来自杂志 Nature methods 的文献。
当前共找到 15 篇文献分享。
1.
白鸟
(2024-03-31 23:05):
#paper Single-cell chromatin state analysis with Signac. Nat Methods (2021). https://doi.org/10.1038/s41592-021-01282-5
最近分析scATAC-seq数据,用到Signac的一些函数,特别GeneActivity函数的理解。系统的学习和理解一个分析工具,还是要花大量的时间,工具包的整体分析思路,源码中如何一步步实现的,fragments到peak,peak的注释,分析延展,与同类软件的对比,需要一点点理解和消化。
IF:36.100Q1
Nature methods,
2021-11.
DOI: 10.1038/s41592-021-01282-5
PMID: 34725479
PMCID:PMC9255697
Abstract:
The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a …
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The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells.
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2.
颜林林
(2024-02-29 09:02):
#paper doi:10.1038/s41592-024-02201-0. Nature Methods, 2024, scGPT: toward building a foundation model for single-cell multi-omics using generative AI. 这篇文章使用了生成式AI大模型,来进行单细胞测序数据分析。文章并未自己收集样本和测序,而仅仅依靠已发表的公开数据或来自公共数据库的数据,进行模型训练、工具开发和性能验证,属于典型的纯生信文章,借着生成式AI概念的火热,加上结果性能表现良好,这篇文章发表到了Nature Methods杂志,很值得生信专业者学习和模仿。文章在九个多月前,就已预发表在bioRxiv上,当时整合使用了1000万个细胞的数据,在这次的正式发表版本中,整合的细胞数量增加到了3300万,模型性能也得到了进一步的改进。文章开发的模型名为scGPT,它基于生成式预训练变换器(Transformer)架构的单细胞基础模型,旨在处理和解析大规模的单细胞数据。scGPT模型展示了在多种下游任务中,如细胞类型注释、遗传扰动反应预测、多批次整合以及多组学数据整合等方面的卓越性能。研究的创新点在于首次将基础模型概念应用于单细胞生物学领域,通过自监督预训练和任务特定的微调,有效捕获和理解细胞和基因之间复杂的生物学关系。scGPT利用其强大的学习能力揭示了特定条件下的基因-基因互作,展现了转移学习中的扩展性和上下文效应。相比传统的机器学习模型,大模型能够捕捉到更为细致和全面的生物学特征,尤其是一些长距离依赖和复杂的数据关系,比如隐藏在数据背后的未知细胞类型或细胞相互作用,这大概也是这篇文章将其用于单细胞数据分析的重要出发点。
scGPT:利用生成式 AI 构建单细胞多组学基础模型
Abstract:
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a …
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Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.
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3.
白鸟
(2024-01-31 23:02):
#paper doi:10.1038/s41592-023-02117-1 SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes.因工作需要搜到这篇文献,
1.囊泡功能简介:胞外小囊泡(sEV)是由细胞分泌的微小囊泡,携带蛋白质、脂质和RNA等多种内容物,广泛存在于组织微环境中,充当细胞间信息交流的“信使”角色,生理病理过程中的关键参与者。
2.待解决:目前缺乏能够捕获到sEV复杂异质性和追踪sEV分泌的潜在细胞的高通量技术,需要证实检出的滴液为囊泡,
3.胞外小囊泡异质性追踪算法SEVtras:判定囊泡的算法,单细胞数据中追踪分泌囊泡的细胞来源;不同样本来源,广泛论证算法可行性;
从公共数据库中汇总胞外小囊泡关联基因集,利用最大期望算法(expectation–maximization, EM)推断单个液滴中胞外小囊泡的信号分值;
4.我的疑惑:对于细胞的身份和生物学功能研究是不容易的,囊泡的研究更甚,该算法可能需要更多的基准测试来证实;单细胞技术和囊泡是否适用;如何解析有限信息的囊泡表达谱?
