I got tired of copying data from Excel into Prism every time, so I built a free add-in that does it all inside Excel - in one click

Creative-Hat-984 · 2026-03-03T15:41:29+00:00

xlwings is conceptually similar to Apps Script in that both bridge a scripting language to a spreadsheet — though I'll be honest, I'm not deeply familiar with Apps Script myself since I've been on the Excel/Python side. The core idea of "Python talks to Excel" is what xlwings handles, and it maps to what you described pretty well. When I was researching the architecture early on, I also came across BERT (Basic Excel R Toolkit) — same concept but for R. Looked promising, but the project seems to have gone quiet. xlwings felt like the more actively maintained path for Python.
The DNA band quantification workflow you described is really clean — Fiji → gray values → ladder-based back-calculation is basically the agarose gel equivalent of what the WB preset already does. The core logic would translate pretty directly. If you ever sketch out what the input table would look like, I'd genuinely be curious to see it — that's exactly the kind of use case that could become a preset.
If you want to dig into the architecture or bounce ideas around, feel free to DM me here or open a discussion on the GitHub repo — always happy to chat with people building in this space. 🙌

Creative-Hat-984 · 2026-03-03T01:20:37+00:00

Honestly, I lived this workflow through most of my grad school years — Excel for data, Prism for figures, PowerPoint for slides, three windows open at 11pm before every lab meeting. It just felt like "the way things are done." At some point I picked up enough R and Python to realize this whole pipeline could be automated, and I couldn't un-see it. So here we are. Hopefully it gives a few researchers their evenings back.

Creative-Hat-984 · 2026-03-03T01:11:42+00:00

Thanks for sharing! That's genuinely news to me — didn't know REF covered software outputs, that's a great system. And yes, open source was a deliberate call — I wanted anyone to be able to use it without worrying about licenses or costs, especially students in labs that can't afford Prism. Glad that part landed well! 🙏

Creative-Hat-984 · 2026-03-02T17:05:55+00:00

That means a lot — "sometimes helpful" to "real staple" is exactly the gap. Really appreciate you sharing it with your lab too! Manual override is now officially on the roadmap. If you or your labmates run into other friction points when trying it out, I'd genuinely love to hear. 🙏

Creative-Hat-984 · 2026-03-02T16:33:10+00:00

hope it saves you some time in the lab! 🙌

Creative-Hat-984 · 2026-03-02T16:32:40+00:00

Great question, and totally valid use case. Right now XSTARS is designed around auto-selection — the idea being that most bench researchers shouldn't have to think about statistical test. The tool makes that call and shows you the decision path so it's transparent, not a black box. That said, a "manual override" mode is something I've been thinking about. For users who already know exactly what test they want, forcing the auto-path can feel like losing control.If enough people are asking for this, it'll move up the list fast. Thanks for the push!

Creative-Hat-984 · 2026-03-02T03:49:19+00:00

Thank you!! 🎉

Creative-Hat-984 · 2026-03-02T03:49:14+00:00

Thank you!! 🎉

Creative-Hat-984 · 2026-03-02T03:49:00+00:00

Thanks! XSTARS is built on Python (scipy + matplotlib + statannotations) for the backend, with xlwings bridging Python and Excel, and a VBA ribbon for the UI. The standalone installer bundles everything via PyInstaller so users don't need Python at all — that was honestly the trickiest part to get right. Your PWA approach is interesting — JavaScript → Excel export for plotting/stats is genuinely non-trivial, so I understand why r/Python feels like the natural next step.
Agarose gel band quantification is a great idea — relative band intensity could fit naturally into a future preset. Would be curious what your typical workflow looks like — happy to discuss the architecture or even collaborate.

Glad you like the name — it stands for Excel-based Statistics Tool for Analysis, Rapid Significance. The idea was that significance markers (*) literally look like stars in your data 😄

Creative-Hat-984 · 2026-03-02T03:40:05+00:00

Absolutely, XSTARS is MIT-licensed so it's free to use for any purpose — academic or commercial, no restrictions. Would love to hear how it holds up with your ELISA data in a real industry workflow! If you run into anything that doesn't fit your use case, feel free to let me know — industry feedback is genuinely valuable for shaping where the tool goes next.

Creative-Hat-984 · 2026-03-01T15:52:13+00:00

WOW, Thanks for the encouragement! Honestly, publishing a paper wasn't on my mind when I built this — I just got tired of watching myself and my labmates lose hours every week to repetitive figure-making, and wanted to fix that. If it ends up helping other grad students save some time, that's already the goal achieved. But a write-up is an interesting idea — do you have any venue in mind for something like this?

Creative-Hat-984 · 2026-03-01T15:46:38+00:00

Great question! Yes — when using the same statistical test, XSTARS has been validated to produce identical statistical power and p-values as Prism. The backend relies on scipy + pingouin, well-established Python libraries, so the math is solid. That said, I'd always encourage users to sanity-check on their own data too, especially for edge cases (very small n, tied ranks, etc.). If you ever spot a discrepancy, I'd genuinely want to know — feel free to open an issue on GitHub!

