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Statistical Genomics

Overview of attention for book
Cover of 'Statistical Genomics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Overview of Sequence Data Formats
  3. Altmetric Badge
    Chapter 2 Integrative Exploratory Analysis of Two or More Genomic Datasets
  4. Altmetric Badge
    Chapter 3 Study Design for Sequencing Studies
  5. Altmetric Badge
    Chapter 4 Genomic Annotation Resources in R/Bioconductor
  6. Altmetric Badge
    Chapter 5 The Gene Expression Omnibus Database
  7. Altmetric Badge
    Chapter 6 A Practical Guide to The Cancer Genome Atlas (TCGA)
  8. Altmetric Badge
    Chapter 7 Working with Oligonucleotide Arrays
  9. Altmetric Badge
    Chapter 8 Meta-Analysis in Gene Expression Studies
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    Chapter 9 Practical Analysis of Genome Contact Interaction Experiments
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    Chapter 10 Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data
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    Chapter 11 Variant Calling From Next Generation Sequence Data
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    Chapter 12 Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes
  14. Altmetric Badge
    Chapter 13 NGS-QC Generator: A Quality Control System for ChIP-Seq and Related Deep Sequencing-Generated Datasets
  15. Altmetric Badge
    Chapter 14 Operating on Genomic Ranges Using BEDOPS
  16. Altmetric Badge
    Chapter 15 GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality
  17. Altmetric Badge
    Chapter 16 Visualizing Genomic Data Using Gviz and Bioconductor
  18. Altmetric Badge
    Chapter 17 Introducing Machine Learning Concepts with WEKA
  19. Altmetric Badge
    Chapter 18 Experimental Design and Power Calculation for RNA-seq Experiments
  20. Altmetric Badge
    Chapter 19 It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR
Attention for Chapter 6: A Practical Guide to The Cancer Genome Atlas (TCGA)
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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Chapter title
A Practical Guide to The Cancer Genome Atlas (TCGA)
Chapter number 6
Book title
Statistical Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3578-9_6
Pubmed ID
Book ISBNs
978-1-4939-3576-5, 978-1-4939-3578-9
Authors

Zhining Wang, Mark A. Jensen, Jean Claude Zenklusen, Wang, Zhining, Jensen, Mark A, Zenklusen, Jean Claude, Jensen, Mark A.

Editors

Ewy Mathé, Sean Davis

Abstract

The Cancer Genome Atlas (TCGA) is one of the most ambitious and successful cancer genomics programs to date. The TCGA program has generated, analyzed, and made available genomic sequence, expression, methylation, and copy number variation data on over 11,000 individuals who represent over 30 different types of cancer. This chapter provides a brief overview of the TCGA program and detailed instructions and tips for investigators on how to find, access, and download this data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 211 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Germany 1 <1%
Unknown 209 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 14%
Researcher 23 11%
Student > Master 23 11%
Student > Bachelor 21 10%
Student > Doctoral Student 12 6%
Other 37 18%
Unknown 66 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 63 30%
Agricultural and Biological Sciences 17 8%
Medicine and Dentistry 17 8%
Pharmacology, Toxicology and Pharmaceutical Science 9 4%
Computer Science 7 3%
Other 28 13%
Unknown 70 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 March 2016.
All research outputs
#2,642,826
of 23,207,489 outputs
Outputs from Methods in molecular biology
#490
of 13,305 outputs
Outputs of similar age
#46,742
of 395,410 outputs
Outputs of similar age from Methods in molecular biology
#72
of 1,471 outputs
Altmetric has tracked 23,207,489 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,305 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 395,410 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 1,471 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.