider — 2025-03-26T13:14:41+00:00

This course introduces scientists to the data analysis platform Galaxy. The course is a beginner course; no programming skills are required.

🛤️ Bioinformatics Projects: Using deconvolution to get new insights from old bulk RNA-seq data

2025-03-06T11:13:12+00:00

Are you an educator looking for project ideas for students to practice independent enquiry and research skills? Are you a student looking for a project idea? Look no more - here, you will find a learning pathway of tutorials that can guide you through the skills to find old data and transform it into new results! To be clear, we will only provide the methods - you will need to come up with your own research question by exploring the literature and available public datasets, apply these analyses, and interpret the results. Your research question will take the form of, **"How does `variable X` impact the cell type proportions in `issue/sample/organism Y`?"** Note: You will need to be familiar with the Galaxy interface and single-cell RNA-seq analysis in general to follow this Learning Pathway. You can do so by completing the [Introduction to single-cell analysis learning pathway]({% link learning-pathways/intro_single_cell.md %}). It would be a bonus to also complete the [Beyond single cell learning pathway]({% link learning-pathways/beyond_single_cell.md %}) to reinforce that knowledge. For support throughout these tutorials, join our Galaxy [single cell chat group on Matrix](https://matrix.to/#/#Galaxy-Training-Network_galaxy-single-cell:gitter.im) to ask questions!

📰 Bioinformatics Projects Pathway for Students: New insights from public data!

2025-03-03T00:00:00+00:00

With a huge thanks to ELIXIR-UK’s DaSH Fellowship funding, we are delighted to present our new Learning Pathway, “Bioinformatics Projects: Using deconvolution to get new insights from old bulk RNA-seq”.

🛠️ Multisample Batch Correction with SnapATAC2 and Harmony

2025-02-28T07:41:57+00:00

This Workflow takes a dataset collection of single-cell ATAC-seq fragments and performs: - preprocessing - filtering - concatenation - dimension reduction - batch correction - leiden clustering

📚 Multi-sample batch correction with Harmony and SnapATAC2

2025-02-28T07:41:57+00:00

📰 SPOC HDR UK ELIXIR-UK CoFest 2025: How did it go?

2025-02-14T00:00:00+00:00

We held our second 🖖🏾SPOC CoFest, in the great tradition of the excellent CoFests organised in the GTN that welcomed

🛠️ Understanding Barcodes

2025-02-13T15:21:28+00:00

🛠️ Pseudobulk with decoupler and edgeR tutorial workflow

2025-02-12T16:29:15+00:00

Pseudobulk Analysis Workflow for Tutorial

📚 Pseudobulk Analysis with Decoupler and EdgeR

2025-02-12T16:29:15+00:00

Pseudobulk analysis is a powerful technique that bridges the gap between single-cell and bulk RNA-seq data. It involves aggregating gene expression data from groups of cells within the same biological replicate, such as a mouse or patient, typically based on clustering or cell type annotations (Murphy and Skene 2022).

📅 Galaxy Training Academy 2025

Delphine Lariviere — 2025-02-10T19:40:45+00:00

The Galaxy Training Academy is a self-paced online training event for beginners and advanced learners who want to improve their Galaxy data analysis skills. Over the course of one week, we offer a diverse selection of learning track for you.

🛠️ Music: Pre-grouping cell types

2025-02-10T14:23:01+00:00

Workflow for the second half of the "Bulk RNA Deconvolution with MuSiC" tutorial. Implements the "Estimation of cell type proportions with pre-grouping of cell types" section

🛠️ Music Stage 4 - Compute metrics

2025-02-02T21:13:57+00:00

🛠️ Music Stage 3 - Preprocess Visualisations

2025-02-02T21:13:57+00:00

🛠️ Music Stage 2 - Inferring cellular proportions

2025-02-02T21:13:57+00:00

🛠️ Music Stage 1 - Create pseudobulk and actual proportions

2025-02-02T21:13:57+00:00

📚 Evaluating Reference Data for Bulk RNA Deconvolution

2025-02-02T21:13:57+00:00

There are various methods to estimate the proportions of cell types in bulk RNA data. Since the actual cell proportions of the data are unknown, how do we know if our tools are producing accurate results?

🛠️ Cluster 3k PBMCs with Seurat - Workflow - SCTransform Version

2025-01-23T21:33:29+00:00

This is the workflow for the Clustering 3K PBMCs with Seurat tutorial if you use SCTransform for preprocessing.

🛠️ Clustering 3k PBMCs with Seurat - Workflow

2025-01-23T21:33:29+00:00

This is the workflow for the Clustering 3K PBMCs with Seurat tutorial if you are using the separate preprocessing tools (NormalizeData, FindVariableFeatures, ScaleData).