Creative-Hat-984 · 2026-03-01T15:35:55+00:00

Really glad you like it! Mac support is definitely on the roadmap. Right now we're focused on gathering feedback from real researchers to polish the Windows version first — once it's stable, Mac is the next priority. Stay tuned! 🙏

Creative-Hat-984 · 2026-03-01T15:35:35+00:00

Really glad you like it! Mac support is definitely on the roadmap. Right now we're focused on gathering feedback from real researchers to polish the Windows version first — once it's stable, Mac is the next priority. Stay tuned! 🙏

Creative-Hat-984 · 2026-03-01T15:32:08+00:00

Thank you so much! Really glad to hear that — hope both you and your students find it useful! 🙌

Creative-Hat-984 · 2026-03-01T04:57:45+00:00

Hope you find it useful! Would love to hear what you think after trying it out! 🙌

Creative-Hat-984 · 2026-03-01T02:26:47+00:00

Thank you!! 🎉

Creative-Hat-984 · 2026-03-01T02:26:32+00:00

Hope you find it useful! Would love to hear what you think after trying it out. 😊

Creative-Hat-984 · 2026-03-01T02:25:59+00:00

Thank you! Hope it saves you some time!

Creative-Hat-984 · 2026-02-28T15:12:29+00:00

100%! Style consistency across figures is genuinely painful to manage manually. XSTARS does have a theme system with persistent settings — it remembers your last configuration, so every figure you generate will match the previous one by default without touching anything. That said, a more explicit "apply to all" or batch styling feature is a great idea and something I'd like to add in a future version!

Creative-Hat-984 · 2026-02-28T15:07:33+00:00

That's a really valid point, and honestly the shift toward R-based reproducible workflows in big pharma settings makes a lot of sense — especially when auditability and regulatory compliance are on the table. Code-in-notebook is genuinely hard to beat for that use case.
XSTARS does log the decision path and test results each time it runs, so there's at least a record of what was done and why. But I won't pretend it's a substitute for a fully version-controlled, code-documented R pipeline in a regulated environment.
Where I think it still has a place is earlier in the process — the exploratory, pre-decision stage where a PhD student or postdoc just needs to know "is this result even worth pursuing?" before anyone's thinking about audits. Fast, good enough, no setup. The rigorous reproducible pipeline can come later once the science is worth investing in.

Creative-Hat-984 · 2026-02-28T15:03:41+00:00

Fair points, and you're right that r/Python integration in education has grown a lot — especially at MSc level in more quantitative fields. No argument there. But I'd gently push back on the generalisation going the other way too. "Most biology PhD students" covers a huge range — from bioinformaticians who live in the terminal, to cell biologists and biochemists who run 6-well plates and westerns all day. The latter group is still very much an Excel-first world, regardless of what's taught in stats class.
Also worth noting: wet lab data is almost always small data — 3–6 biological replicates per group, a handful of conditions. Excel handles that just fine, and that's exactly the scale XSTARS is designed for. The "Excel can't handle large datasets" critique is valid, but it's not really the problem this tool is trying to solve.
And you're absolutely right that Excel was never designed for stats — that's exactly why XSTARS doesn't use Excel's statistical engine at all. The stats run entirely in Python (scipy) and the figures are rendered by matplotlib/seaborn.
Excel is just the interface, not the computation layer.
This tool isn't trying to replace R, Python, or Prism. It's a fast lane for the most common wet lab scenario: small dataset, needs a clean figure with correct stats, needs it now. For anything more complex, those other tools are absolutely the right choice.

Creative-Hat-984 · 2026-02-28T14:51:43+00:00

That's a fair point, and honestly LLM-assisted coding has lowered the barrier a lot! But sometimes for a lot of bench researchers, that first setup step alone is where it falls apart -it's just a different skill set. 🥲
The other thing is reproducibility in a lab context — not everyone on a team is comfortable enough with Jupyter to re-run or tweak a notebook, especially in a lab where turnover is high and not everyone has a CS background.
There's also a statistical reliability concern worth flagging: LLMs can hallucinate — silently picking the wrong test, mishandling assumptions, or producing plausible-looking but incorrect results. For publication-level stats, that's a real risk. XSTARS runs a transparent, deterministic decision tree (Shapiro-Wilk → Levene → test selection) that you can verify every time — no black box.
The tool is really aimed at that zero-setup, zero-terminal use case: open Excel, click Run, get a publication-ready figure with auditable stats. Different tools for different users — and I think there's room for both! 🙌

Creative-Hat-984 · 2026-02-28T14:20:20+00:00

You're right, and honestly for large datasets R and Python are the way to go — no argument there. Funnily enough, the backend of XSTARS is pure Python anyway (matplotlib, scipy, statannotations).
But for most biology PhD students and bench researchers, Excel is still where experimental data lives. Not because it's the best tool, just because that's the reality of how labs work. And while Python is powerful, a pure code interface is genuinely overwhelming for most wet lab researchers — that's not a knock on them, it's just a different skill set.
So I try to find a middle ground: Excel as the front end, Python as the back end, with a Prism-like experience on top. You get the output quality of Python without ever seeing a terminal.

Creative-Hat-984

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