问题1:判定捕获的barcode是不是为真实的囊泡--->通过SEVtras判别;
问题2:先暂不判定barcode身份(假定为真实的囊泡),基因表达谱可以分析出哪些内容?--->通过高表达基因的富集分析;
Abstract:
Small extracellular vesicles (sEVs) are emerging as pivotal players in a wide range of physiological and pathological processes. However, a pressing challenge has been the lack of high-throughput techniques capable …
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Small extracellular vesicles (sEVs) are emerging as pivotal players in a wide range of physiological and pathological processes. However, a pressing challenge has been the lack of high-throughput techniques capable of unraveling the intricate heterogeneity of sEVs and decoding the underlying cellular behaviors governing sEV secretion. Here we leverage droplet-based single-cell RNA sequencing (scRNA-seq) and introduce an algorithm, SEVtras, to identify sEV-containing droplets and estimate the sEV secretion activity (ESAI) of individual cells. Through extensive validations on both simulated and real datasets, we demonstrate SEVtras' efficacy in capturing sEV-containing droplets and characterizing the secretion activity of specific cell types. By applying SEVtras to four tumor scRNA-seq datasets, we further illustrate that the ESAI can serve as a potent indicator of tumor progression, particularly in the early stages. With the increasing importance and availability of scRNA-seq datasets, SEVtras holds promise in offering valuable extracellular insights into the cell heterogeneity.
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4.
颜林林
(2023-10-27 12:22):
#paper doi:10.1038/s41592-023-02043-2. Nature Methods, 2023, Comprehensive benchmarking and guidelines of mosaic variant calling strategies. 本文是一篇方法学评估对比的文章,对11个嵌合体突变鉴定工具(这其中也包括我读博期间参与的MosaicHunter)进行了系统评估。嵌合体突变是精卵结合形成合子后,在生物个体发育早期发生的一类体细胞突变,这类突变会随着发育和器官形成,被携带并分布到生物个体的不同部位。本文使用预先确定了胚系突变信息的细胞系,分步骤进行混合,以模拟生物个体早期不同阶段发生的嵌合体突变,由此得到一组拥有不同频率嵌合体突变结果(ground truth)的参考样品,用来测试和评估各鉴定工具(这个参考品制备方法,在过去几年里,也被我们用于癌症基因检测产品研发,对体细胞突变鉴定进行技术验证)。本文的评估结果显示,嵌合体突变鉴定,很大程度上取决于研究目的(及由此考虑的假设条件),根据不同目的所选择的工具及参数,可能对结果产生较大影响,本文根据评估结果对不同工具的特点进行了描述,为后续其他关于嵌合体突变的研究,以及分析工具开发,提供了参考指导和建议。
Abstract:
Rapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants represented as heterozygous, homozygous and mosaic mutations. However, the best practices for …
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Rapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants represented as heterozygous, homozygous and mosaic mutations. However, the best practices for mosaic variant calling remain disorganized owing to the technical and conceptual difficulties faced in evaluation. Here we present our benchmark of 11 feasible mosaic variant detection approaches based on a systematically designed whole-exome-level reference standard that mimics mosaic samples, supported by 354,258 control positive mosaic single-nucleotide variants and insertion-deletion mutations and 33,111,725 control negatives. We identified not only the best practice for mosaic variant detection but also the condition-dependent strengths and weaknesses of the current methods. Furthermore, feature-level evaluation and their combinatorial usage across multiple algorithms direct the way for immediate to prolonged improvements in mosaic variant detection. Our results will guide researchers in selecting suitable calling algorithms and suggest future strategies for developers.
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5.
徐炳祥
(2023-10-21 11:39):
#paper doi: 10.1038/s41592-023-01978-w Nature methods, 2023, scNanoHi-C: a single-cell long-read concatemer sequencing method to reveal high-order chromatin structures within individual cells。本文提出了一种将基于Nanopore的染色质空间构象捕获技术(Pore-C)推广到了单细胞水平的新技术,命名为scNanoHi-C。其在保持与其他单细胞Hi-C和bulk Hi-C结果的高度一致性前提下,提高了相互作用片段的产量。此外,基于ONT的长读长优势,scNanoHiC的结果可用于检查由多个位点参与的复杂基因组相互作用,也可用于在单细胞水平下检测拷贝数变异和基因组结构变异,并辅助基因组组装。本文的单细胞处理是通过多重标签策略实现的。其思路并不新鲜,其成功之处在于对复杂实验流程的把控和愿意投入大量资源。
Abstract:
The high-order three-dimensional (3D) organization of regulatory genomic elements provides a topological basis for gene regulation, but it remains unclear how multiple regulatory elements across the mammalian genome interact within …
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The high-order three-dimensional (3D) organization of regulatory genomic elements provides a topological basis for gene regulation, but it remains unclear how multiple regulatory elements across the mammalian genome interact within an individual cell. To address this, herein, we developed scNanoHi-C, which applies Nanopore long-read sequencing to explore genome-wide proximal high-order chromatin contacts within individual cells. We show that scNanoHi-C can reliably and effectively profile 3D chromatin structures and distinguish structure subtypes among individual cells. This method could also be used to detect genomic variations, including copy-number variations and structural variations, as well as to scaffold the de novo assembly of single-cell genomes. Notably, our results suggest that extensive high-order chromatin structures exist in active chromatin regions across the genome, and multiway interactions between enhancers and their target promoters were systematically identified within individual cells. Altogether, scNanoHi-C offers new opportunities to investigate high-order 3D genome structures at the single-cell level.
