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ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data

Overview of attention for article published in BMC Bioinformatics, October 2015
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
25 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
148 Mendeley
citeulike
2 CiteULike
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Title
ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0763-1
Pubmed ID
Authors

Ivana Ihnatova, Eva Budinska

Abstract

Pathway analysis methods, in which differentially expressed genes are mapped to databases of reference pathways and relative enrichment is assessed, help investigators to propose biologically relevant hypotheses. The last generation of pathway analysis methods takes into account the topological structure of a pathway, which helps to increase both specificity and sensitivity of the findings. Simultaneously, the RNA-Seq technology is gaining popularity and becomes widely used for gene expression profiling. Unfortunately, majority of topological pathway analysis methods remains without implementation and if an implementation exists, it is limited in various factors. We developed a new R/Bioconductor package ToPASeq offering uniform interface to seven distinct topology-based pathway analysis methods, of which three we implemented de-novo and four were adjusted from existing implementations. Apart this, ToPASeq offers a set of tailored visualization functions and functions for importing and manipulating pathways and their topologies, facilitating the application of the methods on different species. The package can be used to compare the differential expression of pathways between two conditions on both gene expression microarray and RNA-Seq data. The package is written in R and is available from Bioconductor 3.2 using AGPL-3 license. ToPASeq is a novel package that offers seven distinct methods for topology-based pathway analysis, which are easily applicable on microarray as well as RNA-Seq data, both in human and other species. At the same time, it provides specific tools for visualization of the results.

X Demographics

X Demographics

The data shown below were collected from the profiles of 25 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Spain 2 1%
Korea, Republic of 1 <1%
Sri Lanka 1 <1%
Czechia 1 <1%
United Kingdom 1 <1%
Taiwan 1 <1%
Unknown 138 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 43 29%
Student > Ph. D. Student 36 24%
Student > Master 18 12%
Student > Bachelor 9 6%
Other 6 4%
Other 15 10%
Unknown 21 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 32%
Biochemistry, Genetics and Molecular Biology 36 24%
Computer Science 14 9%
Medicine and Dentistry 12 8%
Engineering 3 2%
Other 9 6%
Unknown 26 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 27 April 2016.
All research outputs
#1,679,843
of 25,116,143 outputs
Outputs from BMC Bioinformatics
#297
of 7,653 outputs
Outputs of similar age
#24,247
of 291,151 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 158 outputs
Altmetric has tracked 25,116,143 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,653 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 291,151 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 158 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 96% of its contemporaries.