Lior Pachter

The cyclic nature of many biological systems is frequently reflected in periodic gene expression patterns. Standard dimensionality reductions can reveal such variation but do not link a sample’s position on a cycle to an ordering of the molecular features that define it. We introduce clrcycle , which applies a centered log-ratio transformation, learns a cyclic feature ordering by maximizing variance captured by the first Fourier mode, and projects samples onto the corresponding cosine and sine coordinates. The resulting two-dimensional geometry assigns each sample a phase and a radius measuring the magnitude of its projection onto the learned Fourier circle. Across a mouse circadian atlas, clrcycle recovers known temporal organization and reveals tissue-specific differences in rhythmic timing. In FUCCI-profiled single cells, clrcycle represents independently measured cell-cycle phase in two-dimensional coordinates more reliably than conventional two-dimensional embeddings. In human endometrium, a circular coordinate learned from 250 highly variable genes recovers menstrual-stage organization and can stage unseen endometrial biopsies. Together, these results establish clrcycle as powerful, explicitly circular dimensionality reduction technique to quantify high-dimensional cyclic variation across biological cycles.

Spatial transcriptomics technologies enable the identification of spatially variable genes that can shed light on tissue structure and function. However, spatial variability of gene expression can be due not only to biological function but also technical artifact. We develop a model for spatially variable capture that forms part of a bursty transcription model, allowing us to distinguish biological variability from spatially variable technical noise. Our approach is implemented in a program called stipple, which we demonstrate can be useful for identifying and interpreting spatial gene expression variation from spatial transcriptomics assays.

We describe a coordinate system and associated hypothesis-testing framework for determining whether cis or trans regulation is responsible for differences in gene expression between two homozygous strains or species. We apply our framework to data from single replicate studies on yeast strains and human-chimpanzee hybrid cells, as well as to data from a mouse study with replicates, showing marked differences between our gene regulatory assignments and those previously reported. We also show how our multisample framework can determine the context dependency of cis and trans effects as well as explicitly model different hypotheses regarding the underlying mechanism of trans regulation.

The rapid advancement of spatially resolved transcriptomics (SRT) technologies has facilitated exploration of how gene expression varies across tissues. However, identifying spatially variable genes remains challenging due to confounding variation introduced by the spatial distribution of cell types. We introduce a new approach to identifying spatial domains that are homogeneous with respect to cell type composition that facilitates the decomposition of gene expression patterns by cell type and spatial variation. Our method, called concordex, is efficient and effective across technological platforms and tissue types, and using several biological datasets we show that it can be used to identify genes with subtle variation patterns that are missed when considering only cell type variation, or spatial variation, alone. The concordex tool is freely available at https://github.com/pachterlab/concordexR .

1 We developed a standardized, reproducible preprocessing framework for computed tomography (CT) imaging data from multi-institutional repositories such as The Cancer Imaging Archive (TCIA), enabling consistent radiomics and artificial intelligence (AI) analyses. Imaging data from TCGA-KIRC patients available on TCIA were used as a representative heterogeneous dataset characterized by variation in acquisition protocols, inconsistent metadata, and differing image quality. The pipeline includes series filtering, DICOM-to-NIfTI conversion, orientation harmonization to a canonical coordinate system, voxel spacing normalization, intensity clipping and normalization, segmentation integration, and metadata validation, and is implemented in a reproducible, notebook-based framework compatible with common radiomics and deep learning workflows. This pipeline standardizes imaging data into analysis-ready volumes with consistent geometry, intensity distributions, and spatial alignment, reducing non-biological variability that can adversely affect radiomic feature stability and model performance. The modular design enables task-specific adaptation of individual preprocessing steps while maintaining overall consistency. Although demonstrated on TCIA, this framework is generalizable to other heterogeneous imaging datasets and provides a foundation for robust, large-scale computational imaging studies.

FastQpick is a command-line tool and Python library for sampling FASTQ reads with replacement. Sampling with replacement turns a single FASTQ file into an arbitrary number of bootstrap replicates, which enables uncertainty quantification and statistical analysis at the level of raw reads. This process answers questions such as how much an abundance estimate would change if the library were resequenced, or whether a low-abundance call is robust to the particular reads that were sequenced. fastQpick works efficiently on large libraries by streaming files in two passes by default: first to count reads and create a hash-based counter, and then to write the sample. It generates a full-size bootstrap replicate of a 500-million-read library in under 30 minutes with 9.4 GB of peak memory, with a low-memory mode that reduces the peak to 1.4 GB. A single-pass mode draws samples in a single read through the file, using O(1) working memory and producing an output size that is exact in expectation but not fixed. In a real yeast RNA-seq experiment, bootstrap replicates generated by fastQpick recover the sampling uncertainty of transcript abundance estimates, matching the analytic multinomial standard errors to within a few percent. fastQpick is open source and freely available under the MIT license on GitHub at https://github.com/pachterlab/fastQpick and on PyPI (pip install fastQpick).

The bio-molecular changes of exercise, and how to best optimize them for improved performance, are an important human health research question. A recent study by the Molecular Transducers of Physical Activity Consortium (MoTrPAC) used a cohort of Rattus norvegicus to produce a whole-organism molecular map of the temporal effects of endurance exercise training. This dataset, encompassing hundreds of assays across 19 different tissues, can be used to evaluate the predictive power of gene expression, understand isoform-level changes in response to exercise, and with modern tools can be examined for associations with viral infection. Our analysis of the RNA-seq data reveals that gene expression can accurately predict the amount of exercise a rat was trained in. Additionally, we find biologically relevant isoform-level differences in expression that are masked in gene-level analysis. Finally, we find a potential novel virus that may negatively impact physiological measurements. This more comprehensive analysis provides a blueprint for directing similar analyses with respect to physiological perturbations across organisms.

Contrastive principal component analysis (PCA) methods are effective approaches to dimensionality reduction where variance of a target dataset is maximized while variance of a background dataset is minimized. We previously described how contrastive PCA problems can be written as solutions to generalized eigenvalue problems that maximize particular instantiations of the Rayleigh quotient. Here, we discuss two extensions of contrastive PCA: we use kernel weighting from spatial PCA (k- ρ PCA) to contrast spatial and non-spatial axes of variation, and separately solve the Rayleigh quotient in the space of basis function coefficients (f- ρ PCA) to find modes of variation in functional data. Together, these extensions expand the scope of contrastive PCA while unifying disparate fields of spatial and functional methods within a single conceptual and mathematical framework. We showcase the utility of these extensions with several examples drawn from genomics, analyzing gene expression in cancer and immune response to vaccination.

We introduce a spectral existence criterion for the evolution of cooperation in the form of the inequality λ _max b > c , where λ _max is the leading eigenvalue of an interaction operator encoding population structure, and b and c represent benefit and cost tradeoffs, respectively. Nowak’s five rules for the evolution of cooperation correspond to cases in which the cooperation condition reduces to a scalar assortment coefficient. These results follow from the Price equation, which sheds light on a long-standing debate on the role of inclusive fitness and evolutionary dynamics in explaining the evolution of cooperation.