warming up your workspace

Computational Biology with R

Learn R by investigating living systems: audit experiments, work with genomes and RNA-seq, interpret pathways and variants, map microbiomes and cell populations, and assemble reproducible biological evidence.

8 projects, 200 hands-on levels, run in your browser.

Syllabus

  • Foundations: Code Through Living Data: Start R through the objects a computational biologist actually meets: sample identifiers, read counts, genes, quality flags, assay matrices, and metadata. You will learn functions, vectors, data frames, decisions, matrix summaries, validation, and reusable analysis while building a compact experiment reporter. No generic toy shop or unexplained statistics, every programming idea is attached to a biological measurement.
  • Can This Experiment Answer the Question?: Audit a synthetic treatment study before running statistics. The apparent treatment effect becomes less convincing when sample identity, replication, missing measurements, exclusions, and processing batches are examined. Build an experiment auditor that separates invalid input from repairable imbalance and from a design whose treatment effect is mathematically not identifiable.
  • Where in the Genome Did the Signal Land?: Investigate a synthetic regulatory assay whose peaks arrive as chromosome coordinates rather than tidy rows. Build trustworthy genomic intervals, distinguish overlap from proximity, preserve ambiguous evidence, measure coverage, respect strand-aware promoters, and produce a candidate-assignment table without pretending that proximity proves regulation.
  • Which Genes Changed, and Can We Trust It?: Investigate a synthetic RNA-seq perturbation study without turning a ranked gene list into a biological verdict. Prove sample identity and design rank, normalize count depth, fit an explicit DESeq2 contrast, distinguish multiple-testing evidence from moderated effect size, and publish a complete gene ledger that preserves filtered and insufficient results.
  • Which Biological Programs Are Overrepresented?: Investigate whether a differential gene set contains more members of declared pathways than expected from the genes that could have been selected. Define the measurable universe, compute one-sided hypergeometric evidence and effect ratios, control multiplicity, expose redundant terms and threshold sensitivity, and publish a pathway dossier that suggests follow-up without claiming a pathway was activated.
  • Which Variants Deserve Human Review?: Investigate a synthetic variant call set without turning software annotations into diagnoses. Validate reference and alternate alleles, expand multiallelic records, preserve sample-level depth and genotype evidence, reconcile many transcript consequences, combine population and clinical assertions cautiously, and publish a complete review dossier with conflicts and missing evidence visible.
  • How Did the Microbial Community Change?: Investigate a synthetic microbiome census without confusing read proportions with absolute abundance. Align feature counts, taxonomy, and sample context; audit depth and prevalence; construct relative and log-ratio views; measure within-sample and between-sample diversity; separate location from dispersion in group comparisons; and publish a community dossier that keeps compositional limits and design structure visible.
  • Which Cell Populations Are Really in the Sample?: Investigate a synthetic single-cell RNA-seq experiment from raw gene-by-cell counts to cautious population evidence. Assemble a SingleCellExperiment, keep cell-level QC decisions and batch context visible, normalize without rewriting counts, select variable genes, construct reproducible low-dimensional geometry, test cluster stability, summarize markers and donor support, and publish a dossier that never equates an algorithmic cluster with a proven cell type.

Key concepts

  • Assay: A structured collection of biological measurements. In this track, an assay is usually a matrix whose rows are features and whose columns are samples.
  • Batch effect: Systematic variation caused by when, where, or how samples were processed rather than by the biological condition of interest.
  • Bioconductor: An open-source R ecosystem for analysing and understanding high-throughput biological data.
  • Biological replicate: An independently sampled biological unit used to measure natural biological variation. Repeated measurements of the same unit are technical replicates, not add…
  • Condition: A biological or experimental group, such as treated and control, whose difference is part of the study question.
  • Confounding: A design problem in which the effect of one variable cannot be separated from another. If every treated sample is processed in one batch and every control in a…
  • Count matrix: A matrix of non-negative integer observations, commonly genes by samples for RNA-seq. Row and column identities are part of the data contract.
  • Feature: A measured biological entity such as a gene, transcript, genomic region, microbial taxon, or cell marker.
  • Library size: The total number of counts recorded for one sequencing sample. It reflects sequencing depth and can differ greatly between samples.
  • Metadata: Information that describes samples or features, including identifiers, conditions, batches, collection times, and annotations.
  • Missing value: An observation that is unavailable or unknown. R represents a missing value with NA ; it should not be silently replaced with zero.
  • Provenance: A record of where data came from and how it was transformed, including inputs, parameters, software versions, and decisions.
  • Quality control: A declared process for detecting invalid, unreliable, or unusual measurements before interpreting biological results.
  • Sample identifier: A stable key that links an assay column to exactly one sample record. Matching by identifier is safer than trusting row order.
  • SummarizedExperiment: A Bioconductor container that keeps one or more assays aligned with feature metadata and sample metadata.