Recently added tutorials, slides, FAQs, and events in the single-cell topic
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.
Published: 2024-10-11T09:33:58+00:00Single-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).
Published: 2024-10-11T09:32:06+00:00The 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.
Published: 2024-10-10T15:07:28+00:00Tutorial mode saves you screen space, finds the tools you need, and ensures you use the correct versions for the tutorials to run.
Published: 2024-09-18T15:47:56+00:00scRNA-Seq data analysis roadmap
Published: 2024-09-17T07:09:29+00:00In 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.
Published: 2024-09-17T07:09:29+00:00Published: 2024-09-12T15:03:29+00:00
Breakdown of single-cell data
Published: 2024-07-26T17:22:16+00:00We 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.
Published: 2024-07-12T00:00:00+00:00Workflow 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.
Published: 2024-07-11T14:34:09+00:00Single-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.
Published: 2024-07-11T14:34:09+00:00With 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.
Published: 2024-06-13T00:00:00+00:00You’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.
Published: 2024-04-09T08:31:24+00:00AnnData to SCE format conversion (manually using Galaxy buttons)
Published: 2024-02-13T12:28:05+00:00AnnData to Seurat format conversion (manually using Galaxy buttons)
Published: 2024-02-13T12:28:05+00:00AnnData to CDS format conversion (manually using Galaxy buttons). This workflow does not include renaming the column containing gene symbols.
Published: 2024-02-13T12:28:05+00:00You 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?
Published: 2024-02-13T12:28:05+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!✨
Published: 2023-12-22T00:00:00+00:00Creates input file for Filter, Plot, Explore tutorial
Published: 2023-12-14T16:16:41+00:00The 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.
Published: 2023-12-13T18:43:30+00:00Setting up the environment
Published: 2023-12-08T14:46:22+00:00Introduction
Published: 2023-12-08T07:20:20+00:00Workflow based on clustering 3K PBMCs with Scanpy tutorial
Published: 2023-12-05T14:45:06+00:00Introduction
Published: 2023-11-14T17:16:58+00:00The 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.
Published: 2023-10-17T12:50:06+00:00What is Cell Annotation?
Published: 2023-09-04T08:24:00+00:00The 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.
Published: 2023-07-19T06:47:23+00:00Filter, Plot and Explore Single-cell RNA-seq Data
Published: 2023-06-13T11:38:52+00:00Trajectory analysis using Monocle3, starting from AnnData
Published: 2023-05-16T08:09:55+00:00Similar 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.
Published: 2023-04-24T18:23:20+00:00Single-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.
Published: 2023-01-25T09:43:32+00:00This 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.
Published: 2023-01-20T10:58:39+00:00This workflow creates bulk ESet objects from uploaded raw matrix & metadata files
Published: 2023-01-20T10:58:39+00:00This workflow generates from only an EBI SCXA reference the metadata for creating an ESet object
Published: 2023-01-20T10:58:39+00:00This workflow creates an ESet object from scRNA metadata file and EBI SCXA retrieveal
Published: 2023-01-20T10:58:39+00:00After 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!
Published: 2023-01-20T10:58:39+00:00The 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).
Published: 2023-01-20T10:58:39+00:00After 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!
Published: 2023-01-20T10:58:39+00:00The 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.
Published: 2022-11-29T00:00:00+00:00Published: 2022-11-18T15:34:02+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
Published: 2022-11-18T00:00:00+00:00Trajectory analysis using Monocle3, starting from 3 input files: expression matrix, gene and cell annotations
Published: 2022-09-30T20:06:34+00:00Preparing and filtering gene and cell annotations files and expression matrix to be passed as input for Monocle
Published: 2022-09-30T20:06:34+00:00What is trajectory analysis?
Published: 2022-09-30T20:06:34+00:00This 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.
Published: 2022-09-30T20:06:34+00:00Check your tool version, you need to use 1.3.0+galaxy2
Published: 2022-09-08T14:00:12+00:00Este tutorial te ayudará a pasar de datos crudos de secuenciación en archivos FASTQ a una matriz de datos en formato AnnData donde cada célula es una fila y cada gen es una columna. Pero, ¿Qué es una matriz de datos y cuál es el formato AnnData? Lo averiguaremos a su debido tiempo. Enfatizamos que este es el primer paso para procesar datos de secuenciación de células únicas para poder realizar su análisis.
Published: 2022-09-08T14:00:12+00:00This 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.
Published: 2022-09-08T14:00:12+00:00Try a different colour palette. For upstream code reasons, the default color palette sometimes causes the tool to error out.
Published: 2022-03-02T15:46:20+00:00No, it really depends on the protocol. In some protocols this convention is swapped, in others the barcodes can be distributed across both reads.
