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We are analyzing https://www.nature.com/articles/s41467-020-17281-7.

Title:
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST | Nature Communications
Description:
Single-cell RNA-seq (scRNA-seq) is being used widely to resolve cellular heterogeneity. With the rapid accumulation of public scRNA-seq data, an effective and efficient cell-querying method is critical for the utilization of the existing annotations to curate newly sequenced cells. Such a querying method should be based on an accurate cell-to-cell similarity measure, and capable of handling batch effects properly. Herein, we present Cell BLAST, an accurate and robust cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric. Through extensive benchmarks and case studies, we demonstrate the effectiveness of Cell BLAST in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST provides the one-stop solution for real-world scRNA-seq cell querying and annotation. Single-cell RNA-seq (scRNA-seq) is being widely used to resolve cellular heterogeneity. Here, the authors present a cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric.
Website Age:
30 years and 10 months (reg. 1994-08-11).

Matching Content Categories {πŸ“š}

  • Telecommunications
  • Education
  • Science

Content Management System {πŸ“}

What CMS is nature.com built with?

Custom-built

No common CMS systems were detected on Nature.com, but we identified it was custom coded using Vue.js (JavaScript).

Traffic Estimate {πŸ“ˆ}

What is the average monthly size of nature.com audience?

πŸŒ† Monumental Traffic: 20M - 50M visitors per month


Based on our best estimate, this website will receive around 42,554,915 visitors per month in the current month.

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How Does Nature.com Make Money? {πŸ’Έ}


Display Ads {🎯}


The website utilizes display ads within its content to generate revenue. Check the next section for further revenue estimates.

Ads are managed by yourbow.com. Particular relationships are as follows:

Direct Advertisers (10)
google.com, pmc.com, doceree.com, yourbow.com, audienciad.com, onlinemediasolutions.com, advibe.media, aps.amazon.com, getmediamx.com, onomagic.com

Reseller Advertisers (38)
conversantmedia.com, rubiconproject.com, pubmatic.com, appnexus.com, openx.com, smartadserver.com, lijit.com, sharethrough.com, video.unrulymedia.com, google.com, yahoo.com, triplelift.com, onetag.com, sonobi.com, contextweb.com, 33across.com, indexexchange.com, media.net, themediagrid.com, adform.com, richaudience.com, sovrn.com, improvedigital.com, freewheel.tv, smaato.com, yieldmo.com, amxrtb.com, adyoulike.com, adpone.com, criteo.com, smilewanted.com, 152media.info, e-planning.net, smartyads.com, loopme.com, opera.com, mediafuse.com, betweendigital.com

How Much Does Nature.com Make? {πŸ’°}


Display Ads {🎯}

$536,300 per month
Our calculations suggest that Nature.com earns between $357,503 and $983,134 monthly online from display advertisements.

Keywords {πŸ”}

cell, data, cells, supplementary, pubmed, blast, fig, batch, article, datasets, model, reference, types, query, posterior, google, scholar, methods, gene, singlecell, left, cas, dataset, expression, distribution, type, central, bbb, embedding, querying, genes, analysis, based, effect, alignment, mathop, nature, distance, space, adversarial, similarity, sim, method, average, negative, scrnaseq, celltype, models, generative, ontology,

Topics {βœ’οΈ}

nature portfolio privacy policy /gao-lab/cell_blast/tree/master/local shelf querying service org/packages/release/bioc/html/sva single-cell rna-seq denoising single-cell rna-seq profiles high-resolution cell-type description single-cell rna-seq data io/scrna advertising single-cell rna-sequencing data {\mathrm{dec}}_{\mathbf{\mu }}\left c_i\preccurlyeq c_k\preccurlyeq c_j single-cell transcriptomics databases {\mathrm{dec}}_{\mathbf{\theta }}\left c_{{\mathrm{ref}}}\preccurlyeq c_k} social media single cell portal cell querying tools imbalance nature index reprints modeling scrna-seq data $$\mathop {{\max }}\limits_{{\mathrm{ single-cell rna sequencing large-scale machine learning single-cell transcriptomics data query-reference nearest-neighbor pair functional genomics research india /single-cell-gene-expression/datasets/1 noisy scrna-seq data plant gene research nature 560 nature 555 nature public scrna-seq data high throughput sequencing”[filter] tensorflow43 python library }} \sim p_{{\mathrm{data}}}\left exploring single-cell data quad \mathop {\sum}\limits_{ special online-tuning mode single-cell sequencing platforms multi-dimensional posterior distributions national key research high-dimensional transcriptomic space web portal https query-based cell-type prediction

Schema {πŸ—ΊοΈ}

WebPage:
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         headline:Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
         description:Single-cell RNA-seq (scRNA-seq) is being used widely to resolve cellular heterogeneity. With the rapid accumulation of public scRNA-seq data, an effective and efficient cell-querying method is critical for the utilization of the existing annotations to curate newly sequenced cells. Such a querying method should be based on an accurate cell-to-cell similarity measure, and capable of handling batch effects properly. Herein, we present Cell BLAST, an accurate and robust cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric. Through extensive benchmarks and case studies, we demonstrate the effectiveness of Cell BLAST in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST provides the one-stop solution for real-world scRNA-seq cell querying and annotation. Single-cell RNA-seq (scRNA-seq) is being widely used to resolve cellular heterogeneity. Here, the authors present a cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric.
         datePublished:2020-07-10T00:00:00Z
         dateModified:2020-07-10T00:00:00Z
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ScholarlyArticle:
      headline:Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
      description:Single-cell RNA-seq (scRNA-seq) is being used widely to resolve cellular heterogeneity. With the rapid accumulation of public scRNA-seq data, an effective and efficient cell-querying method is critical for the utilization of the existing annotations to curate newly sequenced cells. Such a querying method should be based on an accurate cell-to-cell similarity measure, and capable of handling batch effects properly. Herein, we present Cell BLAST, an accurate and robust cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric. Through extensive benchmarks and case studies, we demonstrate the effectiveness of Cell BLAST in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST provides the one-stop solution for real-world scRNA-seq cell querying and annotation. Single-cell RNA-seq (scRNA-seq) is being widely used to resolve cellular heterogeneity. Here, the authors present a cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric.
      datePublished:2020-07-10T00:00:00Z
      dateModified:2020-07-10T00:00:00Z
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                     name:Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing, China
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      name:Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing, China
      name:Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing, China
      name:Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing, China
      name:Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing, China

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