📚 Clustering 3K PBMCs with Seurat

2025-01-23T21:33:29+00:00

Single cell RNA-seq analysis enables us to explore differences in gene expression between cells. It can reveal the heterogenity within cell populations and help us to identify cell types that could play roles in development, disease, or other processes. Single cell omics is a relatively young field, but there are a few commonly-used analysis pipelines that you will often see in the literature. In this tutorial, we will use one of these pipelines, Seurat, to cluster single cell data from a 10X Genomics experiment (Hao et al. 2023). You can follow the same analysis using the Scanpy pipeline in the Clustering 3K PBMCs with Scanpy tutorial.

📰 GTN's Gift for 2024

2024-12-19T00:00:00+00:00

Community Pages

📰 🖖🏾Galaxy SPOC Community: Year in Review

2024-12-18T00:00:00+00:00

🚀 2024: A SPOC-tacular Year in Review 🌌

🛠️ scRNA Plant Analysis

2024-12-13T18:54:19+00:00

Downstream Single-cell RNA Plant analysis with ScanPy

📰 SPOC CoFest 2024: How did it go?

2024-12-06T00:00:00+00:00

First SPOC CoFest

🛠️ Seurat Filter, Plot and Exlore tutorial

2024-12-05T13:20:05+00:00

📅 🖖🏾 SPOC II: the Write-a-Than

Wendi Bacon — 2024-11-26T13:38:15+00:00

Single-cell & sPatial Omics Community (SPOC) contributors in Galaxy will get together online or asynchronously to continue working on our draft of an updates paper on all things SPOC. If you have not yet been involved, please read through our shared googledoc - https://docs.google.com/document/d/179G-VDl7NggXr2AhMPQ8UPrYwoa7KgPd29Us6G74O7U/edit?usp=sharing - and then email Wendi (wendi.bacon@open.ac.uk) with what you'd like to contribute, or to be assigned a task.

🛤️ Applying single-cell RNA-seq analysis in Coding Environments

2024-10-30T14:54:23+00:00

Gone is the pre-annotated, high quality tutorial data - now you have real, messy data to deal with. You have decisions to make and parameters to decide. This learning pathway challenges you to replicate a published analysis as if this were your own dataset. You will perform this analysis in coding environments hosted on Galaxy, instead of Galaxy's button-based tool interface. 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: performing the analysis predominantly in R or in Python. For support throughout these tutorials, join our Galaxy [single cell chat group on Matrix](https://matrix.to/#/#Galaxy-Training-Network_galaxy-single-cell:gitter.im) to ask questions!

📅 Single-cell and sPatial Omics | Collaboration Fest

Wendi Bacon — 2024-10-11T09:33:58+00:00

The Single-cell and sPatial Omics Community of Practice (SPOC) are hosting their first Collaboration Fest, welcoming new and experienced contributors to our training materials.

📅 🖖🏾 SPOC Write-a-thon

Wendi Bacon — 2024-10-11T09:32:06+00:00

Single-cell & sPatial Omics Community (SPOC) contributors in Galaxy will get together online or asynchronously to draft an updates paper on all things SPOC. For those unable to make the event live, please add your thoughts and authoring in advance of the Write-a-thon using the shared [googledoc](https://docs.google.com/document/d/179G-VDl7NggXr2AhMPQ8UPrYwoa7KgPd29Us6G74O7U/edit?usp=sharing).

📅 2024 Bioinformatics Bootcamp - The Open University

Wendi Bacon — 2024-10-10T15:07:28+00:00

The School of Life, Health, and Chemical Sciences (LHCS) at The Open University (OU) is running a free, week-long Bioinformatics Bootcamp from the 16-20th September aimed at level 2 and level 3 OU students who are studying life, health and chemical sciences modules and have already completed 120 credits of level 1 study.

🎥 Recording of Single-cell Formats and Resources

2024-09-30T00:00:00+00:00

A 19M long recording is now available.

🎥 Recording of Clustering 3K PBMCs with Scanpy

2024-09-26T00:00:00+00:00

A 1H39M long recording is now available.

❓ Using tutorial mode and the Case Study suite

2024-09-18T15:47:56+00:00

Tutorial mode saves you screen space, finds the tools you need, and ensures you use the correct versions for the tutorials to run.

