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  6. How Much Does Nature.com Make
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We are analyzing https://www.nature.com/articles/s41592-018-0229-2.

Title:
Deep generative modeling for single-cell transcriptomics | Nature Methods
Description:
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task. scVI is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream analyses.
Website Age:
30 years and 10 months (reg. 1994-08-11).

Matching Content Categories {πŸ“š}

  • Education
  • Science
  • Telecommunications

Content Management System {πŸ“}

What CMS is nature.com built with?

Custom-built

No common CMS systems were detected on Nature.com, and no known web development framework was identified.

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”}

pubmed, article, data, google, scholar, singlecell, scvi, central, nature, cells, analysis, supplementary, methods, cell, dataset, cas, expression, figure, gene, nat, values, access, simlr, information, imputation, cortex, latent, space, number, content, deep, variational, single, batch, rnaseq, yosef, observed, learning, datasets, genome, models, xaxis, posterior, original, cookies, genes, biology, bioinformatics, model, zifa,

Topics {βœ’οΈ}

nature portfolio journals permissions reprints nature portfolio privacy policy adjusted rand index single-cell rna-sequencing data single-cell rna-seq data advertising single-cell network biology social media library size /single-cell-gene-expression/datasets author information authors single-cell sequencing data single-cell rna-seq nature+ nature 555 nature 541 nature single-cell analysis gregory scaling single-cell genomics kernel-based similarity learning distribution-matching residual networks author correspondence learning context-specific gene cell–cell similarity matrix rna-seq data t-sne coordinates provided k-nearest-neighbors graph single cell data auto-encoding variational bayes published maps downsampled version versus permissions springerlink instant access single-cell genomics single-cell transcriptomics human cell atlas /romain-lopez/scvi-reproducibility microarray expression data real data analysis differential expression analysis org/content/early/2017/02/25/111591 org/content/early/2017/10/06/199315 org/content/early/2018/04/13/300681 org/content/early/2018/05/16/318295 org/content/early/2018/05/18/235382 quality control metrics expected expression level applying differential expression

Schema {πŸ—ΊοΈ}

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         headline:Deep generative modeling for single-cell transcriptomics
         description:Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task. scVI is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream analyses.
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      headline:Deep generative modeling for single-cell transcriptomics
      description:Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task. scVI is a ready-to-use generative deep learning tool for large-scale single-cell RNA-seq data that enables raw data processing and a wide range of rapid and accurate downstream analyses.
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Social Networks {πŸ‘}(1)

External Links {πŸ”—}(160)

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