--- title: "Single-Cell Analysis: Normalisation, Embedding and Naming Cell States" description: "A count matrix becomes a cell atlas through normalisation, feature selection, a linear reduction, a neighbour graph and a clustering step, each with choices that move the answer. This stop makes the c" canonical: https://lightmysky.com/learn/science/single-cell-analysis-normalisation-embedding-and-naming-cell-states-mt_diYHLaLmij source: https://lightmysky.com/learn/science/single-cell-analysis-normalisation-embedding-and-naming-cell-states-mt_diYHLaLmij.md retrieved: 2026-09-12 --- > **Agent view.** This is the Markdown twin of the page, for tools and assistants. > When to use this site, and the call that answers each job: https://lightmysky.com/agent-instructions.md > API description (OpenAPI 3.1): https://lightmysky.com/openapi.json · Authentication: https://lightmysky.com/auth.md > Pricing: https://lightmysky.com/pricing.md · Catalog: https://lightmysky.com/llms.txt · Full catalog: https://lightmysky.com/llms-full.txt > Every machine-readable file on this domain: https://lightmysky.com/.well-known/ai-catalog.json > Ask for Markdown with `Accept: text/markdown`, a `.md` address, or `?mode=agent`. # Single-Cell Analysis: Normalisation, Embedding and Naming Cell States A count matrix becomes a cell atlas through normalisation, feature selection, a linear reduction, a neighbour graph and a clustering step, each with choices that move the answer. This stop makes the choices explicit and tests how much the conclusion rests on them. Subject: Science · Area: Genetics & Evolution · Ages 23 to 24 Page: https://lightmysky.com/learn/science/single-cell-analysis-normalisation-embedding-and-naming-cell-states-mt_diYHLaLmij ## Ready when they can - Justify a normalisation and feature-selection choice for a stated dataset - Explain what a two-dimensional embedding preserves and what it distorts - Assign an identity to a cluster from marker genes and state the evidence a label needs ## Lesson: From soup of reads to named cell states Modern readers spell out millions of DNA fragments in parallel. In single-cell work each fragment carries a barcode tag that reveals which cell it came from. That tag turns a soup of reads into per-cell data. Read depth varies wildly between cells for purely technical reasons, from uneven capture to uneven amplification. Counts need scaling before any comparison means anything. Zeros flood the table because capture misses many real messages, so treat zeros as unknown rather than absent. Feature selection then keeps the genes that vary most across cells and sets the flat ones aside. When you hunt markers, keep genes that vary between groups and drop the flat ones: that feature selection step decides what the later analysis can see. **Example.** The two-dimensional picture that follows is a sketch for grouping, not a map with true distances. Rerun the reduction and clustering with different settings and watch which groups hold. Trust only the conclusions that survive the rerun. **Tip.** A cluster earns its name the hard way. Its marker genes must make sense, repeat across samples, and survive a second test like staining. A name without that backing is a guess wearing a lab coat. **Recap.** Tag reads to cells, scale counts, group with care, and name clusters only on tested markers. ## Practice 8 questions on this page, each with its working shown. ## Needs first - [Clustering: Structure Without Labels](https://lightmysky.com/learn/computing/clustering-structure-without-labels-mt_BAMGOlWgRY) - [Principal Components and Dimensionality Reduction](https://lightmysky.com/learn/computing/principal-components-and-dimensionality-reduction-mt_l-9XHg_ChZ) - [From a Gene List to Biology: Enrichment Tests and Their Backgrounds](https://lightmysky.com/learn/science/from-a-gene-list-to-biology-enrichment-tests-and-their-backgrounds-mt_M9eHEXq6iA) - [Single-Cell RNA Sequencing: From Droplets to a Count Matrix](https://lightmysky.com/learn/science/single-cell-rna-sequencing-from-droplets-to-a-count-matrix-mt_QCoqY7w_De) ## Opens up - [Modelling a Biological System: Rate Equations, Parameters and Fit](https://lightmysky.com/learn/science/modelling-a-biological-system-rate-equations-parameters-and-fit-mt_Al6sNjUOW5)