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Wildtype One @wildtypeone.bsky.social · 14/07/2026

🧫 Join 1,000+ researchers getting weekly notes on how to diagnose ugly data, avoid weak readouts, and make better bench decisions (it’s free) 👉 wildtypeone.substack.com/about

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Wildtype One @wildtypeone.bsky.social · 14/07/2026

Biology rewards traceability. — Wildtype One 🧬

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Wildtype One @wildtypeone.bsky.social · 14/07/2026

Density is not just “how many cells.” It changes physiology. And physiology changes your readout. We often blame weird data on biological variability. Yet sometimes, you can save yourself ugly data and repetitions, ...simply by doing the boring work.

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Wildtype One @wildtypeone.bsky.social · 14/07/2026

So, what can you do about that? 🤔 👉 For EVERY experiment you seed, write down: - Thaw date - Passage number - Split ratio - Days/weeks in culture - Cells per well/flask - Confluence at seeding - Confluence at treatment

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Wildtype One @wildtypeone.bsky.social · 14/07/2026

They found nonlinear transcriptomic shifts in tumor cell lines. The scarier finding? It wasn’t just RNA. Mid-passages showed altered: - Cell cycle - Metabolism - Stress response - Signaling - Immune-response pathways

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Wildtype One @wildtypeone.bsky.social · 14/07/2026

Your passage number might be affecting your data more than you think❗ Liu et al. (2025) looked at RNA-seq data across passages P3–P39. (a thread)

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

🧫 Join 1,000+ researchers getting weekly notes on how to diagnose ugly data, avoid weak readouts, and make better bench decisions: wildtypeone.substack.com/about

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

Before adding more replicates, write down: 1. What changed? 2. What did I measure? 3. What conclusion am I trying to draw? If those three don’t match… more data won’t save you. — Wildtype One 🧬

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

In short 💡: Repeating the same experiment is only rigorous when the problem was execution ✅ Repeating the wrong experiment is just expensive confidence ❌

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

Readout too narrow? - qPCR changed but protein didn’t. - Western blot shows one band but not mechanism. - Flow shows a population shift but not what drives it. Action: Don’t repeat ❌ Change readout ✅

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

Design problem? Pseudo-replication, weak controls, unstable reference gene, no predefined exclusion rule? Action: Redesign ✅

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

Analysis choice wrong? Wrong test, SEM hiding variation, bad normalization, post-hoc outlier removal? Action: Reanalyze ✅

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

Ask 🤔: Execution failed? Contamination, bad staining, failed transfer, wrong dilution, instrument issue? Action: Repeat ✅

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

- Sometimes you should repeat. - Sometimes you should reanalyze. - Sometimes you should redesign. - Sometimes you should change the readout. The mistake is treating every ugly result like a repeat problem.

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Wildtype One @wildtypeone.bsky.social · 11/07/2026

Before you repeat the experiment, answer this: What kind of problem do you actually have? Ugly data does NOT have one next step. (a thread)

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Wildtype One @wildtypeone.bsky.social · 08/07/2026

One of the hardest things in science is explaining biology without losing the nuance. In simple language" is an underrated research skill. Nice thread. @jackamatica.bsky.social

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Wildtype One @wildtypeone.bsky.social · 08/07/2026

It would've been impressive if you'd replied to the reviewer with that last sentence @jtimmer.bsky.social

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Wildtype One @wildtypeone.bsky.social · 08/07/2026

This webinar helps you spot the most common statistical traps in biology labs and gives you a simple framework to avoid them. Designed for people who actually run experiments—not statisticians. 👉 Register for the webinar here: wildtypeone.com (3/3)

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Wildtype One @wildtypeone.bsky.social · 08/07/2026

Many analysis habits are: - Lab-inherited - Incorrect - And they show up immediately in peer review. (2/3)

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Wildtype One @wildtypeone.bsky.social · 08/07/2026

“Everyone does it this way” is not a valid statistical defense. (1/3)

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Wildtype One @wildtypeone.bsky.social · 07/07/2026

🧫 Join 1,000+ researchers getting weekly notes on how to diagnose ugly data, avoid weak readouts, and make better bench decisions (it’s free) 👉 wildtypeone.substack.com/about

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Wildtype One @wildtypeone.bsky.social · 07/07/2026

But never do it to rescue a conclusion. That’s not rigor. That’s extra time. — Wildtype One 🧬

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Wildtype One @wildtypeone.bsky.social · 07/07/2026

You can add more independent biological replicates when: 1. The design/power calculation called for it. 2. A predefined stopping rule allows it. 3. A replicate had a documented technical failure and needs replacing. 4. You are running a new validation experiment.

