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DOI . ORG {}

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Doi.org Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. Schema
  10. Social Networks
  11. External Links
  12. Analytics And Tracking
  13. Libraries
  14. Hosting Providers
  15. CDN Services

We began analyzing https://www.nature.com/articles/nbt.3711, but it redirected us to https://www.nature.com/articles/nbt.3711. The analysis below is for the second page.

Title[redir]:
Revealing the vectors of cellular identity with single-cell genomics | Nature Biotechnology
Description:
Computational methods for analyzing single-cell data are uncovering new ways of defining cells. Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell

Matching Content Categories {📚}

  • Education
  • Science
  • Telecommunications

Content Management System {📝}

What CMS is doi.org built with?

Custom-built

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

Traffic Estimate {📈}

What is the average monthly size of doi.org audience?

🌠 Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 5,000,019 visitors per month in the current month.
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How Does Doi.org Make Money? {💸}

We find it hard to spot revenue streams.

Earning money isn't the goal of every website; some are designed to offer support or promote social causes. People have different reasons for creating websites. This might be one such reason. Doi.org has a secret sauce for making money, but we can't detect it yet.

Keywords {🔍}

pubmed, article, google, scholar, cas, central, singlecell, cell, nat, data, expression, analysis, sequencing, gene, nature, cells, rnaseq, methods, science, single, reveals, biol, genome, biotechnol, rna, human, usa, cellular, genet, sci, bioinformatics, rev, proc, stem, res, natl, acad, noise, heterogeneity, regev, experiments, profiling, institute, identity, computational, access, control, transcriptomics, regulatory, mammalian,

Topics {✒️}

permissions reprints nature portfolio privacy policy standard library preparations quantitative single-cell rna-seq single-cell rna-seq reveals single-cell rna-seq supports advertising single-cell rna-sequencing data single-cell rna-seq data single-cell sequencing-based technologies single-cell rna-seq experiments thermo fisher scientific social media high-dimensional single-cell analysis large-scale single-cell data single-cell transcriptomics reveals scientific advisory board single-cell rna sequencing single-cell rna-seq single-cell exome sequencing single-cell sequencing technologies single-cell cancer genomics single-cell sequencing data single-cell sequencing experiments single-cell transcriptome data single-cell methylome landscapes system wide analyses single-cell transcriptomics applied full-length mrna-seq single-cell experiments continues epigenomic cell-state dynamics early blood development single-cell expression profiling single-cell mutational profiling jak2-negative myeloproliferative neoplasm optimized single-molecule barcodes single-cell genome sequencing combined single-cell functional high-dimensional cytometry data conditional density-based analysis continuous dynamic transitions single-neuron sequencing analysis single-cell mass cytometry single-cell genomics visualizing high-dimensional data author correspondence kernel-based similarity learning cortical development rna-seq data

Questions {❓}

  • Cellular heterogeneity: do differences make a difference?
  • When is “nearest neighbor” meaningful?

Schema {🗺️}

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         description:Computational methods for analyzing single-cell data are uncovering new ways of defining cells. Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges—from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.
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      headline:Revealing the vectors of cellular identity with single-cell genomics
      description:Computational methods for analyzing single-cell data are uncovering new ways of defining cells. Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges—from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.
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Social Networks {👍}(2)

External Links {🔗}(880)

Analytics and Tracking {📊}

  • Google Tag Manager

Libraries {📚}

  • AOS
  • Prism.js
  • Zoom.js

Emails and Hosting {✉️}

Mail Servers:

  • mx.zoho.eu
  • mx2.zoho.eu
  • mx3.zoho.eu

Name Servers:

  • josh.ns.cloudflare.com
  • zita.ns.cloudflare.com

CDN Services {📦}

  • Crossref

5.74s.