Justin Silverman
@inschool4life.bsky.social
Assistant Professor of Informatics, Statistics, and Medicine at Penn State University
https://jsilve24.github.io/SilvermanLab/
https://jsilve24.github.io/SilvermanLab/
@cellpress.bsky.social
We submitted a presubmission inquiry on 9/12 and followed up again on 9/24. We have not heard a response. Is this typical? Could you please help us, we are trying to confirm how we should submit, as a matters arising or as a research article
www.biorxiv.org/content/10.1...
We submitted a presubmission inquiry on 9/12 and followed up again on 9/24. We have not heard a response. Is this typical? Could you please help us, we are trying to confirm how we should submit, as a matters arising or as a research article
www.biorxiv.org/content/10.1...
Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis
Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, of...
www.biorxiv.org
September 30, 2025 at 1:03 PM
@cellpress.bsky.social
We submitted a presubmission inquiry on 9/12 and followed up again on 9/24. We have not heard a response. Is this typical? Could you please help us, we are trying to confirm how we should submit, as a matters arising or as a research article
www.biorxiv.org/content/10.1...
We submitted a presubmission inquiry on 9/12 and followed up again on 9/24. We have not heard a response. Is this typical? Could you please help us, we are trying to confirm how we should submit, as a matters arising or as a research article
www.biorxiv.org/content/10.1...
New Paper!
Machine learning models that attempt to predict microbial load collapse outside of their training context with an R2<0!
In contrast, our Bayesian Partially Identified Models embrace uncertainty in unmeasured microbial load and consistently outpreform.
www.biorxiv.org/content/10.1...
Machine learning models that attempt to predict microbial load collapse outside of their training context with an R2<0!
In contrast, our Bayesian Partially Identified Models embrace uncertainty in unmeasured microbial load and consistently outpreform.
www.biorxiv.org/content/10.1...
Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis
Microbiome sequencing measures relative rather than absolute abundances, providing no direct information about total microbial load. Normalization methods attempt to compensate, but rely on strong, of...
www.biorxiv.org
September 17, 2025 at 5:41 PM
New Paper!
Machine learning models that attempt to predict microbial load collapse outside of their training context with an R2<0!
In contrast, our Bayesian Partially Identified Models embrace uncertainty in unmeasured microbial load and consistently outpreform.
www.biorxiv.org/content/10.1...
Machine learning models that attempt to predict microbial load collapse outside of their training context with an R2<0!
In contrast, our Bayesian Partially Identified Models embrace uncertainty in unmeasured microbial load and consistently outpreform.
www.biorxiv.org/content/10.1...
Reposted by Justin Silverman
Excited to summarize our most recent paper, "Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2" on controlling the false discovery rate (FDR) when analyzing high throughput sequencing (HTS) data. This has been an open problem since the dawn of HTS.
August 21, 2025 at 8:59 PM
Excited to summarize our most recent paper, "Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2" on controlling the false discovery rate (FDR) when analyzing high throughput sequencing (HTS) data. This has been an open problem since the dawn of HTS.
New preprint!
PCR bias doesn’t just distort relative abundances—it reshapes microbiome ecological analyses.
We show that commonly used diversity metrics (e.g., UniFrac or Shannon) are not robust to amplification bias, while perturbation-invariant alternatives are.
www.biorxiv.org/content/10.1...
PCR bias doesn’t just distort relative abundances—it reshapes microbiome ecological analyses.
We show that commonly used diversity metrics (e.g., UniFrac or Shannon) are not robust to amplification bias, while perturbation-invariant alternatives are.
www.biorxiv.org/content/10.1...
PCR Bias Impacts Microbiome Ecological Analyses
Polymerase Chain Reaction (PCR) is a critical step in amplicon-based microbial community profiling, allowing the selective amplification of marker genes such as 16S rRNA from environmental or host-ass...
www.biorxiv.org
August 1, 2025 at 1:55 PM
New preprint!
