Learning path 03 · Beginner to advanced
Make sense of omics data
Choose a biological question and learn one analysis end to end. Bulk analysis, single-cell analysis, and other omics specialisms share foundations, but each needs its own experimental and statistical context.
Put it into practiceYour first RNA-seq comparison →BeginnerSequencing and bulk RNA-seq
Connect read processing and quantification to sample-level statistical analysis.
Before you begin: Basic R or a Galaxy introduction; statistics and sample metadata.
What to understand
- Read QC, alignment or quantification, references, and gene-level counts.
- Sample-level QC, design formulas, contrasts, and differential expression.
- Effect sizes, multiple testing, gene-set interpretation, and reporting.
What you’ll be able to do
Produce a reproducible comparison with a clear design, QC plots, and an interpreted results table.
Try this
Start with the count-based RNA-seq project. Then follow a full FASTQ-to-results tutorial to learn where those counts come from.
Keep in mind
A count-based tutorial does not teach raw-read processing by itself. Keep normalization for visualization distinct from the model's input requirements.
A few good resources
- Bioconductor: gene-level RNA-seq workflow ↗
Connect RNA-seq processing with exploratory analysis and differential expression through a documented workflow.
Workflow · Intermediate · Free · English - DESeq2 vignette ↗
Understand count-based differential expression, model design, contrasts, diagnostics, and interpretation.
Documentation · Intermediate · Free · English - Galaxy Training Network ↗
Hands-on biological analysis tutorials across sequencing, proteomics, metabolomics, and other domains.
Tutorial · Beginner · Training free; server quotas vary · English - nf-core/rnaseq ↗
A production workflow to examine after a smaller tutorial; learn its inputs, profiles, QC outputs, and documented options.
Workflow · Intermediate · Code free; compute and storage may cost · English
IntermediateSingle-cell RNA-seq foundations
Learn the core workflow before adding integration or complex models.
Before you begin: R or Python, count matrices, and experimental design.
What to understand
- Cell and sample QC, ambient RNA, doublets, and normalization.
- Dimensionality reduction, clustering, marker evidence, and cell annotation.
- Donor-aware comparisons, pseudobulk approaches, and differential abundance.
What you’ll be able to do
Explain the evidence for an annotation and the experimental unit for a comparison.
Try this
Follow one small tutorial in Scanpy or Seurat. Keep a table recording every filtering decision and inspect how one changed threshold affects the result.
Keep in mind
Clusters are analytical groupings, and annotation requires biological evidence. Preserve donor metadata throughout the workflow.
A few good resources
- Single-cell Best Practices ↗
A question-oriented reference for single-cell analysis across modalities, including preprocessing, statistics, and integration.
Book · Intermediate · Free online · English - Scanpy tutorials ↗
Work through Python single-cell analysis examples and inspect how preprocessing decisions affect downstream results.
Tutorial · Intermediate · Free · English - Seurat guided clustering tutorial ↗
An R entry point to single-cell QC, normalization, dimensionality reduction, clustering, and marker exploration.
Tutorial · Intermediate · Free · English - AnnData documentation ↗
Understand the annotated matrix structure used in many Python single-cell workflows, including observations, variables, and layers.
Documentation · Intermediate · Free · English
AdvancedDataset integration and scvi-tools
Address technical differences while checking that meaningful biological variation is retained.
Before you begin: A complete single-cell workflow, covariates, and batch-effect concepts.
What to understand
- Integration across datasets versus integration across molecular modalities.
- Reference mapping, batch covariates, latent representations, and scVI/scANVI.
- Evaluation of biological conservation alongside technical mixing.
What you’ll be able to do
Justify an integration strategy and describe how you checked its consequences.
Try this
Compare an unintegrated baseline with one integration method. Inspect known biological groups and sample composition as well as a low-dimensional plot.
Keep in mind
A well-mixed embedding is not sufficient evidence of a good integration. Confounded study designs can make technical and biological effects difficult to separate.
A few good resources
- scvi-tools tutorials and model guides ↗
Choose probabilistic models by task and modality, and read their assumptions alongside the worked tutorials.
Documentation · Advanced · Free; compute may cost · English - Single-cell Best Practices ↗
A question-oriented reference for single-cell analysis across modalities, including preprocessing, statistics, and integration.
Book · Intermediate · Free online · English
AdvancedCITE-seq and multimodal analysis
Relate RNA, protein, and chromatin measurements while respecting how they were collected.
Before you begin: Single-cell foundations and basic integration concepts.
What to understand
- CITE-seq RNA and antibody-derived tags; protein background and modality-specific QC.
- Paired versus unpaired measurements, missing modalities, and MuData.
- totalVI for RNA–protein data and MultiVI for RNA–chromatin accessibility data.
What you’ll be able to do
Choose a method whose assumptions match the modalities and pairing in the experiment.
Try this
Follow the totalVI CITE-seq tutorial. Compare RNA and protein evidence for selected markers and document cases where they disagree.
