--- title: "Single-cell analysis: normalisation, embedding and naming cell states" description: "Single-cell RNA analysis uses normalisation, feature selection, dimensional reduction and clustering to explore candidate cell groups. Check how analysis choices affect those groups, interpret embeddi" 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: 2026-11-12 --- > **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 Single-cell RNA analysis uses normalisation, feature selection, dimensional reduction and clustering to explore candidate cell groups. Check how analysis choices affect those groups, interpret embedding limits and record the evidence for provisional cell labels. 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 - Calculate total-count scaling under stated assumptions and choose a variable gene from supplied values - 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 In a droplet UMI assay, a barcode identifies a droplet. Quality checks assess whether its profile represents one usable cell. Counts estimate detected RNA molecules after grouping duplicate reads. A zero means no molecule of that gene was counted: RNA may be absent or may have escaped detection. A positive count can include ambient RNA or material from a doublet. Neither entry alone establishes cell identity. Cell totals reflect capture and sampling as well as biological RNA content. Total-count scaling divides each gene count by its cell total and multiplies by a shared target. It can help when totals differ mainly for technical reasons and expression proportions are broadly comparable. It cannot recover absolute RNA content or replace checks for doublets, ambient RNA and batch effects, which are differences tied to sample processing. **Example.** Assume two cells have the same RNA composition and differ only in capture. A gene has 15 of 150 counts in one cell and 30 of 300 in the other. Scale to 1,000: 15 / 150 × 1,000 = 100 and 30 / 300 × 1,000 = 100. Both assign one tenth of their counts to this gene. Matching scaled values do not prove equal amounts of RNA in real cells. For grouping, select genes with variation beyond expected technical noise, accounting for mean expression. A constant gene cannot separate cells by its values. Equal means can hide different variation, so high mean expression alone is not a selection rule. Marker testing is a later comparison between a cluster and other cells. Its eligible genes need not be restricted to the features used for grouping. *(drawing: This is one common workflow. Clustering and drawing a two-dimensional view are separate analysis tasks.)* In this workflow, PCA summarises processed expression values for selected genes using linear combinations called components. Each cell receives component scores. A neighbour graph links cells with similar scores under a chosen distance and neighbour setting. Graph clustering partitions those connections. It need not use the two coordinates displayed by UMAP. UMAP aims to retain local neighbourhood structure in a low-dimensional view, but can distort local relationships too. Its axes, island gaps and island sizes are not calibrated biological measures. A large gap alone does not establish different cell types. Check the analysis representation and marker evidence before interpreting a separation. Higher clustering resolution often makes finer partitions. Compare their markers, sample composition and membership across reasonable settings. A split may reflect a biological state or a technical effect. A broad identity can remain supported after splitting. Continuous variation need not form discrete types. Stability is supporting evidence, not proof of biology. Use several enriched markers and a tissue- and species-matched reference for a provisional label. If a reference links M1 and M2 to type R, their joint enrichment supports an R-like label. Repetition across samples and independent assays strengthen it; staining is one possible check. Pathway enrichment can describe marker functions using genes eligible for marker selection as background. It cannot establish identity alone. **Recap.** Check what counts measure, justify scaling and feature choices, interpret groups beyond their UMAP layout, and record evidence for provisional labels. *Lesson, questions, diagrams, worked solutions and flashcards written for this dataset.* ## Practice 8 questions on this page, each with its working shown. ## Needs first - [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) ## Optional connections - [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) ## 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)