Scientific Communication Hub

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Interactive Terminology Dictionary

Communication Templates

🧬 → 📊 Experiment Data Request

Subject: Statistical Analysis Request - [Project Name] Hi [Data Scientist Name], I need statistical analysis support for our [compound/target] study. Here are the experimental details: STUDY DESIGN: - Objective: [e.g., determine if compound X significantly improves cell viability] - Primary endpoint: [e.g., % cell viability at 72h] - Sample size: n=[X] per group, [Y] independent experiments - Controls: [positive control, negative control, vehicle control] - Treatment groups: [list concentrations/conditions] DATA DETAILS: - Data type: [continuous/categorical/time-to-event] - Expected effect size: [if known] - Statistical assumptions: [normal distribution? equal variances?] - Multiple comparisons: [comparing X groups to control] ANALYSIS NEEDS: - Primary question: [specific hypothesis to test] - Secondary analyses: [if any] - Significance level: [typically α = 0.05] - Deliverable: [summary report, presentation slides, etc.] The data is in [location/format]. Happy to discuss experimental design details. Thanks, [Your name]

📊 → 🧬 Analysis Results Summary

Subject: Analysis Results - [Project Name] Hi [Scientist Name], Here are the statistical analysis results for your [compound/study] data: BOTTOM LINE: [Main finding in plain language - e.g., "Compound X significantly improved cell viability compared to control"] KEY FINDINGS: - Primary result: [effect size and statistical significance] - Effect magnitude: [e.g., 2.3-fold increase, 95% CI: 1.8-2.9] - Statistical confidence: p = [value] (significant if p < 0.05) - Sample adequacy: [sufficient/insufficient power for detection] EXPERIMENTAL VALIDATION: - Controls performed as expected: [Yes/No, brief explanation] - Data quality: [good/concerns about outliers, variance, etc.] - Assumptions met: [normality, equal variance - any violations?] BIOLOGICAL INTERPRETATION: [What this means for your research question in biological terms] LIMITATIONS/CAVEATS: [Any statistical limitations, need for additional experiments, etc.] NEXT STEPS: [Recommendations for follow-up experiments or analyses] Data files and detailed statistical output attached. Best, [Your name]

🤝 Joint Project Planning Template

PROJECT: [Name] TEAM: [Scientists] + [Data Scientists] OBJECTIVE: [Clear statement of biological question and computational goals] EXPERIMENTAL PLAN: - Biological system: [cell line, assay, model] - Variables to test: [treatments, doses, time points] - Sample size: [based on power analysis if available] - Controls: [what will be included] - Timeline: [key milestones] DATA REQUIREMENTS: - Primary measurements: [what will be measured] - Data format: [how data will be collected/stored] - Quality controls: [replication, validation steps] - Metadata needed: [experimental conditions, batch info, etc.] ANALYSIS PLAN: - Statistical methods: [tests to be used] - Success criteria: [how to define positive results] - Visualization needs: [plots, dashboards, reports] - Deliverables: [what outputs are expected] COMMUNICATION: - Check-in frequency: [weekly/bi-weekly meetings] - Data sharing: [where/how data will be shared] - Decision points: [when to modify approach] ROLES: Scientists: [specific responsibilities] Data Scientists: [specific responsibilities] Shared: [joint activities]

Communication Examples

Data Analysis Requests

❌ Problematic

"Can you analyze this data for significance? The results look promising."

✅ Clear

"Can you perform statistical analysis to determine if the 2.3-fold increase in binding affinity vs. control is statistically significant? We have n=6 per group across 3 independent experiments. Need to account for multiple comparisons since we tested 5 compounds."

Presenting Computational Results

❌ Confusing

"The model has 73% accuracy with precision of 0.85 and recall of 0.62. The AUC is 0.78."

✅ Actionable

"Our screening model correctly identifies 73% of compounds overall. When it predicts a compound is active, it's right 85% of the time (few false positives). However, it misses 38% of truly active compounds (more false negatives). This means it's good for prioritizing hits but you may want to test some 'negative' predictions too."