🛠️ GO Enrichment Workflow

2024-09-17T07:09:29+00:00

🖼️ GO Enrichment Analysis on Single-Cell RNA-Seq Data

2024-09-17T07:09:29+00:00

scRNA-Seq data analysis roadmap

📚 GO Enrichment Analysis on Single-Cell RNA-Seq Data

2024-09-17T07:09:29+00:00

In the tutorial Filter, plot and explore single-cell RNA-seq data with Scanpy, we took an important step in our single-cell RNA sequencing analysis by identifying marker genes for each of the clusters in our dataset. These marker genes are crucial, as they help us distinguish between different cell types and states, giving us a clearer picture of the cellular diversity within our samples.

❓ Why is my tool erroring as 'Above error raised while reading key '/layers' of type from /.'

2024-09-12T15:03:29+00:00

🎥 Recording of Generating a single cell matrix using Alevin

2024-09-12T00:00:00+00:00

A 49M long recording is now available.

🎥 Recording of Importing files from public atlases

2024-09-12T00:00:00+00:00

A 18M54S long recording is now available.

🎥 Recording of Filter, plot and explore single-cell RNA-seq data with Scanpy (Python)

2024-08-06T00:00:00+00:00

A 13M long recording is now available.

🖼️ Single-cell Formats and Resources

2024-07-26T17:22:16+00:00

Breakdown of single-cell data

📰 New Tutorial: Single-cell ATAC-seq standard processing with SnapATAC2

2024-07-12T00:00:00+00:00

We are proud to announce that a new training, explaining the analysis of single cell ATAC-seq data with SnapATAC2 and Scanpy, is now available in the Galaxy Training Network.

🛠️ Workflow - Standard processing of 10X single cell ATAC-seq data with SnapATAC2

2024-07-11T14:34:09+00:00

Workflow of Tutorial "Single-cell ATAC-seq standard processing with SnapATAC2". This workflow takes a fragment file as input and performs the standard steps of scATAC-seq analysis: filtering, dimension reduction, embedding and visualization of marker genes with SnapATAC2. In an alternative step, the fragment file can also be generated from a BAM file.

📚 Single-cell ATAC-seq standard processing with SnapATAC2

2024-07-11T14:34:09+00:00

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) analysis is a method to decipher the chromatin states of the analyzed cells. In general, genes are only expressed in accessible (i.e. “open”) chromatin and not in closed chromatin.

📰 From GTN Intern to Tutorial Author to Bioinformatician

2024-06-13T00:00:00+00:00

With growing access and interest in sequencing data, Galaxy is a knight in shining armor for wet lab scientists hoping to analyze their own data. With long term intentions of increasing access to bioinformatic analyses, the Galaxy Training Network (GTN) creates a safe space where non-computer-scientists may analyze their own data and even learn to code: an invaluable skill in today’s scientific world. Galaxy introduced me to brand new skills as an undergraduate and ultimately changed the trajectory of my career. Here is my story as a biology undergraduate with no coding experience turned GTN contributor &, eventually, coding bioinformatician: thanks to Galaxy.

📅 Galaxy Training Academy 2024

Teresa Müller — 2024-06-11T15:07:31+00:00

The Galaxy Academy is a self-paced online training event for beginners as well as learners who would like to improve their Galaxy data analysis skills. Over the course of one week, we will have a different topic and focus every day.

📅 FAIR data management in single-cell analysis

Katarzyna Kamieniecka — 2024-06-04T15:45:47+00:00

This course will introduce the Galaxy Platform, covering the basic functionality for single-cell data processing. It will include an overview of various common single-cell datatypes used in bioinformatics. Participants will gain hands-on experience loading single-cell data from external resources into Galaxy, parsing academic literature to find relevant metadata, and converting the data into the common AnnData format, ready for further analysis in Galaxy.

📚 Filter, plot, and explore single cell RNA-seq data with Seurat

2024-04-09T08:31:24+00:00

You’ve previously done all the work to make a single cell matrix. Now it’s time to fully process our data using Seurat: remove low quality cells, reduce the many dimensions of data that make it difficult to work with, and ultimately try to define clusters and find some biological meaning and insights! There are many packages for analysing single cell data - Seurat (Satija et al. 2015), Scanpy (Wolf et al. 2018), Monocle (Trapnell et al. 2014), Scater (McCarthy et al. 2017), and many more. We’re working with Seurat because it is well updated, broadly used, and highly trusted within the field of bioinformatics.

🛤️ Applying single-cell RNA-seq analysis

2024-03-07T16:22:08+00:00

Gone is the pre-annotated, high quality tutorial data - now you have real, messy data to deal with. You have decisions to make and parameters to decide. This learning pathway challenges you to replicate a published analysis as if this were your own dataset. You will be introduced to a few more tools available for scRNA-seq in Galaxy. Finally, if our tool offerings are not enough for you, you will be directed towards how to use coding notebooks within Galaxy, setting you up to analyse scRNA-seq in R or python notebooks. 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. For support throughout these tutorials, join our Galaxy [single cell chat group on Matrix](https://matrix.to/#/#Galaxy-Training-Network_galaxy-single-cell:gitter.im) to ask questions!

