How did wet-lab biologists learn ML and win PI buy-in?

[Kenneth29]

I'm a postdoc in molecular biology, and our lab is generating massive, complex datasets from single-cell sequencing that traditional statistical methods struggle to analyze effectively. We're considering applying machine learning in scientific research for tasks like cell type classification and predicting gene regulatory networks, but none of us have a strong background in ML. For researchers who have successfully integrated ML into wet-lab disciplines, what was your learning pathway and what specific algorithms or tools proved most accessible and interpretable for biological data? How did you address the skepticism from senior PIs who view ML as a 'black box,' and what are the common pitfalls in applying these models to noisy, high-dimensional experimental data where sample sizes can be limited?

[JosephMG]

You're diving into a tough but rewarding transition. A practical way to start is with simple, well-understood models before jumping to fancy deep nets. Begin with a baseline logistic regression with small, interpretable features, then add a tree-based model (Random Forest or XGBoost) to see if you gain predictive power without losing interpretability. For single-cell data, practice on public datasets (e.g., PBMC) using Scanpy to reproduce a standard task like cell-type classification or clustering. If you’re curious about Bayesian ideas, scVI is a good entry point: it’s a probabilistic model that gives you cell embeddings with uncertainty estimates instead of a single point. When you present results to PIs or stakeholders, emphasize actionable findings, not just accuracy: calibration, posterior uncertainty, and what features drive the model (via SHAP or local explanations). Start with a concrete biological question and show how the model informs it, then iterate.

[Jerry_R]

Address skepticism by anchoring ML in biology questions and a clear end-to-end workflow. Use simple baselines first, then progressively add complexity. Track calibration and predictive intervals, not just point estimates. Create lightweight model cards that spell out assumptions, data sources, and limitations. Do ablation studies to show what each component adds. If you can, run a small replication across another dataset; cross-dataset validity is powerful against 'black box' critiques. Where possible, use hierarchical models to borrow strength across cell types or donors and show how priors stabilize estimates when data are sparse.

[Larry91]

Common pitfalls to avoid: high dimensionality, sparse counts, batch effects, and leakage. Design cross-validation to reflect real-world use: leave-one-donor-out or hold out a batch rather than random splits. Normalize counts properly (log-CPM or scran normalization) and use distributions suitable for counts (negative binomial, zero-inflated models). For small datasets, leaning into Bayesian methods or regularized models can prevent overfitting. Consider transfer learning from larger public datasets to give your model a sensible starting point.

[Brian_G]

Tooling and pipelines I’d recommend: Python with Scanpy for preprocessing and visualization; scVI-tools for Bayesian inference; Pyro/PyTorch or TensorFlow Probability for custom models; for gene regulatory networks, GENIE3 or GRNBoost2 can be used, though many groups move to simpler correlation/causal inference methods when data are sparse. For interpretability, SHAP, LIME, and permutation tests are helpful. For scalability, Dask or Apache Spark can handle larger datasets; Snakemake or Nextflow helps you automate workflows; or use a data versioning tool like DVC to track datasets and model artifacts.

[RobertLL]

Pilot plan you can adapt: start with a one-dataset baseline, e.g., a PBMC single-cell dataset. Week 1–2: get data cleaned and preprocessed; Week 3–4: train a baseline classifier (cell types), evaluate with ARI/NMI and accuracy; Week 5–6: experiment with scVI to model cell populations with latent variables and uncertainty; Week 7–8: interpret with SHAP or feature importances and validate in a second dataset. Document decisions, share code, and present findings to your PI with a simple 'learned things' memo. Also consider collaboration with a lab that can supply a small validation set.