Applied statistician. I tweet data-driven observations, data science educational materials, academic research updates, and the occasional joke.
Boston, MA
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Online resources useful for learning or teaching data science, biostats and bioinformatics including 15 online courses. Topics include:
R
tidyverse
Dataviz & ggplot2
Linux, GitHub
Probability
Inference & Modeling
Regression
Machine Learning
Bioconductor
rafalab.github.io/pages/teac…
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🚨We are hiring! 🚨
The Department of Biostatistics @HarvardChan and the Department of Data Science @DanaFarber are recruiting a tenure-track Assistant/Associate Professor in statistics or machine learning with interests in single-cell or spatial genomics.
Link below 👇
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Congratulations to the R Core Team on receiving the 2026 Rousseeuw Prize for Statistics. For over 30 years, R Core has maintained one of the most important pieces of infrastructure in statistics. Little recognition, thousands of volunteer hours, and enormous impact. Thank you!
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A flaw in Person-Δ may be overstating progress in single-cell perturbation prediction models. Pearson warned about this in the 19th century: reusing the same controls induces spurious correlation. Split the controls, and much of the claimed prediction power fades. Link below 👇
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📢 We are taking applications for our Postdoctoral Fellows Program at Harvard/DFCI!
🔹Join a research group in our department
🔹Co-mentoring opportunities with 2+ faculty
🔹Collaborate with investigators beyond our department
🔹Salary starts at $75K
Application link in replies.
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Apply here: ds.dfci.harvard.edu/postdocs…
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Rafael Irizarry retweeted
Unique Molecular Identifiers (UMIs) in RNA-seq are supposed to be… unique. But what if they don’t have to be?
In our new preprint w/ Dylan Agyemang + @rafalab, we show that UMIs can be shorter—if you use the right estimator. 1/12
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Looking forward to speaking at #FOGBoston this June.
If there’s something you would like me to answer during my session, feel free comment or message me.
Registration is free for most attendees.
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The Zelen Symposium is next Friday, April 4!
This year’s theme: Data-driven controversies — from AI hype in medicine to climate change to the origins of COVID-19.
Speakers include Alina Chan @Ayjchan, Auroop Ganguly, Sayash Kapoor, Marc Lipsitch, Arjun Manrai & Gavin Schmidt.
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In a @simplystats post I argue that universities do spend indirect costs on research.
Many, including prominent scientists, seem to misunderstand the true cost of biomedical research and the fact that universities subsidize it beyond what they receive from NIH.
Link below! 👇
Last year, $9B of the $35B that the National Institutes of Health (NIH) granted for research was used for administrative overhead, what is known as “indirect costs.” Today, NIH lowered the maximum indirect cost rate research institutions can charge the government to 15%, above what many major foundations allow and much lower than the 60%+ that some institutions charge the government today. This change will save more than $4B a year effective immediately.
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If indirect costs are capped as proposed, universities may need to shift more expenses into direct costs, increasing administrative complexity and reducing research efficiency. This could also offset any intended savings from the cap.
More details here: simplystatistics.org/posts/2…
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This UMAP plot was generated from data simulated without inherent clusters, meaning the observed clusters are artifacts.
It highlights how default Seurat clustering and UMAP settings can sometimes produce misleading patterns in data visualization.
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The data mimics real experiments, including gene correlations and coverage variability. The code is here: github.com/rafalab/misc/blob…. You can run the Seurat analysis with different parameters to see how the figure changes. For example, here is the plot with default parameters:
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Rafael Irizarry retweeted
What’s the most influential course I’ve taken for computational genomics?
Hands down: HarvardX’s Data Analysis for Life Sciences by Rafa Irizarry & Mike Love. Here’s why 👇 rafalab.dfci.harvard.edu/pag…
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Biologists, stop putting UMAP plots in your papers!
UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation.
Link to @simplystats blogpost below.
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