Posts Tagged ‘dqtl’

A mechanism for adaptive genome regulation in cancer | Nature

April 25, 2026

https://www.nature.com/articles/s41586-026-10269-1

Nice discussion of discrete cell types v cont. cell states

QT:{{”
Although the transcriptomic classification of cell types and states into discrete hierarchical entities is useful for standardizing and recogniz- ing functional units8, this framework could risk the reinforcement of a Platonic view, in which observed states are viewed as approximations to idealized configurations underpinned by strict gene programs (Fig. 1a). The advent of single-cell RNA sequencing has provided evidence for the notion that cells often traverse continuous and multidimensional landscapes of gene expression, shaped by varying degrees of constraint and plasticity. Such dynamics are an inherent cellular attribute that also occurs in seemingly stable physiological states, and processes in normal physiology once thought to involve binary choices are now recognized as continuous (Fig. 1b). For example, haematopoiesis reflects gradual acquisition of lineage biases rather than transitions between discrete progenitor states9. Epithelial-to-mesenchymal transition (EMT) proceeds through multiple intermediate hybrid states with context-specific tran- scriptional profiles10. Waddington’s well-known epigenetic landscape metaphor, where cells roll down a fixed, branching landscape during cell-fate decisions and settle at valleys corresponding to stable inter- mediate or terminally differentiated states, may not fully capture the continuity of cellular-state transitions11. Instead, the landscape itself appears to be flexible, especially in disease contexts, with environmental and genetic changes reshaping the accessibility of states, thus changing the barriers that govern cell-state transitions (Fig. 1c). “}}

França, G. S., & Yanai, I. (2026). A mechanism for adaptive genome regulation in cancer. Nature, 652(8110), 581–590.
https://doi.org/10.1038/s41586-026-10269-1

Send ppt from our chat

February 22, 2026

good tutorial/textbook chaper/review paper on Poisson regression :

Below are a few readings that discuss how to fit a generalized linear mixed model.

1. Breslow & Clayton (1993), JASA, Approximate Inference in
Generalized Linear Mixed Models.
https://doi.org/10.2307/2290687
A classic statistical paper introducing the Laplace approximation and penalized quasi-likelihood for GLMMs

2. Bates (2011), Mixed models in R using the lme4 package Part 5: Generalized linear mixed models
https://lme4.r-forge.r-project.org/slides/2011-03-16-Amsterdam/5GLMMH.pdf 3. Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of statistical software, 67, 1-48.
https://doi.org/10.18637/jss.v067.i01
4. Bates (2025), Computational methods for mixed models
https://cran.r-project.org/web/packages/lme4/vignettes/Theory.pdf

These are written by the authors of the lme4 package, discussing the details of how a mixed-effects model (more specifically, a generalized linear mixed-effects model) is trained using the PIRLS approach.