Keep in mind
Multi-omics is broader than single-cell RNA–protein analysis. Describe each modality and its measurement process before selecting an integration tool.
A few good resources
- totalVI: RNA and protein analysis ↗
A model guide for joint RNA–protein analysis, with links to CITE-seq examples and discussion of the observation model.
Guide · Advanced · Free; compute may cost · English - MultiVI: RNA and chromatin accessibility ↗
Study joint RNA–chromatin accessibility modeling, including the paired and unpaired data setting.
Guide · Advanced · Free; compute may cost · English - MuData documentation ↗
Understand how multimodal observations and modality-specific matrices are organized and linked.
Documentation · Intermediate · Free · English - Single-cell Best Practices ↗
A question-oriented reference for single-cell analysis across modalities, including preprocessing, statistics, and integration.
Book · Intermediate · Free online · English
AdvancedSingle-cell perturbation analysis
Study how cells respond to genetic, chemical, or environmental interventions.
Before you begin: Single-cell foundations, experimental controls, and sample-level inference.
What to understand
- Perturb-seq design, guide assignment, controls, and perturbation efficacy.
- Differential expression, differential abundance, and heterogeneous responses.
- Observed-response analysis versus prediction of unseen responses; held-out evaluation.
What you’ll be able to do
Define an appropriate comparison and explain what a model evaluation does and does not test.
Try this
Use a Pertpy tutorial to compare perturbations with controls. Record replicate structure, guide assignment choices, and the intended generalization task.
Keep in mind
Random cell-level splits can fail to test the generalization you care about. Design evaluation around held-out donors, conditions, or perturbations as appropriate.
A few good resources
- Pertpy: perturbation analysis ↗
Explore guide assignment, perturbation efficacy, response comparisons, and prediction through documented examples.
Tutorial · Advanced · Free; compute may cost · English - Single-cell Best Practices ↗
A question-oriented reference for single-cell analysis across modalities, including preprocessing, statistics, and integration.
Book · Intermediate · Free online · English - scvi-tools tutorials and model guides ↗
Choose probabilistic models by task and modality, and read their assumptions alongside the worked tutorials.
Documentation · Advanced · Free; compute may cost · English
AdvancedTCR, BCR, and immune repertoires
Connect immune receptor sequences, clonotypes, and cellular phenotypes.
Before you begin: Basic immunology and single-cell data handling.
What to understand
- V(D)J sequences, chain pairing, productive receptors, and clonotype definitions.
- Clonal expansion, diversity, overlap, and sampling depth.
- Linking repertoire information to RNA expression and cell states.
What you’ll be able to do
Explain a clonotype definition and distinguish clonal relationships from evidence of antigen specificity.
Try this
Complete a Scirpy tutorial and compare two reasonable clonotype definitions. Report how the interpretation changes.
Keep in mind
Receptor similarity or clonal expansion alone does not establish shared antigen specificity. Document sequence and grouping criteria.
A few good resources
- Scirpy: immune receptor analysis ↗
Learn repertoire QC, clonotype analysis, and integration of receptor information with cell-level measurements.
Tutorial · Advanced · Free · English - AIRR Community documentation ↗
Use community terminology, data standards, and metadata guidance when handling receptor repertoire datasets.
Documentation · Intermediate · Free · English
IntermediateOther omics and directions to explore
Keep the wider field visible while choosing a tractable next specialization.
Before you begin: Biological data literacy and statistics.
What to understand
- Variant analysis, genome annotation, and reference-dependent interpretation.
- Proteomics and metabolomics: experimental platforms, quantification, and data processing.
- Spatial omics: tissue context, spatial dependence, and assay-specific analysis.
What you’ll be able to do
Choose a specialization and find an introductory workflow appropriate to its data.
Try this
Choose one Galaxy learning pathway and map its raw data, processing stages, biological question, and outputs.
Keep in mind
This is an orientation to additional fields, not a complete curriculum for each one. Use the specialist training materials for detailed methods.
A few good resources
- Mass spectrometry basics — Lennart Martens ↗
An introduction to mass spectrometry for proteomics, starting with amino acids and proteins and covering ion sources, analyzers, detectors, tandem mass spectrometry, and peptide fragmentation.
Video series · Beginner · Free · English - Galaxy Training Network ↗
Hands-on biological analysis tutorials across sequencing, proteomics, metabolomics, and other domains.
Tutorial · Beginner · Training free; server quotas vary · English - EMBL-EBI on-demand training ↗
Find focused introductions to biological databases and analysis areas, including routes into additional omics fields.
Course · Beginner · Free · English - Ensembl training ↗
Learn to navigate genome annotation, identifiers, comparative data, and Ensembl tools using worked training materials.
Tutorial · Beginner · Free · English - Single-cell Best Practices ↗
A question-oriented reference for single-cell analysis across modalities, including preprocessing, statistics, and integration.
Book · Intermediate · Free online · English