🛠️ AnnData to SingleCellExperiment (SCE) conversion

2024-02-13T12:28:05+00:00

AnnData to SCE format conversion (manually using Galaxy buttons)

🛠️ AnnData to Seurat conversion

2024-02-13T12:28:05+00:00

AnnData to Seurat format conversion (manually using Galaxy buttons)

🛠️ AnnData to Cell Data Set (CDS) conversion

2024-02-13T12:28:05+00:00

AnnData to CDS format conversion (manually using Galaxy buttons). This workflow does not include renaming the column containing gene symbols.

📚 Converting between common single cell data formats

2024-02-13T12:28:05+00:00

You finally decided to analyse some single cell data, you got your files either from the lab or publicly available sources, you opened the first tutorial available on Galaxy Training Network and… you hit the wall! The format of your files is not compatible with the one used in tutorial! Have you been there?

📰 FAIR Data management in single cell analysis

2024-01-17T00:00:00+00:00

New single cell section: Changing data formats & preparing objects

📰 Galaxy Single-cell Community: Year in Review

2023-12-22T00:00:00+00:00

🚀Embarking on a cosmic journey, the Galaxy Single-cell Community has clustered together to unveil a constellation of tools, making strides in RNA-stellar discoveries and creating out-of-this-world workflows. With a commitment to battling work duplication across the multiverse, this community is boldly charting a course for global domination, proving that when it comes to bioinformatics, the Galaxy is the limit!✨

🛠️ EBI SCXA to AnnData (Scanpy) or Seurat Object

2023-12-14T16:16:41+00:00

Creates input file for Filter, Plot, Explore tutorial

🛠️ NCBI to Anndata

2023-12-13T18:43:30+00:00

📚 Converting NCBI Data to the AnnData Format

2023-12-13T18:43:30+00:00

The goal of this tutorial is to take raw NCBI data from some published research, convert the raw data into the AnnData format then add metadata to the object so that it can be used for further processing / analysis. Here we will look at the steps to obtain, understand, and manipulate the data in order for it to be properly processed.

📚 Generating a single cell matrix using Alevin and combining datasets (bash + R)

2023-12-08T14:46:22+00:00

Setting up the environment

🛠️ Inferring Trajectories with Scanpy Tutorial Workflow

2023-12-08T07:20:20+00:00

📚 Inferring single cell trajectories with Scanpy

2023-12-08T07:20:20+00:00

You’ve done all the hard work of preparing a single-cell matrix, processing it, plotting it, interpreting it, and finding lots of lovely genes. Now you want to infer trajectories, or relationships between cells… you can do that here, using the Galaxy interface, or head over to the Jupyter notebook version of this tutorial to learn how to perform the same analysis using Python.

🛠️ Clustering 3k PBMC with Scanpy

2023-12-05T14:45:06+00:00

Workflow based on clustering 3K PBMCs with Scanpy tutorial

📚 Importing files from public atlases

2023-11-14T17:16:58+00:00

Public single cell datasets seem to accumulate by the second. Well annotated, quality datasets are slightly trickier to find, which is why projects like the Single Cell Expression Atlas (SCXA) exist - to curate datasets for public use. Here, we will guide you through transforming data imported from the SCXA repository into the input file required for the Filter, Plot, Explore tutorial and we will also show how to use the public atlases for your own research.

❓ Notebook-based tutorials can give different outputs

2023-10-17T12:50:06+00:00

The nature of coding pulls the most recent tools to perform tasks. This can - and often does - change the outputs of an analysis. Be prepared, as you are unlikely to get outputs identical to a tutorial if you are running it in a programming environment like a Jupyter Notebook or R-Studio. That’s ok! The outputs should still be pretty close.

📰 Single cell subdomain re-launch: Unified and feedback-driven

2023-10-12T00:00:00+00:00

The European Galaxy Days 2023 CoFest combined the forces of administrator, developer and trainer to update and re-launch the single cell Galaxy instance. Where previously there were two subdomains each with their own sets of tools, there is now a unified subdomain with re-categorized tools that makes sense for users.

❓ How can I talk with other users?

2023-10-08T15:21:27+00:00

❓ Use our Single Cell Omics Lab

2023-10-08T15:21:27+00:00

🛤️ Introduction to Galaxy and Single Cell RNA Sequence analysis

2023-10-04T16:02:12+00:00

This learning path aims to teach you the basics of Galaxy and analysis of Single Cell RNA-seq data. You will learn how to use Galaxy for analysis, and an important Galaxy feature for iterative single cell analysis. You'll tbe guided through the general theory of single analysis and then perform a basic analysis of 10X chromium data. For support throughout these tutorials, join our Galaxy [single cell chat group on Matrix](https://matrix.to/#/#Galaxy-Training-Network_galaxy-single-cell:gitter.im) to ask questions!