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6.
Vincent
(2023-09-30 23:59):
#paper https://doi.org/10.1038/s41592-018-0213-x Identification of differentially methylated cell types in epigenome-wide association studies. Nature Methods, 2018。表观基因组关联研究经常使用细胞类型的比例作为协变量,使用线性模型挖掘出与研究性状相关的差异甲基化位点,然而此类方法很难确定具体是什么细胞类型导致了该差异甲基化位点。这篇论文介绍了简单而有效的新的甲基化差异检测方法,通过引入性状与细胞类型的interaction term,在原有的统计框架下,该方法能够发现引起甲基化位点变化的具体的细胞类型。在模拟研究中,该方法表现优异,能够达到超过90%的灵敏度和特异性。
Abstract:
An outstanding challenge of epigenome-wide association studies (EWASs) performed in complex tissues is the identification of the specific cell type(s) responsible for the observed differential DNA methylation. Here we present …
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An outstanding challenge of epigenome-wide association studies (EWASs) performed in complex tissues is the identification of the specific cell type(s) responsible for the observed differential DNA methylation. Here we present a statistical algorithm called CellDMC ( https://github.com/sjczheng/EpiDISH ), which can identify differentially methylated positions and the specific cell type(s) driving the differential methylation. We validated CellDMC on in silico mixtures of DNA methylation data generated with different technologies, as well as on real mixtures from epigenome-wide association and cancer epigenome studies. CellDMC achieved over 90% sensitivity and specificity in scenarios where current state-of-the-art methods did not identify differential methylation. By applying CellDMC to an EWAS performed in buccal swabs, we identified smoking-associated differentially methylated positions occurring in the epithelial compartment, which we validated in smoking-related lung cancer. CellDMC may be useful in the identification of causal DNA-methylation alterations in disease.
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7.
小擎子
(2023-04-30 23:14):
#paper doi: 10.1038/s41592-021-01141-3 Nat Methods, 2021, Challenges in Benchmarking Metagenomic Profilers. 文献提出了一个研究宏基因组中会遇到的问题,即计算相对丰度时,不同生信工具给出的统计结果不同。区别就是有的结果是给出的序列丰度(DNA to DNA),有的结果给出的是物种丰度(DNA to Marker)。序列丰度和物种丰度的差别在于,有没有将物种的基因组大小考虑在其中。序列丰度是不考虑物种基因组大小的(如Kraken)。文章认为,基于物种丰度(即考虑物种基因组大小)的结果更具有解释性,建议严谨解释宏基因组分析结果,特别是从序列丰度得出的结果。
Abstract:
Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have …
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Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.
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8.
Vincent
(2023-02-28 19:08):
#paper DOI: https://doi.org/10.1038/s41592-021-01205-4 DOME: recommendations for supervised machine learning validation in biology. Nat Methods 2021. 机器学习方法在生物学领域变得越发重要,理想情况下机器学习预测结果最好能够被生物实验所验证,但是目前绝大多数的文章并没有配套的实验验证步骤,而只是通过计算指标来反映模型的性能,但这类计算指标往往受很多步骤的影响(例如数据集选择,训练集测试集的拆分,正负样本平衡性等等),导致最后的结论不一定稳定可靠。这篇评论文章旨在号召相关领域应该建立一套机器学习研究的写作和汇报标准,从而提高该领域内机器学习应用的交流效率。这篇文章从数据,算法,模型,评价四个方面列举了诸多影响模型性能的因素,并建议研究者在发表机器学习的文章时应该参照这四个方面的问题,详细阐述方法的细节,以此推动文章评审的效率,提高研究的透明度和可重复性
Abstract:
No abstract available.
9.