Published: 2022-03-02T15:46:20+00:00Published: 2022-03-02T15:46:20+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!)
Published: 2022-02-28T15:26:33+00:00Try selecting “Use raw attributes if present: NO”
Published: 2022-02-28T15:26:33+00:00Try selecting: “Use programme defaults: Yes” and see if that fixes it.
Published: 2022-02-28T15:26:33+00:00Check your tool version, you need to use 1.3.0+galaxy2
Published: 2022-02-28T15:26:33+00:00Bulk RNA Deconvolution with MuSiC
Published: 2022-02-11T12:34:18+00:00Bulk 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.
Published: 2022-02-11T12:34:18+00:00Not 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.
Published: 2021-11-17T06:38:37+00:00The 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.
Published: 2021-11-11T16:57:25+00:00The 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.
Published: 2021-11-11T16:57:25+00:00Think 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.
Published: 2021-11-11T16:57:25+00:00The 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.
Published: 2021-11-11T16:57:25+00:00Due 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.
Published: 2021-11-11T16:57:25+00:00Single-cell RNA-seq
Published: 2021-10-19T15:23:36+00:00RNA-seq de una sola célula
Published: 2021-10-19T15:12:12+00:00Introducción
Published: 2021-10-07T15:52:06+00:00Este tutorial te ayudará a pasar de datos crudos de secuenciación en archivos FASTQ a una matriz de datos en formato AnnData donde cada célula es una fila y cada gen es una columna. Pero, ¿Qué es una matriz de datos y cuál es el formato AnnData? Lo averiguaremos a su debido tiempo. Enfatizamos que este es el primer paso para procesar datos de secuenciación de células únicas para poder realizar su análisis.
Published: 2021-08-31T20:57:30+00:00Downstream Single-cell RNA Plant analysis with ScanPy
Published: 2021-04-08T10:59:53+00:00Single 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.
Published: 2021-04-08T10:59:53+00:00Run the tutorial!
Published: 2021-04-07T14:04:47+00:00Single 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.
Published: 2021-03-30T00:00:00+00:00Updated tool versions Aug 24 2022
Published: 2021-03-24T11:32:22+00:00You’ve done all the work to make a single cell matrix, with gene counts and mitochondrial counts 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 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, because currently Galaxy hosts the most Scanpy tools of all of those options.
Published: 2021-03-24T11:32:22+00:00Updated March 2024
Published: 2021-03-03T13:21:27+00:00This workflow generates a single cell matrix using Alevin.
Published: 2021-03-03T13:21:27+00:00This 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. Currently you have a bunch of strings of ATGGGCTT etc. in your sequencing files, and what you need to know is how many cells you have and what genes appear in those cells. These steps are the most computationally heavy in the single cell world, as you’re starting with 100s of millions of reads, each with 4 lines of text. Later on in analysis, this data becomes simple gene counts such as ‘Cell A has 4 GAPDHs’, which is a lot easier to store! Because of this data overload, we have downsampled the FASTQ files to speed up the analysis a bit. Saying that, you’re still having to map loads of reads to the massive murine genome, so get yourself a cup of coffee and prepare to analyse!
Published: 2021-03-03T13:21:27+00:00Single-cell RNA-seq
Published: 2021-01-29T16:45:13+00:00Fixed Barcode Protocols and Multiplexing
Published: 2020-03-17T16:28:35+00:00Single Cell RNA Pre-processing
Published: 2020-02-28T16:35:17+00:00Single-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.
Published: 2019-12-19T19:40:33+00:00Single-cell quality control with scater
Published: 2019-10-23T18:48:12+00:00Single-cell RNA-seq (scRNA-seq) is emerging as a promising technology for analysing variability in cell populations. However, the combination of technical noise and intrinsic biological variability makes detecting technical artefacts particularly challenging. Removal of low-quality cells and detection of technical artefacts is critical for accurate downstream analysis.
Published: 2019-10-23T18:48:12+00:00Single-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.
Published: 2019-09-11T13:07:03+00:00Downstream Single-cell RNA analysis with RaceID
Published: 2019-03-25T16:14:31+00:00Pre-processing of Single-Cell RNA Data
Published: 2019-02-22T19:53:50+00:00Pre-processing of Single-Cell RNA Data
Published: 2019-02-22T19:53:50+00:00Introduction
Published: 2019-02-22T19:53:50+00:00Barcodes are small oligonucleotides that are inserted into the captured sequence at a specific point, and provide two pieces of information about the sequence:
Published: 2019-02-20T18:33:11+00:00Sorting Plates
Published: 2019-02-16T20:04:07+00:00