📚 Filter, plot, and explore single cell RNA-seq data with Seurat (R)

2023-10-02T09:11:50+00:00

🖼️ Automated Cell Annotation

2023-09-04T08:24:00+00:00

What is Cell Annotation?

📚 Filter, plot and explore single-cell RNA-seq data with Scanpy (Python)

2023-08-25T05:00:35+00:00

🛠️ Scanpy Parameter Iterator workflow full (imported from URL)

2023-07-19T06:47:23+00:00

📚 Scanpy Parameter Iterator

2023-07-19T06:47:23+00:00

The magic of bioinformatic analysis is that we use maths, statistics and complicated algorithms to deal with huge amounts of data to help us investigate biology. However, analysis is not always straightforward – each tool has various parameters to select. Eventually, we can end up with very different outcomes depending on the values we choose. With analysing scRNA-seq data, it’s almost like you need to know about 75% of your data, then make sure your analysis shows that, for you to then be able to identify the 25% new information.

🛠️ Filter, Plot and Explore Single-cell RNA-seq Data updated

2023-06-13T11:38:52+00:00

Filter, Plot and Explore Single-cell RNA-seq Data

🛠️ GTN - Preprocessing of 10X scRNA-seq data

2023-05-19T11:33:23+00:00

🎥 Recording of Pre-processing of 10X Single-Cell RNA Datasets

2023-05-19T00:00:00+00:00

A 19M long recording is now available.

🛠️ Trajectory analysis using Monocle3 - full tutorial workflow

2023-05-16T08:09:55+00:00

Trajectory analysis using Monocle3, starting from AnnData

🎥 Recording of Combining single cell datasets after pre-processing

2023-05-09T00:00:00+00:00

A 11M long recording is now available.

🛠️ scATAC-seq FASTQ to Count Matrix

2023-04-24T18:23:20+00:00

This workflow creates an count matrix anndata file given 10x scATAC-seq data.

🛠️ scATAC-seq Count Matrix Filtering

2023-04-24T18:23:20+00:00

Visualize and filter scATAC-seq anndata to produce a high quality count matrix

📚 Pre-processing of 10X Single-Cell ATAC-seq Datasets

2023-04-24T18:23:20+00:00

Similar to bulk ATAC-Seq, single-cell ATAC-Seq (scATAC-seq) leverages the hyperactive Tn5 Transposase to profile open chromatin regions but at single-cell resolution. Thus helps in understanding cell type-specific chromatin accessibility from a heterogeneous cell population.

📚 Inferring single cell trajectories with Monocle3 (R)

2023-04-12T19:29:39+00:00

🎥 Recording of Inferring single cell trajectories with Monocle3

2023-04-11T00:00:00+00:00

A 15M long recording is now available.

🛠️ Cell Cycle Regression Workflow

2023-01-25T09:43:32+00:00

📚 Removing the effects of the cell cycle

2023-01-25T09:43:32+00:00

Single-cell RNA sequencing can be sensitive to both biological and technical variation, which is why preparing your data carefully is an important part of the analysis. You want the results to reflect the interesting differences in expression between cells that relate to their type or state. Other sources of variation can conceal or confound this, making it harder for you to see what is going on.

🛠️ MuSiC-Deconvolution: Compare

2023-01-20T10:58:39+00:00

This workflow runs 3 comparisons using MuSiC Deconvolution compare: where datasets cell compositions are inferred from a reference containing healthy and diseased cells; where diseased are inferred from disease and healthy from healthy; and where both diseased and healthy are inferred from a healthy reference.

🛠️ MuSiC-Deconvolution: Data generation | bulk | ESet

2023-01-20T10:58:39+00:00

This workflow creates bulk ESet objects from uploaded raw matrix & metadata files

🛠️ MuSiC-Deconvolution: Data generation | sc | metadata

2023-01-20T10:58:39+00:00

This workflow generates from only an EBI SCXA reference the metadata for creating an ESet object

🛠️ MuSiC-Deconvolution: Data generation | sc | matrix + ESet

2023-01-20T10:58:39+00:00

This workflow creates an ESet object from scRNA metadata file and EBI SCXA retrieveal

📚 Bulk matrix to ESet | Creating the bulk RNA-seq dataset for deconvolution

2023-01-20T10:58:39+00:00

After completing the MuSiC deconvolution tutorial (Wang et al. 2019), you are hopefully excited to apply this analysis to data of your choice. Annoyingly, getting data in the right format is often what prevents us from being able to successfully apply analyses. This tutorial is all about reformatting a raw bulk RNA-seq dataset pulled from a public resource (the EMBL-EBI Expression atlas (Moreno et al. 2021). Let’s get started!