Ricardo
(2023-01-31 23:52):
#paper doi:https://doi.org/10.1038/s41592-022-01703-z Multifaceted atlases of the human brain in its infancy 脑图谱是整合、处理和分析从不同个体、来源和尺度收集的大脑特征的空间参考。这篇发表于nature methods的文章介绍了一组关于脑皮层-脑体积的联合脑图谱,以时空密集的方式绘制了从两周到两岁的人脑产后发育轨迹。这套特异性图谱捕捉了早期大脑发育的关键特征,因此有助于识别正常发育轨迹的异常。这些图谱将促进绘制婴儿大脑的不同特征,从而为精确量化皮层和皮层下变化提供一个共同的参考框架,从而增强我们对早期结构和功能发展的理解。
Abstract:
Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface-volume atlases that chart …
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Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface-volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.
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10.
徐炳祥
(2022-11-23 13:30):
#paper doi:10.1038/s41592-021-01248-7 Nature methods, 2021, Systematic evaluation of chromosome conformation capture assays。染色质空间构象捕获(3C)及由其衍生的一系列技术是当前研究真核生物染色质空间组织模式的主要高通量手段,已经取得了多项重要发现。目前,多个实验室已发展了多套不同的实验流程。本文对这些流程中的主要差异点,包括交联剂配方,使用的内切酶等对实验结果的影响进行了详细分析。通过对比多个细胞类型的结果,作者找到了最优的交联剂配方和内切酶类型,发展了一套新的,能同时适用于染色质结构与和染色质环检测的新Hi-C实验流程。
Abstract:
Chromosome conformation capture (3C) assays are used to map chromatin interactions genome-wide. Chromatin interaction maps provide insights into the spatial organization of chromosomes and the mechanisms by which they fold. …
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Chromosome conformation capture (3C) assays are used to map chromatin interactions genome-wide. Chromatin interaction maps provide insights into the spatial organization of chromosomes and the mechanisms by which they fold. Hi-C and Micro-C are widely used 3C protocols that differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of 3C experimental parameters. We identified optimal protocol variants for either loop or compartment detection, optimizing fragment size and cross-linking chemistry. We used this knowledge to develop a greatly improved Hi-C protocol (Hi-C 3.0) that can detect both loops and compartments relatively effectively. In addition to providing benchmarked protocols, this work produced ultra-deep chromatin interaction maps using Micro-C, conventional Hi-C and Hi-C 3.0 for key cell lines used by the 4D Nucleome project.
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11.
笑对人生
(2022-10-06 00:00):
#paper doi: 10.1038/nmeth.2883. PyClone: statistical inference of clonal population structure in cancer. Nat Methods. 2014 Apr;11(4):396-8.
恶性肿瘤的发生往往起源于一个癌变细胞(即肿瘤是由单克隆发育而来的)。癌变细胞在细胞增殖的过程中,由于变异或外界因素的压力选择,可能会产生在基因和表型方面与母细胞存在较大差异的子细胞。当这些具有相同遗传特点的子细胞逐渐形成一个细胞群体时,就称为是一个亚克隆。体细胞的突变是随机的,因此一个肿瘤块可能存在不同的克隆或亚克隆细胞。PyClone是一个基于分层贝叶斯的统计推断模型来分析癌症中克隆群体结构的软件。PyClone适用于多样本深度测序的体细胞突变数据,推断克隆群体时主要评估了细胞普遍性(prevalences),并解释了由于片段拷贝数变异(segmental copy-number changes)和正常细胞污染(normal-cell contamination)引起的等位基因不平衡。本研究还利用单细胞测序验证了PyClone推断克隆和亚克隆细胞群体的准确性。
Abstract:
We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative …
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We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.
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12.
吴增丁
(2022-08-31 17:15):
#paper https://doi.org/10.1038/s41592-022-01488-1 这篇于2022年发表在nature method的文章,介绍了一种基于AlphaFold2的蛋白质折叠预测的接口工具ColabFold。该工具首要解决了一个广大用户使用AlphaFold2的难点,就是在无GUP,无大存储计算资源下依然可以使用这些蛋白质结构预测的工具,并且提升了计算速度。 ColabFold工作主要在三个方面:1.在多序列比对(MSA)时用MMseqs2替换了 HMMer和HHblits的方法,从结果看提高了约50倍速度且保持高准确度。值得提一下,MSA在蛋白质结构预测中是主要的限速步骤;2.构建了自己的同源比对数据库ColabFoldDB。 相比较Big Fantastic Databse(BFD)和 MGnify database,ColabFoldDB数据库具有更好的MSA多样性。3.开发基于Google Colaboratory的notebook版本的使用接口 ,这个使用工具允许无计算资源和编程经验的用户方便使用https://github.com/sokrypton/ColabFold。当然也开发了本地命令行版本https://github.com/YoshitakaMo/localcolabfold
IF:36.100Q1
Nature methods,
2022-06.
