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R Foundations through Computational Biology

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.

First Observations

  1. Give a sample a readable label
  2. Count every sequenced read
  3. Measure how much data mapped
  4. Express composition as a percentage
  5. Mini-project: capture a sample snapshot

Biological Variables

  1. Select genes without losing order
  2. Compute expression change as a vector
  3. Turn measurements into logical evidence
  4. Handle a missing measurement honestly
  5. Mini-project: build a sample record

Quality Decisions

  1. Decide whether sequencing depth passes
  2. Reject invalid mapping fractions
  3. Combine quality rules into a status
  4. Keep every reason a sample failed
  5. Mini-project: apply one QC policy

Assay Matrices

  1. Measure every sample's library size
  2. Count detected genes per sample
  3. Keep genes supported across samples
  4. Compare conditions gene by gene
  5. Mini-project: summarize an assay

Reusable Analysis

  1. Reject an impossible count matrix
  2. Align metadata to assay columns
  3. Transform counts without losing zeros
  4. Rank genes by variation
  5. Capstone: build an experiment reporter

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