Here's how SATIJALAB.ORG makes money* and how much!

*Please read our disclaimer before using our estimates.
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SATIJALAB . ORG {}

Detected CMS Systems:

  1. Analyzed Page
  2. Matching Content Categories
  3. CMS
  4. Monthly Traffic Estimate
  5. How Does Satijalab.org Make Money
  6. Keywords
  7. Topics
  8. Questions
  9. External Links
  10. Libraries
  11. Hosting Providers

We are analyzing https://satijalab.org/seurat/archive/v3.0/pbmc3k_tutorial.html.

Title:
Seurat - Guided Clustering Tutorial
Description:
No description found...
Website Age:
10 years and 11 months (reg. 2014-07-30).

Matching Content Categories {📚}

  • Science
  • Education
  • Movies

Content Management System {📝}

What CMS is satijalab.org built with?


Satijalab.org employs MYBB. But there are also traces of other content systems on the page (phpBB, 1vBulletin).

Traffic Estimate {📈}

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

🌟 Strong Traffic: 100k - 200k visitors per month


Based on our best estimate, this website will receive around 100,019 visitors per month in the current month.
However, some sources were not loaded, we suggest to reload the page to get complete results.

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How Does Satijalab.org Make Money? {💸}

We don't see any clear sign of profit-making.

Websites don't always need to be profitable; some serve as platforms for education or personal expression. Websites can serve multiple purposes. And this might be one of them. Satijalab.org could have a money-making trick up its sleeve, but it's undetectable for now.

Keywords {🔍}

cells, features, seurat, pbmc, data, feature, dataset, pca, function, plot, expression, pcs, clusters, cluster, object, number, cell, true, genes, set, reduction, markers, clustering, based, default, dims, matrix, analysis, results, metrics, highly, downstream, datasets, positive, cda, count, top, negative, ilr, find, pct, fcgra, gene, variable, mitochondrial, counts, visualize, step, expressed, msa,

Topics {✒️}

scrna-seq data [snn-cliq standard pre-processing workflow highly interconnected ‘quasi-cliques visualize feature-feature relationships /data/pbmc3k/filtered_gene_bc_matrices/hg19/ graph-based clusters determined graph-based clustering approach low-quality cells visualize qc metrics low-dimensional space standard modularity function scrna-seq data principle components based k-nearest neighbor dimensional reduction techniques linear dimensional reduction exhibit high cell unique molecular identified remove unwanted sources highly variable features adversely affect results highlight biological signal extensive technical noise highly-expressed genes qc metrics commonly increased values leading qc metrics stored place similar cells variance relationship inherent pc essentially representing top principal components feature expression measurements visualizes feature expression removing unwanted cells iteratively group cells knn graph based performing downstream analyses low p-values previously identified pcs unique feature counts test argument requires statistical test based previously defined dimensionality suggest exploring ridgeplot determine relevant sources resampling test inspired roc test returns correlated feature set dimension reduction plots genes cell doublets

Questions {❓}

  • How can I remove unwanted sources of variation, as in Seurat v2?
  • However, how many componenets should we choose to include?
  • What does data in a count matrix look like?
  • Where are QC metrics stored in Seurat?

External Links {🔗}(11)

Libraries {📚}

  • AOS
  • Bootstrap
  • jQuery
  • Quill
  • Vue.js
  • WOW
  • Zoom.js

Emails and Hosting {✉️}

Mail Servers:

  • smtp.secureserver.net
  • mailstore1.secureserver.net

Name Servers:

  • ns53.domaincontrol.com
  • ns54.domaincontrol.com
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