Nature computational science最新文献_第10页

Joint inference of discrete and continuous factors captures variability across and within cell types 离散因子和连续因子的联合推断可捕捉细胞类型间和细胞类型内的变异性。

IF 12

Nature computational science Pub Date : 2024-09-23 DOI: 10.1038/s43588-024-00696-3

引用次数: 0

Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS 利用 MMIDAS 联合推断单细胞数据集中的离散细胞类型和连续类型特异性变异性

IF 12

Nature computational science Pub Date : 2024-09-23 DOI: 10.1038/s43588-024-00683-8

Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül

{"title":"Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS","authors":"Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül","doi":"10.1038/s43588-024-00683-8","DOIUrl":"10.1038/s43588-024-00683-8","url":null,"abstract":"Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here we propose an unsupervised method, Mixture Model Inference with Discrete-coupled AutoencoderS (MMIDAS), which combines a generalized mixture model with a multi-armed deep neural network to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both unimodal and multimodal datasets. Clustering in high-dimensional spaces with a large number of clusters and identifying common aspects of within-cluster variability remain challenging. Here the authors develop an unsupervised method for this purpose and demonstrate it on brain single-cell datasets.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"706-722"},"PeriodicalIF":12.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Biophysically interpretable inference of cell types from multimodal sequencing data 从多模态测序数据推断细胞类型的生物物理可解释性

IF 12

Nature computational science Pub Date : 2024-09-20 DOI: 10.1038/s43588-024-00689-2

Tara Chari, Gennady Gorin, Lior Pachter

{"title":"Biophysically interpretable inference of cell types from multimodal sequencing data","authors":"Tara Chari, Gennady Gorin, Lior Pachter","doi":"10.1038/s43588-024-00689-2","DOIUrl":"10.1038/s43588-024-00689-2","url":null,"abstract":"Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or ‘clusters’ in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for ‘clusters’ through the governing parameters of cellular processes. MeK-means clusters single-cell multimodal data by linking modalities through their biophysical relationships. We redefine clusters through transcription kinetics to reveal how RNA production and processing drive cellular diversity and disease.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"677-689"},"PeriodicalIF":12.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142317000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Delineating cell types with transcriptional kinetics 利用转录动力学划分细胞类型

IF 12

Nature computational science Pub Date : 2024-09-20 DOI: 10.1038/s43588-024-00691-8

Yicheng Gao, Qi Liu

引用次数: 0

Automated customization of large-scale spiking network models to neuronal population activity 根据神经元群体活动自动定制大规模尖峰网络模型

IF 12

Nature computational science Pub Date : 2024-09-16 DOI: 10.1038/s43588-024-00688-3

Shenghao Wu, Chengcheng Huang, Adam C. Snyder, Matthew A. Smith, Brent Doiron, Byron M. Yu

{"title":"Automated customization of large-scale spiking network models to neuronal population activity","authors":"Shenghao Wu, Chengcheng Huang, Adam C. Snyder, Matthew A. Smith, Brent Doiron, Byron M. Yu","doi":"10.1038/s43588-024-00688-3","DOIUrl":"10.1038/s43588-024-00688-3","url":null,"abstract":"Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity’s dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function. An automatic framework, SNOPS, is developed for configuring a spiking network model to reproduce neuronal recordings. It is used to discover previously unknown limitations of spiking network models, thereby guiding model development.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"690-705"},"PeriodicalIF":12.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142255499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deconstructing the compounds of altruism 解构利他主义的化合物

IF 12

Nature computational science Pub Date : 2024-09-12 DOI: 10.1038/s43588-024-00690-9

Jie Hu

引用次数: 0

Heat wave attribution assessment using deep learning 利用深度学习评估热浪归因

IF 12

Nature computational science Pub Date : 2024-09-12 DOI: 10.1038/s43588-024-00700-w

Fernando Chirigati

引用次数: 0

The motive cocktail in altruistic behaviors 利他行为中的动机鸡尾酒

IF 12

Nature computational science Pub Date : 2024-09-12 DOI: 10.1038/s43588-024-00685-6

Xiaoyan Wu, Xiangjuan Ren, Chao Liu, Hang Zhang

{"title":"The motive cocktail in altruistic behaviors","authors":"Xiaoyan Wu, Xiangjuan Ren, Chao Liu, Hang Zhang","doi":"10.1038/s43588-024-00685-6","DOIUrl":"10.1038/s43588-024-00685-6","url":null,"abstract":"Prosocial motives such as social equality and efficiency are key to altruistic behaviors. However, predicting the range of altruistic behaviors in varying contexts and individuals proves challenging if we limit ourselves to one or two motives. Here we demonstrate the numerous, interdependent motives in altruistic behaviors and the possibility to disentangle them through behavioral experimental data and computational modeling. In one laboratory experiment (N = 157) and one preregistered online replication (N = 1,258), across 100 different situations, we found that both third-party punishment and third-party helping behaviors (that is, an unaffected individual punishes the transgressor or helps the victim) aligned best with a model of seven socioeconomic motives, referred to as a motive cocktail. For instance, the inequality discounting motives imply that individuals, when confronted with costly interventions, behave as if the inequality between others barely exists. The motive cocktail model also provides a unified explanation for the differences in intervention willingness between second parties (victims) and third parties, and between punishment and helping. The authors find, through experimental data and computational modeling, that altruistic acts stem from a motive cocktail of up to seven social and economic motives, whose strengths explain distinct behavior patterns across individuals and situations.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"659-676"},"PeriodicalIF":12.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00685-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the role of metamaterials in achieving advantage in optical computing 探索超材料在实现光学计算优势方面的作用。

IF 12

Nature computational science Pub Date : 2024-08-27 DOI: 10.1038/s43588-024-00657-w

Yandong Li, Francesco Monticone

引用次数: 0

Computational challenges in additive manufacturing for metamaterials design 超材料设计增材制造中的计算挑战。

IF 12

Nature computational science Pub Date : 2024-08-27 DOI: 10.1038/s43588-024-00669-6

Keith A. Brown, Grace X. Gu

引用次数: 0