Hello Bioinformatics lovers,

Tommy here. It is the end of Fall in Boston. I am going to go apple-picking with the family today.

So when you see this newsletter (pre-scheduled), we are on our way. (I am writing this newsletter at 1:15 am)

We will discuss creating figures today!

This is the ultimate skill you need to have as a bioinformatician.

When you think about making a figure as a bioinformatician, you probably imagine writing code, choosing colors, and generating plots.

But here’s the truth:

Only 10% is making the plot. 90% is polishing—changing fonts, colors, labels, and layouts until it’s “final.”

Let’s break it down.

The First Draft

Your first ggplot or matplotlib plot works:

• Axes are labeled
• Colors are chosen
• Data looks fine

But then comes the real challenge: feedback from collaborators.

Common Feedback from Wet Lab Teammates

• “Can you increase the font size?”
• “Change the axis label font.”
• “Use blue instead of orange.”
• “Add sample names.”
• “Flip the heatmap orientation.”

Sound familiar?

Heatmap Tips

If you’re making a heatmap: don’t use default colors. Instead, use perceptually uniform colormaps (so differences are meaningful).

Great reference from the ComplexHeatmap author: 👉 Color map guide​

Multi-Panel Figures

Need a figure with multiple subplots?

• R users → Use patchwork to arrange ggplots like LEGO blocks. 📖 Docs: patchwork​
• Python users → Use patchworklib, which mimics patchwork’s grammar. 💻 GitHub: patchworklib​

Reproducibility Matters

Keep your figure scripts reproducible:

• Use ggsave() in R or plt.savefig() in Python
• Avoid manual Illustrator tweaks if possible
• Version your plots: fig1_v3.png

Or better: automate this with gofigr, which versions figures and snapshots the code that generated them.

What Makes a Clear Plot?

• Labels are clear
• Fonts are legible
• Colors work in grayscale
• Enough white space
• The figure tells a story—even without a caption

Takeaways

• Expect multiple rounds of revisions
• Reuse templates for consistency
• Collaborate closely with wet lab teammates
• Remember: polish makes a difference

Because in science, clear plots = clear communication.

Other posts that you may find useful

1. Want to become someone you dream of? The key is 👇 (Life lesson post, skip it if you are here for bioinformatics only).
2. Your data is lying to you. Here’s how technical artifacts distort biology—and how to see the truth. 👇
3. Bioinformatics isn't just code. It’s intuition. You run the stats, but you feel when something’s wrong.
4. chatomics! 9 videos teaching you how to recreate Figure 1 step by step from a genomics paper with ChIP-seq data
5. Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data​
6. what they forgot to teach you about R.​
7. You just got your hands on early clinical trial RNA-seq data. Excited? You should also be cautious. Here's why. 👇
8. ​The pitfalls of class prediction in omics
9. Doing bioinformatics is not just crunching numbers. It’s running experiments—just like a wet lab scientist does. But with code.

Also, reply "yes" if you think my compilation of the posts are helpful for you. I have to manually curate them from my past week's LinkedIn posts.

Happy Learning!

Tommy aka crazyhottommy

PS:

If you want to learn Bioinformatics, there are other ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it. (I have been lazy recently, but I promise to make more videos!)
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 10 years https://divingintogeneticsandgenomics.com/​

Stay awesome!