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Justin Silverman

@inschool4life.bsky.social
190 followers 52 following 53 posts

Associate Professor of Informatics, Statistics, and Medicine at Penn State University jsilve24.github.io/SilvermanLab

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Justin Silverman @inschool4life.bsky.social · 30/09/2025
@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...
biorxiv.org
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...
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Justin Silverman @inschool4life.bsky.social · 17/09/2025
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...
biorxiv.org
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...
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Reposted by Justin Silverman
Greg Gloor @ggloor.bsky.social · 21/08/2025
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.
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Justin Silverman @inschool4life.bsky.social · 01/08/2025
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...
biorxiv.org
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...
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Justin Silverman @inschool4life.bsky.social · 01/07/2025
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...
link.springer.com
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 ...
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Reposted by Justin Silverman
Tal Korem @tkorem.bsky.social · 02/05/2025
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/...
journals.asm.org
Compositional transformations can reasonably introduce phenotype-associated values into sparse features | mSystems
Gihawi et al. claim that finding that a transformation turned highly sparse (mostly zero) features into features that are associated with a phenotype is sufficient to conclude that there is informatio...
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Justin Silverman @inschool4life.bsky.social · 22/05/2025
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
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...
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Reposted by Justin Silverman
Vaughn Cooper @vscooper.micropopbio.org · 22/02/2025
🚨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!
forms.office.com
Microsoft Forms
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Reposted by Justin Silverman
NPR @npr.org · 22/02/2025
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.
npr.org
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.
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Justin Silverman @inschool4life.bsky.social · 19/02/2025
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
NeurIPS Efficient Bayesian Additive Regression Models For Microbiome and Gene Expression StudiesNeurIPS 2024
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Justin Silverman @inschool4life.bsky.social · 19/02/2025
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
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...
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Justin Silverman @inschool4life.bsky.social · 18/02/2025
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)
biorxiv.org
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 ...
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Justin Silverman @inschool4life.bsky.social · 22/11/2024
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.
cell.com
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Justin Silverman @inschool4life.bsky.social · 22/11/2024
@claesengroup.bsky.social I would love to join the starter pack -- I am just transitioning form twitter. Thanks!
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