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Tal Korem @tkorem.bsky.social · 19d

No idea. Still working through this.

Tal Korem @tkorem.bsky.social · 19d

This is from the very paper you linked to - Figure S5. They claim that this has a p-value of 1. It's not here and there, this is what most results look like, and this is a large part of the basis for claiming that there are no robust associations with other tumors.

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Tal Korem @tkorem.bsky.social · 20d

So you look at this figure and your interpretation is "no signal"?

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Tal Korem @tkorem.bsky.social · Aug 16

I never knew I needed this thread

Jess McLaughlin, PhD (they/them) @jfmclaughlin92.bsky.social · Aug 16

The only visually overwhelming flag that is also a 10/10 state flag

Annalee @flowerhorne.com · Aug 15

Happy Friday to the Maryland State flag

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Tal Korem @tkorem.bsky.social · Aug 7

We are hiring a postdoc - come work with us (www.koremlab.science) at the intersection of #microbiome, data science, and women's health!
Message or email me if interested. 🖥️ 🧬

Korem Lab - Microbiome Systems Biology @ Columbia | New York, USA

We apply systems biology approaches to decipher the metabolic interactions between the microbiome and its human host in diverse clinical settings, aiming towards personalized microbiome-based therapeu...

www.koremlab.science

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Reposted by Tal Korem

Nandita Garud @nanditagarud.bsky.social · Jul 22

I am seeking a postdoc for my group at UCLA. We work at the intersection of population genetics x microbiome (garud.eeb.ucla.edu). If interested, please message me!

Garud Lab

garud.eeb.ucla.edu

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Tal Korem @tkorem.bsky.social · Jul 17

This will also likely reduce the number of study sections, firing SROs and making them less specialized.

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Reposted by Tal Korem

Noah Fierer @noahfierer.bsky.social · Jun 21

Hope this is useful - consensus statement "Guidelines for preventing and reporting contamination in low-biomass microbiome studies" rdcu.be/er3Io

Guidelines for preventing and reporting contamination in low-biomass microbiome studies

Nature Microbiology - In this Consensus Statement, the authors outline strategies for processing, analysing and interpreting low-biomass microbiome samples, and provide recommendations to minimize...

rdcu.be

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Reposted by Tal Korem

Erin Biba @erinbiba.bsky.social · May 25

The trolley problem graphic where one side is several people tied to the train tracks except the other side has no one on the tracks. A man is standing holding a lever trying to decide which track the trolley should take. The text reads “You can pull the lever, but to do so you must wear a mask at the grocery store.”

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Reposted by Tal Korem

Timothy McBride @mcbridetd.bsky.social · May 22

Not getting much attention, except in a recent NYTimes story, is a provision to increase the tax on university endowments and those of other nonprofits.

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Reposted by Tal Korem

Justin Silverman @inschool4life.bsky.social · May 22

New paper in Genome Biology!

genomebiology.biomedcentral.com/articles/10....

We introduce scale models, a generalization of normalizations that explciitly account for uncertainty in biological system scale (e.g., microbial load).

Incorporating scale uncertainty in microbiome and gene expression analysis as an extension of normalization - Genome Biology

Statistical normalizations are used in differential analyses to address sample-to-sample variation in sequencing depth. Yet normalizations make strong, implicit assumptions about the scale of biologic...

genomebiology.biomedcentral.com

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Reposted by Tal Korem

"spooky sukkot" daniel s. @self.agency · May 18

as a resident of syracuse, ny, a rust belt town that used to be an economic epicenter for the nation: syracuse university is our largest local employer now and if it goes under, so does my town, which has the largest concentration of child poverty in the nation.

Evan Torner @guyintheblackhat.bsky.social · May 18

There is a false dichotomy drawn between "the ivory tower" and "the real world," and I'm here to report that in a post-industrial society, your real-world economy absolutely hinges on the university.

University towns are factory towns. Universities drive economic activity, not the other way around.

Prof Christina Pagel @chrischirp.bsky.social · May 18

A must read

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Reposted by Tal Korem

Lynn Jolicoeur @lynnjolicoeur.bsky.social · May 16

"I'm still in shock. I know that there have been political issues around Harvard in recent weeks, but antibiotic resistance isn't one of them." My conversation with Harvard microbiologist @baym.lol, one of many researchers there who just lost millions in fed. grants. www.wbur.org/news/2025/05...

Antibiotic research at Harvard lab threatened by federal funding cuts

Microbiologist Michael Baym studies antibiotic resistance at Harvard Medical School. He lost millions in federal funding this week.

www.wbur.org

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Reposted by Tal Korem

Tal Korem @tkorem.bsky.social · May 2

Our paper explaining why Gihawi et al. failed to prove an error in the normalization used by the 2020 cancer #microbiome analysis now out as a Matters Arising in @asm.org #mSystems (w/ @george-austin.bsky.social) 🖥️ 🧬

Thread explaining the key points below.

journals.asm.org/doi/10.1128/...

