We are analyzing https://link.springer.com/article/10.1186/s13059-016-0927-y.

Title:
Design and computational analysis of single-cell RNA-sequencing experiments | Genome Biology
Description:
Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come computational challenges that are just beginning to be addressed. In this article, we highlight the computational methods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantages in various settings, the open questions for which novel methods are needed, and expected future developments in this exciting area.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {📚}

Education
Science
Telecommunications

Content Management System {📝}

What CMS is link.springer.com built with?

Custom-built

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

Traffic Estimate {📈}

What is the average monthly size of link.springer.com 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.
However, some sources were not loaded, we suggest to reload the page to get complete results.

check SE Ranking
check Ahrefs
check Similarweb
check Ubersuggest
check Semrush

How Does Link.springer.com Make Money? {💸}

We can't see how the site brings in money.

Some websites aren't about earning revenue; they're built to connect communities or raise awareness. There are numerous motivations behind creating websites. This might be one of them. Link.springer.com might be making money, but it's not detectable how they're doing it.

Keywords {🔍}

pubmed, article, singlecell, google, scholar, cells, expression, data, cell, methods, rnaseq, central, cas, genes, analysis, sequencing, scrnaseq, bulk, normalization, reads, experiments, gene, rna, variation, nat, samples, quality, technical, genome, number, spikeins, computational, biological, differences, identify, design, studies, depth, developed, bioinformatics, approaches, single, biol, content, individual, million, zeros, variability, control, counts,

Topics {✒️}

single-cell rna-seq profiling single-cell rna-sequencing experiments single-cell rna-sequencing data single-cell rna-seq experiments high-dimensional single-cell analysis quantitative single-cell rna-seq single-cell rna-seq data low-input rna-seq methodologies single-cell rna-seq technologies single-cell rna-sequencing methods large-scale single-cell data single-cell gene-expression profiles single-cell rna-seq datasets shared nearest neighbor single-cell rna-sequencing single-cell rna sequencing single-cell sequencing experiments single-cell rna-seq single cell rna-seq obtain transcriptome-wide data single-cell sequencing data high-throughput sequencing data single-cell sequencing technologies profile genome-wide expression article download pdf genome-wide gene expression full-length mrna-seq profiles single-cell expression thresholded low-magnitude observations single-cell transcriptomics applied high-throughput sequence data fluorescence-activated cell sorting bulk rna-seq experiments gene-set-enrichment analysis routinely generating high-quality snapshot scrna-seq experiments bulk rna-seq setting multi-modal expression distributions sc3 contained spike-ins reported scrna-seq experiments high-throughput spatial mapping single-cell isolation platforms bulk rna-seq include bulk rna-seq hold single-cell transcriptome variation single cell level bulk rna-seq studies article bacher scrna-seq studies normalize scrna-seq normalization amounts

Questions {❓}

Specifically, what proportion of low-quality reads is considered unusually high?

Schema {🗺️}

WebPage:
      mainEntity:
         headline:Design and computational analysis of single-cell RNA-sequencing experiments
         description:Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come computational challenges that are just beginning to be addressed. In this article, we highlight the computational methods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantages in various settings, the open questions for which novel methods are needed, and expected future developments in this exciting area.
         datePublished:2016-04-07T00:00:00Z
         dateModified:2016-04-07T00:00:00Z
         pageStart:1
         pageEnd:14
         license:http://creativecommons.org/publicdomain/zero/1.0/
         sameAs:https://doi.org/10.1186/s13059-016-0927-y
         keywords:
            Differentially Express
            Sequencing Depth
            Average Sequencing Depth
            Drosophila Genetic Reference Panel
            Share Nearest Neighbor
            Animal Genetics and Genomics
            Human Genetics
            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
         image:
            https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13059-016-0927-y/MediaObjects/13059_2016_927_Fig1_HTML.gif
         isPartOf:
            name:Genome Biology
            issn:
               1474-760X
            volumeNumber:17
            type:
               Periodical
               PublicationVolume
         publisher:
            name:BioMed Central
            logo:
               url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
               type:ImageObject
            type:Organization
         author:
               name:Rhonda Bacher
               affiliation:
                     name:University of Wisconsin
                     address:
                        name:Department of Statistics, University of Wisconsin, Madison, USA
                        type:PostalAddress
                     type:Organization
               type:Person
               name:Christina Kendziorski
               affiliation:
                     name:University of Wisconsin
                     address:
                        name:Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, USA
                        type:PostalAddress
                     type:Organization
               email:[email protected]
               type:Person
         isAccessibleForFree:1
         type:ScholarlyArticle
      context:https://schema.org
ScholarlyArticle:
      headline:Design and computational analysis of single-cell RNA-sequencing experiments
      description:Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come computational challenges that are just beginning to be addressed. In this article, we highlight the computational methods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantages in various settings, the open questions for which novel methods are needed, and expected future developments in this exciting area.
      datePublished:2016-04-07T00:00:00Z
      dateModified:2016-04-07T00:00:00Z
      pageStart:1
      pageEnd:14
      license:http://creativecommons.org/publicdomain/zero/1.0/
      sameAs:https://doi.org/10.1186/s13059-016-0927-y
      keywords:
         Differentially Express
         Sequencing Depth
         Average Sequencing Depth
         Drosophila Genetic Reference Panel
         Share Nearest Neighbor
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
      image:
         https://media.springernature.com/lw1200/springer-static/image/art%3A10.1186%2Fs13059-016-0927-y/MediaObjects/13059_2016_927_Fig1_HTML.gif
      isPartOf:
         name:Genome Biology
         issn:
            1474-760X
         volumeNumber:17
         type:
            Periodical
            PublicationVolume
      publisher:
         name:BioMed Central
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Rhonda Bacher
            affiliation:
                  name:University of Wisconsin
                  address:
                     name:Department of Statistics, University of Wisconsin, Madison, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Christina Kendziorski
            affiliation:
                  name:University of Wisconsin
                  address:
                     name:Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, USA
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
      isAccessibleForFree:1
["Periodical","PublicationVolume"]:
      name:Genome Biology
      issn:
         1474-760X
      volumeNumber:17
Organization:
      name:BioMed Central
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:University of Wisconsin
      address:
         name:Department of Statistics, University of Wisconsin, Madison, USA
         type:PostalAddress
      name:University of Wisconsin
      address:
         name:Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Rhonda Bacher
      affiliation:
            name:University of Wisconsin
            address:
               name:Department of Statistics, University of Wisconsin, Madison, USA
               type:PostalAddress
            type:Organization
      name:Christina Kendziorski
      affiliation:
            name:University of Wisconsin
            address:
               name:Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, USA
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Department of Statistics, University of Wisconsin, Madison, USA
      name:Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, USA

External Links {🔗}(337)

Analytics and Tracking {📊}

Google Tag Manager

Libraries {📚}

Clipboard.js
Prism.js

CDN Services {📦}

Crossref

5.26s.

LINK . SPRINGER . COM {}