Rastermap: a discovery method for neural population recordings

Nature Neuroscience, Год журнала: 2024, Номер unknown

Опубликована: Окт. 16, 2024

Abstract Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time noticing patterns activity related ongoing stimuli or behaviors. With the advent large-scale recordings, such close observation data become difficult. To find neural data, we developed ‘Rastermap’, a visualization method that displays neurons as raster plot after sorting them along one-dimensional axis based on their patterns. We benchmarked Rastermap realistic simulations then used it explore recordings tens thousands from mouse cortex during spontaneous, stimulus-evoked task-evoked epochs. also applied whole-brain zebrafish recordings; wide-field imaging data; electrophysiological rat hippocampus, monkey frontal various cortical subcortical regions mice; artificial networks. Finally, illustrate high-dimensional scenarios where similar algorithms cannot be effectively.

Язык: Английский

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Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 24, 2024

Abstract While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates subdivisions, increases marker-gene efficacy, efficiently scales to millions cells.

Язык: Английский

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Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales

Annual Review of Biophysics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 19, 2025

All biological systems are subject to perturbations: due thermal fluctuations, external environments, or mutations. Yet, while composed of thousands interacting components, recent high-throughput experiments show that their response perturbations is surprisingly low-dimensional: confined only a few stereotyped changes out the many possible. Here, we explore unifying dynamical framework - soft modes explain and analyze low-dimensionality in biology, from molecules eco-systems. We argue this one makes non-trivial predictions generalize classic ideas developmental biology disparate systems, namely: phenocopying, dual buffering, global epistasis. While some these have been borne experiments, discuss how allow for far-reaching which data protein biophysics microbial ecology.

Язык: Английский

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A Transition From Interindividual Uniformity to Diversity in Appearance and Transcriptional Features at Midlife in Caenorhabditis elegans

Genes to Cells, Год журнала: 2025, Номер 30(1)

Опубликована: Янв. 1, 2025

ABSTRACT During embryogenesis, organisms function as a robust system that ensures uniformity within individuals, but they lose robustness and develop variations at advanced ages. However, when how this remains largely elusive. Here, we identified sharp transition from interindividual to diversity in the appearance transcriptional features of age‐matched Caenorhabditis elegans midlife. Convolutional neural network analysis individual alterations revealed occurs midlife, which coincides with cessation egg‐laying activity increased motility defects. This period represents young state, marked by shared homogeneous among same‐age old individuals. Transcriptional coherence individuals shows essentially same transition, coinciding features. These findings provide new framework for understanding aging trajectory C. , demonstrating occurrence loss control over homeostasis

Язык: Английский

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High order expression dependencies finely resolve cryptic states and subtypes in single cell data

Molecular Systems Biology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Язык: Английский

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