DOI: 10.1038/s41592-022-01488-1
PMID: 35637307
PMCID:PMC9184281
Abstract:
ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables …
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ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .
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13.
笑对人生
(2022-05-31 22:11):
#paper doi: 10.1038/s41592-022-01481-8. Benchmarking spatial and single-cell transcriptomics integration methods. Nat Methods. 2022 May 16. 空间转录组(spatial transcriptomics)的发展极大提高了我们对组织RNA转录本的空间定位的认知。然而,目前空间转录组的技术并不能获取单个细胞的转录组特征。为了突破这个局限,人们往往将单细胞转录组测序(single-cell transcriptomics)和空间转录组测序进行整合分析。本文利用45对公开数据(空转和单细胞)和32份模拟数据,分别就两个整合需考虑的问题,对16种整合工具(有些工具两种功能都有)进行了基准测试(benchmark)。第一个问题是预测RNA转录本在组织空间分布(复位),共测试了8种整合方法。第二个问题是对组织的spot进行正确的单细胞类型区分和注释,共测试了12种整合方法。结果表明,解决第一个问题优势明显的有Tangram、gimVI、SpaGE。解决第二问题优势明显的是Cell2location、SpatialDWLS和RCTD。如果综合效率和准确性的话,推荐使用Tangram和Seurat。
Abstract:
No abstract available.
14.
na na na
(2022-03-29 23:30):
#paper Zhu T, Liu J, Beck S, Pan S, Capper D, Lechner M, Thirlwell C, Breeze CE, Teschendorff AE. A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nat Methods. 2022 Mar;19(3):296-306. doi: 10.1038/s41592-022-01412-7. Epub 2022 Mar 11. PMID: 35277705; PMCID: PMC8916958.
该文是3月19刚发表在Nat Methods上的一篇文章,文章主要讲的是利用组织特异性单细胞RNA 测序数据集的高分辨率特性构建了针对13种实体组织类型和40种细胞类型定义的DNA甲基化图谱,简单来说就是构建了一个利用DNA甲基化变异解析多种组织中细胞类型。目前单细胞测序主要还是以RNA表达谱为主,因此如何通过甲基化测序来准确预测组织中各种细胞类型还待研究。虽然已经有一些算法例如MehylCIBERSORT,其原理如其名字一样,都是借鉴CIBERSORT的反卷积算法,但根据其原理,只能计算成纤维细胞以及7种免疫细胞的甲基化谱,但不同肿瘤类型的组织中实际情况是更加复杂的。本文作者从多个不同肿瘤组织的单细胞测序数据出发,细胞的marker基因的mRNA表达量与其启动子区域的甲基化成显著反比的位点来定义甲基化marker。可以准确在13种组织类型和40种细胞的高分辨率DNA甲基化图谱。作者基于不同组织的中特异的细胞类型结果,分别做了验证,并且在具体的临床问题(神经细胞瘤和2期黑色素瘤的新预后关联)上,也都有良好的表现。最后作者提供了上述表达谱计算R包,并且该R包也能通过自测数据,在新的组织上构建起特异的细胞类型:https://github.com/ww880412/RPresto ;
遗憾的是,我没成功安装上还,缺少依赖包“presto”。但未找到该包,只有一个RPresto,装上后依然报错,待解决中;
IF:36.100Q1
Nature methods,
2022-03.
DOI: 10.1038/s41592-022-01412-7
PMID: 35277705
PMCID:PMC8916958
Abstract:
Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of …
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Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data.
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15.
Ricardo
(2022-02-27 22:12):
#paper doi:https://doi.org/10.1038/s41592-020-01008-z nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation 介绍这一篇2020年发表在nature methods上的文章,做医学图像算法的同学估计都知道这个非常牛逼的工作,用一套自己设计的图像分割的pipeline,没有对神经网络结构做什么改进,在23个公开的医学影像数据集上大都获得了非常好的结果。细看文章和源码,可以看到作者在数据集的预处理上、超参数的选择上、模型调优和集成以及后处理等步骤上做了相当多的工作。
Abstract:
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable …
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Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.
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