Designing a Biomedical Research Project

Designing a Biomedical Research Project

Every successful computational study in biomedicine begins long before the first command is typed. It begins with a well-posed question. In this course, you will move from the command line to single-cell analysis, but the skills that determine whether your work is publishable, reproducible, and meaningful are established at the design stage. For pharmacy and biotech students, this is especially important: your questions often carry translational weight, such as predicting which patients respond to a drug or identifying resistance mechanisms in a cell line model. A vague question produces vague data, no matter how sophisticated the downstream analysis. A well-designed project starts with a falsifiable hypothesis. Rather than "I want to study cancer drug resistance," write "Inhibition of kinase X reduces viability of resistant AML cell lines by at least 30% compared to sensitive lines." From the hypothesis, derive your variables: the independent variable (treatment condition), dependent variables (viability, gene expression), and critically, the variables you must control (passage number, confluency, batch, donor sex). Confounders that are not planned for at design time cannot be corrected computationally later, and batch effects remain the most common reason a transcriptomic dataset fails replication. Once the biological design is settled, plan the computational architecture. Decide early where raw data will live, how it will be processed, and what the final outputs will be. A standard convention is to keep raw data immutable and read-only, separate code from results, and document every decision in a README. The scaffold below is a habit worth building now, because every project in this course, from simple shell scripts to single-cell pipelines, will follow it. # Create a reproducible project scaffold mkdir -p drug-response-project/{data/raw,data/processed,code,results,docs} cd drug-response-project # Raw data is never modified; all transformations write to processed/ touch data/raw/.gitkeep # Document the project before running anything cat > README.md << 'EOF' # Compound X resistance in AML cell lines ## Hypothesis Kinase X inhibition reduces viability of resistant lines by >= 30%. ## Design - 3 resistant + 3 sensitive lines, 3 biological replicates each - Primary endpoint: IC50; secondary: differential gene expression EOF # Track every change from day one git init git add README.md git commit -m "Initial project design" The next design decision is sample size, and it should be answered with statistics, not intuition. A power analysis asks: given an expected effect size, how many biological replicates do I need to detect that effect with acceptable confidence? Underpowering wastes months of lab work; overpowering wastes budget and animals or reagents. Note the word biological replicates: three technical replicates of one cell line answer a completely different question than three independent lines, and confusing the two is the single most common statistical error in biomedical research. The script below computes the required sample size for a two-group comparison. from statsmodels.stats.power import TTestIndPower analysis = TTestIndPower() # Design parameters chosen BEFORE the experiment effect_size = 0.8 # Cohen's d: expected difference / expected SD alpha = 0.05 # acceptable false-positive rate power = 0.80 # probability of detecting a true effect ratio = 1.5 # 1.5 controls per treated sample (cost asymmetry) n_treated = analysis.solve_power( effect_size=effect_size, alpha=alpha, power=power, ratio=ratio, alternative="two-sided", ) print(f"Treated samples needed: {n_treated:.0f}") print(f"Control samples needed: {n_treated * ratio:.0f}") Finally, write your analysis plan before seeing any data: which statistical tests, which correction for multiple comparisons, which visualization, and what result would falsify your hypothesis. This mirrors the preregistration practice used in clinical research and protects you from unconsciously tuning the analysis until the answer looks favorable. In this course, every dataset you meet, from FASTQ files to single-cell matrices, will be analyzed inside a design framework like this one, and the projects that succeed are the ones where the design document was written first. Frame a narrow, testable, translational question before touching any data or code. State a falsifiable hypothesis and identify independent, dependent, and controlled variables explicitly. Distinguish biological from technical replicates; only the former support statistical inference about populations. Run a power analysis before the experiment to set sample size from effect size, alpha, and power. Keep raw data immutable, separate code from results, and version-control every decision. Write the analysis plan, including tests and multiple-comparison corrections, before results exist. Practice exercise: You have access to RNA-seq data from ten patient-derived colorectal cancer organoids, five resistant and five sensitive to a standard chemotherapy regimen. In a single paragraph, design the complete project: state a falsifiable hypothesis, list your independent and dependent variables plus at least two confounders you would control for, sketch the directory scaffold you would create (following the convention above), and explain what sample size you would need if you expected a moderate effect size (Cohen's d = 0.5) with power of 0.90 at alpha = 0.05, citing how you obtained that number.