📚 Matrix Exchange Format to ESet | Creating a single-cell RNA-seq reference dataset for deconvolution

2023-01-20T10:58:39+00:00

After completing the MuSiC Wang et al. 2019 deconvolution tutorial, you are hopefully excited to apply this analysis to data of your choice. Annoyingly, getting data in the right format is often what prevents us from being able to successfully apply analyses. This tutorial is all about reformatting a raw scRNA-seq dataset pulled from a public resource (the EMBL-EBI single cell expression atlas Moreno et al. 2021. Let’s get started!

📚 Comparing inferred cell compositions using MuSiC deconvolution

2023-01-20T10:58:39+00:00

The goal of this tutorial is to apply bulk RNA deconvolution techniques to a problem with multiple variables - in this case, a model of diabetes is compared with its healthy counterparts. All you need to compare inferred cell compositions are well-annotated, high quality reference scRNA-seq datasets, transformed into MuSiC-friendly Expression Set objects, and your bulk RNA-samples of choice (also transformed into MuSiC-friendly Expression Set objects). For more information on how MuSiC works, you can check out their github site MuSiC or published article (Wang et al. 2019).

📰 New Tutorial Suite: Deconvolution with MuSiC, from public data to disease interrogation!

2022-11-29T00:00:00+00:00

The still new and shiny single-cell analysis topic now boasts a deconvolution tutorial suite! What does deconvolution do you ask? Well, in this context, it infers cell proportions from bulk RNA-seq data. You heard that correctly - instead of expensive new single-cell experiments, you can re-analyse old bulk RNA-seq data and estimate cell proportions. All you need is a reasonably good single cell dataset to use as a reference and you’re good to go! The tutorial suite shows you how to build your reference from publicly available single cell data, and apply analysis to some publicly available bulk RNA-seq data.

❓ How do I know what protocol my data was sequenced with?

2022-11-18T15:34:02+00:00

📰 New Topic: Single Cell Analysis!

2022-11-18T00:00:00+00:00

Single-cell analysis now has it’s own topic! These tutorials were previously part of the transcriptomics topic, but due to the amazing efforts by

🛠️ Monocle3 workflow

2022-09-30T20:06:34+00:00

Trajectory analysis using Monocle3, starting from 3 input files: expression matrix, gene and cell annotations

🛠️ AnnData object to Monocle input files

2022-09-30T20:06:34+00:00

Preparing and filtering gene and cell annotations files and expression matrix to be passed as input for Monocle

🖼️ Trajectory analysis

2022-09-30T20:06:34+00:00

What is trajectory analysis?

📚 Inferring single cell trajectories with Monocle3

2022-09-30T20:06:34+00:00

This tutorial is a follow-up to the ‘Single-cell RNA-seq: Case Study’. We will use the same sample from the previous tutorials. If you haven’t done them yet, it’s highly recommended that you go through them to get an idea how to prepare a single cell matrix, combine datasets and filter, plot and process scRNA-seq data to get the data in the form we’ll be working on today.

❓ Why is Alevin is not working?

2022-09-08T14:00:12+00:00

Check your tool version, you need to use 1.3.0+galaxy2

📚 Combining single cell datasets after pre-processing

2022-09-08T14:00:12+00:00

This tutorial will take you from the multiple AnnData outputs of the previous tutorial to a single, combined AnnData object, ready for all the fun downstream processing. We will also look at how to add in metadata (for instance, SEX or GENOTYPE) for analysis later on.

❓ The UMAP Plots errors out sometimes?

2022-03-02T15:46:20+00:00

Try a different colour palette. For upstream code reasons, the default color palette sometimes causes the tool to error out.

❓ Are Barcodes always on R1 and Sequence data on R2?

2022-03-02T15:46:20+00:00

No, it really depends on the protocol. In some protocols this convention is swapped, in others the barcodes can be distributed across both reads.

❓ AnnData Import/ AnnData Manipulate not working?

2022-03-02T15:46:20+00:00

❓ On the Scanpy PlotEmbed step, my object doesn’t have Il2ra or Cd8b1 or Cd8a etc.

