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Spatial Biology

Spatial biology measures molecules in situ across tissues — spatial transcriptomics (Visium, Xenium, MERFISH, Stereo-seq), spatial proteomics (CODEX, IMC, CosMx) and multi-modal atlases. It links cellular identity with tissue architecture, neighborhoods and disease microenvironments.

VisiumXeniumMERFISHSquidpy
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Default learning track

4 phases · ~16–22 weeks

A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.

1

Phase 1 — Foundations

3–4 weeks 30–40 hours

Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.

Prerequisites

  • Comfortable with basic biology / health-science vocabulary
  • Ability to install software and use a terminal

Skills you'll build

Python or R basicsLinux shellGit/GitHubReading scientific literature

Hands-on projects

Reproducible analysis notebook

Walk through a published tutorial in Squidpy / Seurat-spatial + Scanpy and reproduce its results on the provided sample data.

Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README

Milestones

  • Set up reproducible Conda/Mamba environment and a public GitHub repo
  • Read and summarize 3 review papers covering the sector landscape
  • Complete an intro statistics or scripting course end-to-end
Citation · documentation
Seurat v5 spatial vignettes
Satija Lab
Why: Exact sector match (Spatial Biology)
Dataset · tool
Squidpy — spatial single-cell analysis
scverse / Theis Lab
Why: Exact sector match (Spatial Biology)
2

Phase 2 — Core tools & datasets

4–6 weeks 60–80 hours

Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.

Prerequisites

  • Completed Phase 1 reproducible notebook
  • Working Python/R environment

Skills you'll build

Squidpy / Seurat-spatial + ScanpyData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on 10x Genomics Spatial datasets + HuBMAP + STOmics

Pick one study from 10x Genomics Spatial datasets + HuBMAP + STOmics, reproduce the headline result, and write a short technical note on what you found.

Deliverable: GitHub repo + 3-page PDF write-up

Milestones

  • Run the official Squidpy / Seurat-spatial + Scanpy tutorial end-to-end on real data
  • Download and explore one full dataset from 10x Genomics Spatial datasets + HuBMAP + STOmics
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · documentation
MERFISH / Vizgen resources
Vizgen
Why: Exact sector match (Spatial Biology)
Dataset · dataset
10x Genomics Visium / Xenium datasets
10x Genomics
Why: Exact sector match (Spatial Biology)
3

Phase 3 — Applied projects

6–8 weeks 80–120 hours

Move from tutorials to original analyses on real questions. Start showing your work publicly.

Prerequisites

  • Phase 2 dataset deep-dive complete
  • Comfortable with the sector's primary tooling

Skills you'll build

End-to-end pipeline designStatistical interpretationScientific writingReproducible reports

Hands-on projects

Spatial niche-analysis re-study

Re-analyze a public Visium or Xenium tissue dataset, identify cellular niches, and compare neighborhoods between regions/conditions.

Deliverable: Reproducible repo + niche figures + 4-page brief

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with the scverse spatial and HuBMAP community (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · documentation
CODEX / PhenoCycler workflows
Akoya Biosciences
Why: Exact sector match (Spatial Biology)
Dataset · dataset
HuBMAP — Human BioMolecular Atlas Program
NIH HuBMAP
Why: Exact sector match (Spatial Biology)
4

Phase 4 — Portfolio & career launch

3–5 weeks 30–50 hours

Package your work, target real roles, and prepare to interview in the sector.

Prerequisites

  • Phase 3 capstone project shipped
  • 2+ public repos under version control

Skills you'll build

Technical CVPortfolio siteInterview prep (case studies + technical questions)Networking

Hands-on projects

Job-ready portfolio package

Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.

Deliverable: Live portfolio URL + PDF CV + cover letter template

Milestones

  • Publish a portfolio site or pinned GitHub README linking to 2–3 projects
  • Tailor CV to 3 real job ads in the sector and submit applications
  • Practice 5 mock technical interviews with sector-specific case studies
Citation · course
Coursera · Single-Cell & Spatial-flavored courses
Coursera
Why: Exact sector match (Spatial Biology)
Dataset · dataset
STOmics (BGI Stereo-seq) data portal
BGI
Why: Exact sector match (Spatial Biology)

Curated learning resources

Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.

Courses

Coursera · Single-Cell & Spatial-flavored courses
Coursera
Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

Seurat v5 spatial vignettes
Satija Lab
MERFISH / Vizgen resources
Vizgen
CODEX / PhenoCycler workflows
Akoya Biosciences
Git & GitHub fundamentals
GitHub Docs

Tools

Squidpy — spatial single-cell analysis
scverse / Theis Lab
Giotto Suite — spatial omics analysis
Ruben Dries Lab

Datasets

10x Genomics Visium / Xenium datasets
10x Genomics
HuBMAP — Human BioMolecular Atlas Program
NIH HuBMAP
STOmics (BGI Stereo-seq) data portal
BGI

Books

R for Data Science (2e)
Hadley Wickham et al.

Communitys

scverse — Python single-cell community
scverse

Typical career paths

Spatial Omics Scientist

Runs and analyzes spatial transcriptomics/proteomics experiments and integrates them with scRNA-seq.

Spatial Biology Bioinformatician

Builds pipelines in Squidpy / Giotto / Seurat-spatial and develops neighborhood and niche-analysis methods.

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