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Wildtype One @wildtypeone.bsky.social · 07/07/2026

That is what adding replicates until p < 0.05 looks like. Replicates should be planned before you know the result. Not added because the p-value hurt your feelings.

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Wildtype One @wildtypeone.bsky.social · 07/07/2026

Imagine a football game ⚽ It’s minute 90+5’. The game is over. Your team lost 2–1. So you extend by 5 minutes… Then 5 more… Then 5 more… You keep doing that until your team wins 3–2. Then you stop the match.

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Wildtype One @wildtypeone.bsky.social · 07/07/2026

“Do more replicates until it’s significant” is killing your credibility 🧫 (a thread)

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

PS - If you struggle with “non-significant” statistics and data outliers, Wildtype One's webinar helps you avoid mistakes and get confident statistics. No math or bioinformatics required. Register here 👉 wildtypeone.com

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Stop asking: “Why is this not significant?” Ask: “What decision is this readout actually allowed to support?” “Progress in science depends on new techniques, new discoveries and new ideas, probably in that order.” — Sydney Brenner, Nobel Prize Laureate (2002) — Wildtype One 🧬

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

The Better Bench Decision 💡: - Reanalyze if the test or assumptions are wrong. - Repeat if the technical record is weak. - Add biological replicates if the design was underpowered. - Change readout if the current assay cannot answer the biological question.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Maybe this model dies through a caspase-independent route. Maybe your endpoint is too late. Maybe the cells detached before you measured them. Maybe your assay is measuring survival, not death mechanism.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

🔴 Case C: The readout is wrong. If microscopy suggests cell death but you see no change in cleaved caspase, PARP, viability, Annexin V, PI, or TUNEL... you may not have “failed statistics.” You may have a readout mismatch.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

🔴 Case B: The effect is real but underpowered. Then more biological replicates may be right. But decide that before seeing the p-value. Not after the p-value hurts your feelings.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

You usually cannot remove an outlier just because it ruins your p-value. You need a technical reason. No technical record? Start writing it down for EVERY experiment.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

🔴 Case A: One point is weird (outlier). Do not blame biology yet. Check the experiment details: - reagent lot - serum bottle - passage number - seeding density - time out of incubator - edge effects - imaging field selection

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Step 3️⃣: Choose the next move. You'd typically have three cases:

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Step 2️⃣: Ask what the microscope is actually showing. The cells look different. Maybe it’s death. Maybe it’s lower confluence. Maybe it’s stress, detachment, morphology, growth arrest, or handling damage. Your eyes are allowed to raise suspicion. They are not allowed to finish the conclusion.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Better definition: A p-value is the probability of seeing such data if we assume no effect in your model. So it's a surprise-o-meter. That’s it. Underwhelming, right?

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Instead, read the contradiction properly. Step 1️⃣: Know what the p-value means. A p-value is not a verdict. It does NOT tell you: - how big the effect is - whether the result is true - whether the effect matters biologically

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Mistake 2: Adding “replicates” without asking what kind ❌ More technical replicates pretending to be biological ones is not okay. More biological replicates can be correct. But it can inflate significance.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Most researchers make 2 mistakes when they see this. Mistake 1: Optimizing the statistics ❌ Changing the test until the result becomes significant. That is not analysis. That is p-value fishing in a lab coat.

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Wildtype One @wildtypeone.bsky.social · 30/06/2026

Clear cell death. p = 0.056. Now what?

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

26. "If you torture the data long enough, it will confess to anything." Don't do that. You're an honest scientist. — Wildtype One 🧬

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

25. Retractions are not always intentional fraud. They can also come from innocent mistakes. Track your data.

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

24. When showing your lab data, avoid using "interestingly," "suprisingly," "unexpectedly," "proof," "proves," "robust," and any similar words.

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

23. FlowJo, GraphPad Prism, and ImageJ are standards. but don't give you the best value for price. There are better tools, depending on your case.

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

22. Autofluorescence is not background noise. It can make weak markers look real. Run unstained samples.

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

21. Strong flow dyes are not better. Brightness should improve separation. Only use it if your marker expression is weak.

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

20. Marker expression is not cell identity. CD-something-positive is a phenotype clue. Not a passport.

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

19. Flow cytometry titration is not a suggestion. Vendor dilutions are only a starting point. Test for yourself before experiments.

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Wildtype One @wildtypeone.bsky.social · 26/06/2026

18. Flow cytometry isotype controls and compensation beads are worth the money. But they're still not the best negative control. FMO is.

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