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Statistical Analysis of Proteomic Data

Overview of attention for book
Cover of 'Statistical Analysis of Proteomic Data'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Unveiling the Links Between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff Filters
  3. Altmetric Badge
    Chapter 2 A Pipeline for Peptide Detection Using Multiple Decoys
  4. Altmetric Badge
    Chapter 3 Enhanced Proteomic Data Analysis with MetaMorpheus
  5. Altmetric Badge
    Chapter 4 Validation of MS/MS Identifications and Label-Free Quantification Using Proline
  6. Altmetric Badge
    Chapter 5 Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler
  7. Altmetric Badge
    Chapter 6 Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GSimp
  8. Altmetric Badge
    Chapter 7 Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limma
  9. Altmetric Badge
    Chapter 8 Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass
  10. Altmetric Badge
    Chapter 9 Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar
  11. Altmetric Badge
    Chapter 10 msmsEDA & msmsTests: Label-Free Differential Expression by Spectral Counts
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    Chapter 11 Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts
  13. Altmetric Badge
    Chapter 12 Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells
  14. Altmetric Badge
    Chapter 13 Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQ
  15. Altmetric Badge
    Chapter 14 Robust Prediction and Protein Selection with Adaptive PENSE
  16. Altmetric Badge
    Chapter 15 Multivariate Analysis with the R Package mixOmics
  17. Altmetric Badge
    Chapter 16 Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD
  18. Altmetric Badge
    Chapter 17 Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data
Attention for Chapter 2: A Pipeline for Peptide Detection Using Multiple Decoys
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  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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Chapter title
A Pipeline for Peptide Detection Using Multiple Decoys
Chapter number 2
Book title
Statistical Analysis of Proteomic Data
Published in
Methods in molecular biology, August 2021
DOI 10.1007/978-1-0716-1967-4_2
Pubmed ID
Book ISBNs
978-1-07-161966-7, 978-1-07-161967-4
Authors

Hasam, Syamand, Emery, Kristen, Noble, William Stafford, Keich, Uri

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 50%
Other 1 50%
Readers by discipline Count As %
Unspecified 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 October 2022.
All research outputs
#15,813,478
of 23,485,296 outputs
Outputs from Methods in molecular biology
#5,435
of 13,156 outputs
Outputs of similar age
#249,479
of 430,996 outputs
Outputs of similar age from Methods in molecular biology
#89
of 279 outputs
Altmetric has tracked 23,485,296 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,156 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 430,996 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 279 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.