2022-02-28T15:26:33+00:00

Check your Anndata object - it should be 7874 x 14832, i.e. 7874 cells x 14832 genes. Is it actually 2000 genes only (i.e. and therefore missing the above markers)? You may have selected to remove genes at the Scanpy FindVariableGenes step (last toggle, ‘Remove genes not marked as highly variable’ < Select NO.) (Most likely you did this correctly the first time, but later in investigating how many got marked as highly variable, may have run this tool again and removed the nonvariable ones. We’ve updated the text to more clearly prevent this, but you may have gotten caught out!)

❓ On Scanpy PlotEmbed, the tool is failing

2022-02-28T15:26:33+00:00

Try selecting “Use raw attributes if present: NO”

❓ My Scanpy FindMarkers step is giving me an empty table

2022-02-28T15:26:33+00:00

Try selecting: “Use programme defaults: Yes” and see if that fixes it.

❓ Why is Alevin is not working?

2022-02-28T15:26:33+00:00

Check your tool version, you need to use 1.3.0+galaxy2

🛠️ MuSiC: Deconvolution

2022-02-11T12:34:18+00:00

Workflow for the first half of the "Bulk RNA Deconvolution with MuSiC" tutorial.

📚 Bulk RNA Deconvolution with MuSiC

2022-02-11T12:34:18+00:00

Bulk RNA-seq data contains a mixture of transcript signatures from several types of cells. We wish to deconvolve this mixture to obtain estimates of the proportions of cell types within the bulk sample. To do this, we can use single cell RNA-seq data as a reference for estimating the cell type proportions within the bulk data.

❓ Are UMIs not actually unique?

2021-11-17T06:38:37+00:00

Not strictly, but unique enough. The distribution of UMIs should ideally be uniform so that the chance of any two same UMIs capturing the same transcript (via different amplicons) is small. As barcodes have increased in size, the number of UMIs has also increased allowing for UMIs to reach more or less the same numbers of transcripts.

❓ Why do we only consider highly variable genes?

2021-11-11T16:57:25+00:00

The non-variable genes are likely housekeeping genes, which are expressed everywhere and are not so useful for distinguishing one cell type from another. However background genes are important to the analysis and are used to generate a background baseline model for measuring the variability of the other genes.

❓ Can RNA-seq techniques be applied to scRNA-seq?

2021-11-11T16:57:25+00:00

The short answer is ‘no, but yes’. At the beginning this was impossible due to the over-prevalence of dropout events (“zeroes”) in the data complicating the normalisation techniques, but this is not so much of a problem any more with newer methods.

❓ What exactly is a ‘Gene profile’?

2021-11-11T16:57:25+00:00

Think of it like a fingerprint that some cells exhibit and others don’t. It’s a small collection of genes which are up or down regulated in relation to one another. Their differences are not absolute, but relative. So if CellA has 100 counts of Gene1 and 50 counts of Gene2, this creates a relation of 2:1 between Gene1 and Gene2. If CellB has a 20 counts of Gene1 and 10 counts of Gene2, then they share the same relation. If CellA and CellB share other relations with other genes than this might be enough to say that they share a Gene profile, and will therefore likely cluster together as they describe the same cell type.

❓ Why do we do dimension reduction and then clustering? Why not just cluster on the actual data?

2021-11-11T16:57:25+00:00

The actual data has tens of thousands of genes, and so tens of thousands of variables to consider. Even after selecting for the most variable genes and the most high quality genes, we can still be left with > 1000 genes. Performing clustering on a dataset with 1000s of variables is possible, but computationally expensive. It is therefore better to perform dimension reduction to reduce the number of variables to a latent representation of these variables. These latent variables are ideally more than 10 but less than 50 to capture the variability in the data to perform clustering upon.

❓ Why is amplification more of an issue in scRNA-seq than RNA-seq?

2021-11-11T16:57:25+00:00

Due to the extremely small amount of starting material, the initial amplification is likely to be uneven due to the first cycle of amplified products being overrepresented in the second cycle of amplification leading to further bias. In Bulk RNA-seq, the larger selection of RNA molecules to amplify, evens out the odds that any one transcript will be amplified more than others.

🖼️ Una introducción al análisis de datos scRNA-seq

2021-10-19T15:23:36+00:00

Single-cell RNA-seq

🖼️ Introducción al análisis de datos de scRNA-seq

2021-10-19T15:12:12+00:00

RNA-seq de una sola célula

🛠️ scRNA Plant Analysis

2021-04-08T10:59:53+00:00

Downstream Single-cell RNA Plant analysis with ScanPy

📚 Analysis of plant scRNA-Seq Data with Scanpy

2021-04-08T10:59:53+00:00

Single cell RNA-seq analysis is a cornerstone of developmental research and provides a great level of detail in understanding the underlying dynamic processes within tissues. In the context of plants, this highlights some of the key differentiation pathways that root cells undergo.

