Hi! I'm Tommy Tang I am a bioinformatician/computational biologist with six years of wet lab experience and over 12 years of computation experience. I will help you to learn computational skills to tame astronomical data and derive insights. Check out the resources I offer below and sign up for my newsletter!

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8. www.data-to-viz.com/ help you to choose the right chart

ggplot2: Elegant Graphics for Data Analysis (Use R!) ggplot2: Elegant Graphics for Data Analysis (Use R!)

7. [ggplot2: Elegant Graphics for Data Analysis](www.amazon.com/ggplot2-Ele...) by Hadely Wickham.

6. [R Graphics Cookbook](www.cookbook-r.com/Graphs/) by Winston Chang.

5. Data Visualization socviz.co/ by Kieran Healy. I've read book 4 and 5.

4. Fundamentals of Data Visualization clauswilke.com/dataviz/

3. data visualization resources sabahzero.github.io/dataviz/res...

Ten simple rules to colorize biological data visualization

2. Ten simple rules to colorize biological data visualization journals.plos.org/ploscompbio...

1. Nature Methods point of view data visualization blogs.nature.com/methagora/2... the columns on color mapping and heatmap are very nice.

Data visualization is a critical step in data analysis. 8 links to bookmark for better data visualization:🧵

OmicClaw: executable and reproducible natural-language multi-omics analysis over the unified OmicVerse ecosystem www.biorxiv.org/content/10....

Hi! I'm Tommy Tang I am a bioinformatician/computational biologist with six years of wet lab experience and over 12 years of computation experience. I will help you to learn computational skills to tame astronomical data and derive insights. Check out the resources I offer below and sign up for my newsletter!

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You’re not just analyzing data.
You’re cleaning up after biology.
And sometimes, after people.
That’s the job.
And it matters.

15/
Action items:
Double-check your metadata


Validate sample swaps with NGSCheckMate or Somalier


Never assume your data is clean


Talk to the wet lab

14/
Key takeaways:
Expect human error


PCA is a sanity tool, not just a plot


Use SNP-based tools to verify identity


Always question your input

13/
Bioinformatics isn’t just coding.
It’s detective work.
And your best tools are skepticism and common sense.

12/
These tools compare genetic fingerprints.
If your RNA and DNA don’t match, you’ll catch the swap before it's too late.

GitHub - brentp/somalier: fast sample-swap and relatedness checks on BAMs/CRAMs/VCFs/GVCFs... "like damn that is one smart wine guy" fast sample-swap and relatedness checks on BAMs/CRAMs/VCFs/GVCFs... "like damn that is one smart wine guy" - brentp/somalier

11/
Or try Somalier by Brent Pedersen from Aaron Quinlan’s lab (yes, that bedtools legend).
github.com/brentp/soma...

GitHub - parklab/NGSCheckMate: Software program for checking sample matching for NGS data Software program for checking sample matching for NGS data - parklab/NGSCheckMate

10/
Use genotyping to confirm sample identity.
I’ve used NGSCheckMate:
It matches RNA and DNA using SNP profiles.
github.com/parklab/NGS...

9/
And what about multi-omics?
You assume RNA-seq and DNA-seq came from the same sample?
Be careful.

8/
If controls and knockdowns overlap in PCA?
That’s not subtle biology.
That’s a red flag.

7/
Got 3 control vs 3 knockdown?
Check the gene you silenced.
If it’s not downregulated in the knockdowns—stop.

6/
So how do you fix it?
Use your brain.
Sanity check everything.
Never trust metadata blindly.

5/
Even gold-standard datasets like TCGA have sample swaps.
Human error doesn’t disappear at scale. It is easier to make mistakes when you have thousands of samples.

4/
You stare at that PCA.
Two knockdowns look like controls.
Three controls are drifting off axis.
The data isn't lying. It’s telling you something’s off.

3/
Mislabeling happens.
Cells get mixed.
Samples are swapped.
And your PCA plot?
Suddenly it makes no sense.

2/
Wet lab scientists are not spreadsheets.
They pipette, label, freeze, and extract.
Sometimes in a rush.
Sometimes while tired

1/ Biological data isn’t just messy.
Humans generate it.
And humans make mistakes.
As a bioinformatician, this will be your reality 🧵

My $600 Mac Mini Runs a 35B AI Model. Yesterday I Swapped Its Brain. Three tiers of local inference on 16GB RAM. Qwen to Gemma. Zero downtime.

Same setup, different models. Running 35B Gemma locally on a $600 Mac Mini. Swapped from one model to another last week without breaking anything in production. thoughts.jock.pl/p/local-llm-...

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