PCR bias doesn’t just distort relative abundances—it reshapes microbiome ecological analyses.
We show that commonly used diversity metrics (e.g., UniFrac or Shannon) are not robust to amplification bias, while perturbation-invariant alternatives are.
www.biorxiv.org/content/10.1...
PCR bias doesn’t just distort relative abundances—it reshapes microbiome ecological analyses.
We show that commonly used diversity metrics (e.g., UniFrac or Shannon) are not robust to amplification bias, while perturbation-invariant alternatives are.
www.biorxiv.org/content/10.1...
New Paper:
We relax normalizations to produce statistical methods for bioinformatics that are much more robust and powerful. We see FDR drop from 45% to 5% with increases in power!
This adds to our ongoing work on Scale Reliant Inference.
link.springer.com/article/10.1...
We relax normalizations to produce statistical methods for bioinformatics that are much more robust and powerful. We see FDR drop from 45% to 5% with increases in power!
This adds to our ongoing work on Scale Reliant Inference.
link.springer.com/article/10.1...
Replacing normalizations with interval assumptions enhances differential expression and differential abundance analyses - BMC Bioinformatics
Background Methods for differential expression and differential abundance analysis often rely on normalization to address sample-to-sample variation in sequencing depth. However, normalizations imply ...
link.springer.com
July 1, 2025 at 4:46 PM
New Paper:
We relax normalizations to produce statistical methods for bioinformatics that are much more robust and powerful. We see FDR drop from 45% to 5% with increases in power!
This adds to our ongoing work on Scale Reliant Inference.
link.springer.com/article/10.1...
We relax normalizations to produce statistical methods for bioinformatics that are much more robust and powerful. We see FDR drop from 45% to 5% with increases in power!
This adds to our ongoing work on Scale Reliant Inference.
link.springer.com/article/10.1...
Reposted by Justin Silverman
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/...
Thread explaining the key points below.
journals.asm.org/doi/10.1128/...
May 2, 2025 at 1:59 PM
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/...
Thread explaining the key points below.
journals.asm.org/doi/10.1128/...
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).
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
May 22, 2025 at 4:43 PM
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).
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).
Reposted by Justin Silverman
🚨PA colleagues:
"Senator Fetterman wants to hear from you about how the federal funding freeze is affecting Pennsylvania."
"If your project has been impacted, please fill out our constituent impact form:" forms.office.com/g/mFv2JAPxpC
Get out your Other Support and share that info!
"Senator Fetterman wants to hear from you about how the federal funding freeze is affecting Pennsylvania."
"If your project has been impacted, please fill out our constituent impact form:" forms.office.com/g/mFv2JAPxpC
Get out your Other Support and share that info!
Microsoft Forms
forms.office.com
February 22, 2025 at 8:12 PM
🚨PA colleagues:
"Senator Fetterman wants to hear from you about how the federal funding freeze is affecting Pennsylvania."
"If your project has been impacted, please fill out our constituent impact form:" forms.office.com/g/mFv2JAPxpC
Get out your Other Support and share that info!
"Senator Fetterman wants to hear from you about how the federal funding freeze is affecting Pennsylvania."
"If your project has been impacted, please fill out our constituent impact form:" forms.office.com/g/mFv2JAPxpC
Get out your Other Support and share that info!
Reposted by Justin Silverman
The National Institutes of Health had to stop considering new grant applications, delaying funding for research into diseases ranging from heart disease and cancer to Alzheimer's and allergies.
NIH funding freeze stalls applications on $1.5 billion in medical research funds
The National Institutes of Health had to stop considering new grant applications, delaying funding for research into diseases ranging from heart disease and cancer to Alzheimer's and allergies.
www.npr.org
February 22, 2025 at 6:39 PM
The National Institutes of Health had to stop considering new grant applications, delaying funding for research into diseases ranging from heart disease and cancer to Alzheimer's and allergies.