📚 Inferring single cell trajectories with Scanpy (Python)

2021-04-07T14:04:47+00:00

Run the tutorial!

📰 New Tutorial: Downstream Single-cell RNA Plant analysis with ScanPy

2021-03-30T00:00:00+00:00

🛠️ CS3_Filter, Plot and Explore Single-cell RNA-seq Data

2021-03-24T11:32:22+00:00

Updated tool versions Aug 24 2022

📚 Filter, plot and explore single-cell RNA-seq data with Scanpy

2021-03-24T11:32:22+00:00

You’ve done all the work to make a single cell matrix, with mitochondrial genes flagge and buckets of cell metadata from all your variables of interest. Now it’s time to fully process our data, to remove low quality cells, to reduce the many dimensions of the data that make it difficult to work with, and ultimately to try to define our clusters and to find our biological meaning and insights! There are many packages for analysing single cell data - Seurat Satija et al. 2015, Scanpy Wolf et al. 2018, Monocle Trapnell et al. 2014, Scater McCarthy et al. 2017, and so forth. We’re working with Scanpy, although Galaxy has training using other packages, which you can explore on our level Single-cell training topic.

🎥 Recording of Clustering 3K PBMCs with Scanpy

2021-03-18T00:00:00+00:00

A 45M long recording is now available.

🎥 Recording of Pre-processing of 10X Single-Cell RNA Datasets

2021-03-18T00:00:00+00:00

A 5M long recording is now available.

🎥 Recording of Understanding Barcodes

2021-03-18T00:00:00+00:00

A 10M long recording is now available.

🎥 Recording of Bulk RNA Deconvolution with MuSiC

2021-03-08T00:00:00+00:00

A 20M long recording is now available.

🛠️ Combining datasets after pre-processing

2021-03-03T13:21:27+00:00

Updated March 2024

🛠️ Generating a single cell matrix using Alevin

2021-03-03T13:21:27+00:00

This workflow generates a single cell matrix using Alevin.

📚 Generating a single cell matrix using Alevin

2021-03-03T13:21:27+00:00

This tutorial will take you from raw FASTQ files to a cell x gene data matrix in AnnData format. What’s a data matrix, and what’s AnnData format? Well you’ll find out! Importantly, this is the first step in processing single cell data in order to start analysing it.

🎥 Recording of Inferring single cell trajectories with Scanpy (Python)

2021-02-15T00:00:00+00:00

A 10M long recording is now available.

🎥 Recording of An introduction to scRNA-seq data analysis

2021-02-15T00:00:00+00:00

A 20M long recording is now available.

🎥 Recording of Combining single cell datasets after pre-processing

2021-02-15T00:00:00+00:00

A 11M long recording is now available.

🎥 Recording of Analysis of plant scRNA-Seq Data with Scanpy

2021-02-15T00:00:00+00:00

A 55M long recording is now available.

🎥 Recording of Generating a single cell matrix using Alevin

2021-02-15T00:00:00+00:00

A 30M long recording is now available.

🎥 Recording of Filter, plot and explore single-cell RNA-seq data with Scanpy

2021-02-15T00:00:00+00:00

A 30M long recording is now available.

🖼️ An introduction to scRNA-seq data analysis

2021-01-29T16:45:13+00:00

Single-cell RNA-seq

🎥 Recording of Converting NCBI Data to the AnnData Format

2020-09-12T00:00:00+00:00

A 26M11S long recording is now available.

🖼️ Clustering 3K PBMCs with Scanpy

2020-02-28T16:35:17+00:00

Single Cell RNA Pre-processing

📚 Clustering 3K PBMCs with Scanpy

2019-12-19T19:40:33+00:00

Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq.

🛠️ Single-cell QC with scater

2019-10-23T18:48:12+00:00

Single-cell quality control with scater

📚 Pre-processing of 10X Single-Cell RNA Datasets

2019-09-11T13:07:03+00:00

🛠️ RaceID Workflow

2019-03-25T16:14:31+00:00

Downstream Single-cell RNA analysis with RaceID

🛠️ CelSeq2: Single Batch (mm10)

2019-02-22T19:53:50+00:00

Pre-processing of Single-Cell RNA Data

🛠️ CelSeq2: Multi Batch (mm10)

2019-02-22T19:53:50+00:00

Pre-processing of Single-Cell RNA Data

📚 Understanding Barcodes

2019-02-20T18:33:11+00:00

Barcodes are small oligonucleotides that are inserted into the captured sequence at a specific point, and provide two pieces of information about the sequence:

🖼️ Plates, Batches, and Barcodes

2019-02-16T20:04:07+00:00

Sorting Plates