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Tal Korem @tkorem.bsky.social · May 2

Our paper explaining why Gihawi et al. failed to prove an error in the normalization used by the 2020 cancer #microbiome analysis now out as a Matters Arising in @asm.org #mSystems (w/ @george-austin.bsky.social) 🖥️ 🧬

Thread explaining the key points below.

journals.asm.org/doi/10.1128/...

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Reposted by Tal Korem

Emily White @emily-white.bsky.social · Apr 11

Come and work with me!

The Nature Micro team is expanding and we're looking for someone to champion microbial ecology, plant micro & related areas for the journal

Knowledge of microbial ecology/plant micro is desirable but we're open to applications from all microbiologists

Link below 👇

Nature Microbiology @natmicrobiol.nature.com · Apr 11

📢We are #hiring a full time editor to handle areas including microbial ecology & plant microbiology🦠🌱

This position will work closely with @naturecomms.bsky.social

#microsky #microbiomesky #ecologysky 🧪

Available in NYC, Madrid, or other US offices

Read more & apply here: shorturl.at/yvdYJ

Associate or Senior Editor (Nature Microbiology), Nature Communications

Title: Associate or Senior Editor (Nature Microbiology), Nature Communications Organisation: Nature Portfolio Locations: New York, Philadelphia, Washington DC, Jersey City or Madrid – hybrid working m...

springernature.wd3.myworkdayjobs.com

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Tal Korem @tkorem.bsky.social · Apr 4

Congrats!

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Reposted by Tal Korem

Nature Microbiology @natmicrobiol.nature.com · Mar 27

OUT NOW: Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

@tkorem.bsky.social & co

#microsky #microbiomesky 🧪

www.nature.com/articles/s41...

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models - Nature Microbiology

DEBIAS-M corrects technical variability in microbiome data in a manner both interpretable and suitable for machine learning. In extensive benchmarks, DEBIAS-M facilitates robust analyses that generali...

www.nature.com

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Tal Korem @tkorem.bsky.social · Mar 27

A hopefully paywall-free link:
www.nature.com/articles/s41...

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Nature Microbiology - DEBIAS-M corrects technical variability in microbiome data in a manner both interpretable and suitable for machine learning. In extensive benchmarks, DEBIAS-M facilitates...

www.nature.com

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Tal Korem @tkorem.bsky.social · Mar 27

DEBIAS-M is available as a Python package (korem-lab.github.io/DEBIAS-M/ or just pip install debias-m). It works with any microbiome read count or relative abundance matrices, and any paired metadata. 7/7

DEBIAS-M: Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome

korem-lab.github.io

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Tal Korem @tkorem.bsky.social · Mar 27

Its multi-task version allows DEBIAS-M to learn models for multiple tasks at the same time, further increasing its performance. This is particularly useful for tasks such as metabolite level predictions, where we want to predict multiple metabolite levels using the same microbiome data. 6/7

Boxplots showing performance on metabolite prediction (each point is a different metabolite). Y-axis is Spearman correlation, x-axis are different methods. Prediction using raw data is nearly random (median correlation of ~0). MelonnPan improves substantially to a median of ~.25. DEBIAS-M and multi-task DEBIAS-M improve this further, with a median Spearman of ~.3.

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Tal Korem @tkorem.bsky.social · Mar 27

Finally, DEBIAS-M is designed for machine learning pipelines, allowing to not just hold-out labels for a test set, but actually has an online learning mode that can handle completely new data on the fly (to our knowledge - the only method that allows that for microbiome data). 5/7

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Tal Korem @tkorem.bsky.social · Mar 27

Next, the changes DEBIAS-M makes to the data are interpretable and explained by differences in experimental protocols. Analyzing the biases inferred for these 17 gut microbiome studies in HIV, we found that 84% of the variance can be explained by just three experimental factors. 4/7

An analysis of 17 studies of the gut microbiome in the context of HIV. On the top is an Adonis analysis, showing that 43% of the variance in inferred experimental biases is explained by DNA extraction kit, 27% by the 16S gene region, and 14% by the type of swab used for sample collection. On the bottom is a PCA of inferred biases, where every dot is a study. There is apparent clustering by extraction kit type and 16S gene region.

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Tal Korem @tkorem.bsky.social · Mar 27

This results in several benefits. First, in diverse benchmarks - using metagenomics and 16S sequencing, vaginal and gut microbiomes, and phenotypic and metabolite predictions - DEBIAS-M outperforms alternative methods. Here is an example for a gut 16S-based HIV classification across 17 studies. 3/7

Boxplots with auROC on the y-axis and different methods on the x-axis/ Title reads :"DEBIAS-M improves cross-study HIV prediction". Raw data has a median auROC of ~0.5; ComBat, ConQuR, percnorm, Voom-SNM, MMMUPHin and PLSDA-batch have median auROCs between ~0.5 and ~0.6. The median auROC of DEBIAS-M is close to 0.7 and is significantly higher than all the rest.

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Tal Korem @tkorem.bsky.social · Mar 27

DEBIAS-M is based on the multiplicative bias model of McLaren et al. (elifesciences.org/articles/46923). Under this model, every experimental protocol has different biases for each microbe. We infer the biases that maximize cross-batch association with phenotypes and minimize batch effects. 2/7

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