Describing Experimental Results

❌ Vague

"We saw good activity with nice dose-response curves. The compound was quite potent and selective."

✅ Quantitative

"We observed dose-dependent inhibition with IC50 = 2.3 μM (95% CI: 1.8-2.9 μM, n=9). The dose-response curve fit well (R² = 0.94). Compound showed 15-fold selectivity vs. closest off-target (IC50 = 35 μM)."

Meeting Communication

❌ Unclear

Scientist: "The correlation isn't great."
Data Scientist: "The model needs more features."

✅ Specific

Scientist: "The correlation between binding affinity and cell activity is r=0.43, weaker than we hoped."
Data Scientist: "We need additional molecular descriptors beyond just binding data to improve prediction accuracy."

Collaboration Workflows

🧬 How to Request Statistical Analysis

Define your research question clearly
Describe experimental design: sample sizes, controls, replicates
Specify the data: what was measured, how, when
State your hypothesis: what difference do you expect?
Mention constraints: timeline, resources, follow-up plans
Provide context: why this analysis matters to the project
Share raw data in agreed format

📊 How to Present Results to Scientists

Start with the bottom line: answer their research question first
Explain biological meaning: what do numbers mean for experiments?
Show confidence level: how certain are you of results?
Highlight assumptions: what could affect interpretation?
Suggest next steps: what experiments would help?
Provide raw output: detailed stats for their records
Make visualizations: plots that tell the story

🤝 Planning Joint Projects

📋 Pre-Project

Align on biological question
Define success criteria
Plan experimental design together
Agree on data formats
Set communication schedule

🔬 During Experiments

Share data regularly
Flag quality issues early
Adjust analysis as needed
Validate unexpected results
Document decisions

📊 Post-Analysis

Review results together
Validate biological interpretation
Plan follow-up experiments
Document lessons learned
Share with broader team

FAQ: Understanding Data Science for Scientists

Common questions from bench scientists about computational and statistical concepts in drug discovery.

What does "correlation doesn't imply causation" mean for my experiment?

When we find that two measurements are correlated (e.g., higher binding affinity correlates with better cell activity), it doesn't prove that one causes the other. There could be:

Common cause: Both could be caused by a third factor (like compound solubility)
Reverse causation: Maybe cell activity affects binding measurement
Confounding variables: Other molecular properties driving both effects

For experiments: To establish causation, you'd need controlled experiments where you manipulate binding (e.g., through specific mutations) and measure the effect on cell activity.

How do I know if my sample size is adequate for statistical analysis?

Quick rules of thumb:

For detecting large effects: n=6-8 per group often sufficient
For smaller effects: n=15+ per group may be needed
For multiple comparisons: Add 20-30% more samples

Better approach: Power analysis before the experiment. Tell your data scientist:

What effect size you want to detect (e.g., 2-fold difference)
How variable your assay typically is
How confident you want to be (usually 80-90% power)

What experimental details do data scientists need to know?

Always include:

Sample sizes: How many replicates, independent experiments
Controls: What controls were included, expected results
Randomization: How treatments were assigned
Blinding: Who knew which samples were which
Batch effects: Were experiments done on different days/plates
Quality metrics: Any samples excluded and why

Why it matters: These details affect which statistical tests are appropriate and how to interpret results.

When should I be concerned about multiple testing corrections?

Apply corrections when:

Testing multiple compounds against the same control
Testing the same compound at multiple time points
Testing multiple endpoints in the same experiment
Comparing multiple treatment groups to each other

Don't overcorrect: If you have one primary hypothesis and several secondary analyses, you might only correct the secondary ones.

Discuss with your data scientist: The appropriate correction depends on your experimental goals and how the tests relate to each other.

What does "the model overfitted" mean for my drug discovery project?

In simple terms: The computational model memorized the training compounds too well and won't predict new compounds accurately.

Why it happens:

Too few training compounds for model complexity
Training compounds too similar to each other
Model learned noise rather than real patterns

For your project: You'll need either more diverse training compounds or a simpler model. The current model predictions for new compounds may not be reliable.

Bridge the Communication Gap