Bioinformatics and omics

Single-Cell Analysis interview questions

Single-cell experimental biases, clustering, annotation and integration.

6 questions, each with a worked answer

Why can batch effects distort a single-cell RNA-seq analysis? Fundamentals Concept
How would you identify potential doublets and low-quality cells? Intermediate Scenario
Why should donors rather than individual cells determine biological replication? Intermediate Concept
How would you evaluate whether integrated clusters retain treatment-associated biology? Intermediate Scenario
What is ambient RNA and how can it affect cell-type annotation? Intermediate Scenario
How do you distinguish a new cell state from a doublet population? Advanced Concept

About Single-Cell Analysis

Single-cell analysis measures molecules, most often RNA, in individual cells rather than averaged across a sample. It reveals cell types, states and transitions that bulk measurements hide. The data are sparse and noisy, so analysis involves quality filtering, normalisation, clustering, annotation and integration across samples.

Technical effects such as dissociation stress, doublets and batches can look like biology if they are not handled.

Fundamentals: 1
Intermediate: 4
Advanced: 1

What these questions test

Analyze single-cell data: Check cell quality, batches, doublets and annotation uncertainty.

References

NCBI Bookshelf. National Library of Medicine. ncbi.nlm.nih.gov/books.
pmc.ncbi.nlm.nih.gov/articles/PMC11374959.
sc-best-practices.org/preprocessing-visualization/quality-control.
sc-best-practices.org/conditions/differential-gene-expression.

Practice material written by TheBench.Bio and checked against the references above, reviewed October 23, 2026. Written to practise with, not collected from real interviews.