Non-linear additive regression (using scalable Bayesian Multinomial Logistic Normal models) is now available in fido (on CRAN)!
neurips.cc/virtual/2024...
Also includes extreemly fast marginal likelihood estimation for hyperparameter tuning.
cran.r-project.org/web/packages...
neurips.cc/virtual/2024...
Also includes extreemly fast marginal likelihood estimation for hyperparameter tuning.
cran.r-project.org/web/packages...
NeurIPS Efficient Bayesian Additive Regression Models For Microbiome and Gene Expression StudiesNeurIPS 2024
neurips.cc
February 19, 2025 at 2:53 PM
Non-linear additive regression (using scalable Bayesian Multinomial Logistic Normal models) is now available in fido (on CRAN)!
neurips.cc/virtual/2024...
Also includes extreemly fast marginal likelihood estimation for hyperparameter tuning.
cran.r-project.org/web/packages...
neurips.cc/virtual/2024...
Also includes extreemly fast marginal likelihood estimation for hyperparameter tuning.
cran.r-project.org/web/packages...
New paper was recently accepted to AIStats
arxiv.org/abs/2410.05548
Flexible Multinomial Logistic-Normal time series models (state space models) that scale to extreemly large datasets. Inference is 5-6 orders of magnitude faster than alternatives. R package will soon be released.
arxiv.org/abs/2410.05548
Flexible Multinomial Logistic-Normal time series models (state space models) that scale to extreemly large datasets. Inference is 5-6 orders of magnitude faster than alternatives. R package will soon be released.
Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
Many scientific fields collect longitudinal count compositional data. Each observation is a multivariate count vector, where the total counts are arbitrary, and the information lies in the relative fr...
arxiv.org
February 19, 2025 at 2:47 PM
New paper was recently accepted to AIStats
arxiv.org/abs/2410.05548
Flexible Multinomial Logistic-Normal time series models (state space models) that scale to extreemly large datasets. Inference is 5-6 orders of magnitude faster than alternatives. R package will soon be released.
arxiv.org/abs/2410.05548
Flexible Multinomial Logistic-Normal time series models (state space models) that scale to extreemly large datasets. Inference is 5-6 orders of magnitude faster than alternatives. R package will soon be released.
Just saw this paper saying compositional data methods don't help in differential abudnance analysis. www.biorxiv.org/content/10.1....
We know, we have better methods that have been validated against datasets with ground truth.
www.biorxiv.org/content/10.1...
(thread)
We know, we have better methods that have been validated against datasets with ground truth.
www.biorxiv.org/content/10.1...
(thread)
Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth
Previous benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and "real data" examples, but to the best of our knowledge, none ...
www.biorxiv.org
February 18, 2025 at 7:38 PM
Just saw this paper saying compositional data methods don't help in differential abudnance analysis. www.biorxiv.org/content/10.1....
We know, we have better methods that have been validated against datasets with ground truth.
www.biorxiv.org/content/10.1...
(thread)
We know, we have better methods that have been validated against datasets with ground truth.
www.biorxiv.org/content/10.1...
(thread)
Nice paper by Nishijima et al, from Bork's lab using machine learning to show that the absolute bacterial load is a major confounder in most human microbiome studies (www.cell.com/cell/fulltex.... Now seems a good time to outline the approaches that we have been developing.
www.cell.com
November 22, 2024 at 3:49 PM
Nice paper by Nishijima et al, from Bork's lab using machine learning to show that the absolute bacterial load is a major confounder in most human microbiome studies (www.cell.com/cell/fulltex.... Now seems a good time to outline the approaches that we have been developing.
@claesengroup.bsky.social
I would love to join the starter pack -- I am just transitioning form twitter.
Thanks!
I would love to join the starter pack -- I am just transitioning form twitter.
Thanks!
November 22, 2024 at 3:45 PM
@claesengroup.bsky.social
I would love to join the starter pack -- I am just transitioning form twitter.
Thanks!
I would love to join the starter pack -- I am just transitioning form twitter.
Thanks!