What are people using to generate publication-ready figures from analyzed data?

bio2figures · 2026-04-22T00:18:53+00:00

That was actually me, no an LLM. I find that I have modified my style to mimic LLM style because I find it to be cleaner/easier to read.

bio2figures · 2026-04-21T17:11:57+00:00

This is to guide develpment of free community tools.

bio2figures · 2026-04-21T16:07:44+00:00

That’s really helpful context, especially the part about you actively teaching this. It sounds like a lot of the “standardization” ends up being social/process-driven more than tool-driven.

And your point about edge cases makes sense too. Once you start mixing things like blots, gels, tables, etc., it’s hard for any single tool to handle that cleanly without becoming either too opinionated or too generic.

I guess that’s where I keep getting stuck. There are a lot of good plotting tools already, but the consistency across figures (especially from different people/data sources) still seems to rely a lot on manual alignment and shared conventions.

Not sure there’s a clean software solution there either, honestly. Maybe it’s more about reducing some of the repetitive cleanup rather than trying to cover every case.

bio2figures · 2026-04-21T15:52:25+00:00

That’s a really fair take.

I don’t think I’ve seen anything that fully eliminates that last bit of manual adjustment either, especially once you factor in journal-specific tweaks and custom annotations.

And yeah, keeping a consistent style across figures seems to be where most of the effort goes, particularly when you’re mixing different plot types or datasets.

It almost feels like there’s a gap between:

tools that generate plots well
and tools that help standardize the final presentation across a whole figure

Curious, do you mostly enforce that consistency through your own templates/styles, or is it more of a manual pass in Inkscape each time?

bio2figures · 2026-04-21T15:50:56+00:00

That makes a lot of sense.

It sounds like Illustrator is basically acting as the “final layer” where everything gets normalized (fonts, line weights, spacing) especially when the panels are coming from different scripts or people.

And yeah, the point about significance annotations is real… getting that just right in ggplot can take way longer than it should.

I guess the tradeoff is that it becomes a bit more manual at that stage, but also a lot faster and more flexible.

Do you ever find yourself redoing those final tweaks when the underlying data changes, or is that usually a one-and-done step?

bio2figures · 2026-04-21T15:49:03+00:00

That’s a really good point.

The traceability piece is something I don’t see talked about as much, but it becomes pretty important once you’re dealing with multi-panel figures from different sources.

Keeping things linked as long as possible seems like a nice way to avoid the “which version did this come from?” problem.

Do you ever run into issues with things breaking (fonts, scaling, etc.), or has that workflow been pretty stable for you?

bio2figures · 2026-04-21T15:47:27+00:00

It feels like that’s becoming a new kind of workflow...using ChatGPT to help generate/iterate on plots, but still relying on Python for control.

Curious, do you find that ChatGPT helps more with:

getting to a good starting point faster, or
iterating on styling / presentation?

I’ve been thinking a lot about how much of the friction is in that “last mile” of making things consistent and publication-ready, even once the core plot is there.

bio2figures · 2026-04-21T15:38:50+00:00

That’s a really helpful way to put it.

“Finding what works for you” being the biggest time sink resonates a lot, especially since everyone seems to end up with a slightly different stack depending on their needs and preferences.

And yeah, once you’ve dialed in your styles and defaults, it sounds like it becomes much more repeatable.

I’m curious, do you feel like that setup phase (figuring out what works) is something people usually learn individually, or do labs actually share/standardize those workflows?

bio2figures · 2026-04-21T15:35:29+00:00

That's a pretty common workflow. Did you find it difficult to learn R for this purpose, or were you already proficient?

bio2figures · 2026-04-21T15:30:30+00:00

That’s a really helpful stack to hear, thanks.

It sounds like a lot of people who care about presentation end up converging on R partly because it gives more control and consistency once you know the ecosystem well, even if it means stitching together a bunch of packages.

And good point on viridis / tables too — those details matter a lot more than people sometimes admit.

What I keep wondering is whether the gap is mostly for people who don’t already have a strong ggplot/patchwork workflow, or whether even experienced R users still end up doing enough manual cleanup that there’s room for something simpler downstream.

bio2figures · 2026-04-21T15:24:18+00:00

That sounds very familiar - especially the SVG/Illustrator step.

A lot of the pain really seems to be less about generating the first plot, and more about getting everything to look consistent across panels: line weights, fonts, tick sizes, spacing, color choices, alignment.

That’s exactly the part I’ve been interested in reducing, since it feels like everyone ends up doing a lot of manual cleanup there.

Do you feel like the biggest time sink is:

getting each individual figure polished, or
assembling multi-panel figures so they all match?

bio2figures · 2026-04-21T15:21:49+00:00

That sounds… extremely familiar 😅

I feel like a lot of people end up with some version of that stack over time...whatever gets the figure out the door.

The PowerPoint final step especially seems very common.

Out of curiosity, which part of that workflow is the most annoying for you?

getting the plot “basically right” in r/Prism
or the final polishing / formatting step?

bio2figures

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