↓ Skip to main content

Impact of missing data imputation methods on gene expression clustering and classification

Overview of attention for article published in BMC Bioinformatics, February 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
99 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Impact of missing data imputation methods on gene expression clustering and classification
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0494-3
Pubmed ID
Authors

Marcilio CP de Souto, Pablo A Jaskowiak, Ivan G Costa

Abstract

Several missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms. Initially, most algorithms were assessed with an emphasis on the accuracy of the imputation, using metrics such as the root mean squared error. However, it has become clear that the success of the estimation of the expression value should be evaluated in more practical terms as well. One can consider, for example, the ability of the method to preserve the significant genes in the dataset, or its discriminative/predictive power for classification/clustering purposes. We performed a broad analysis of the impact of five well-known missing value imputation methods on three clustering and four classification methods, in the context of 12 cancer gene expression datasets. We employed a statistical framework, for the first time in this field, to assess whether different imputation methods improve the performance of the clustering/classification methods. Our results suggest that the imputation methods evaluated have a minor impact on the classification and downstream clustering analyses. Simple methods such as replacing the missing values by mean or the median values performed as well as more complex strategies. The datasets analyzed in this study are available at http://costalab.org/Imputation/ .

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
Netherlands 2 2%
France 1 1%
Malaysia 1 1%
Germany 1 1%
Spain 1 1%
United States 1 1%
Unknown 90 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 24%
Student > Master 17 17%
Researcher 16 16%
Student > Bachelor 12 12%
Professor > Associate Professor 7 7%
Other 16 16%
Unknown 7 7%
Readers by discipline Count As %
Computer Science 35 35%
Agricultural and Biological Sciences 18 18%
Engineering 11 11%
Biochemistry, Genetics and Molecular Biology 8 8%
Mathematics 6 6%
Other 8 8%
Unknown 13 13%

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 02 March 2015.
All research outputs
#7,020,454
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,914
of 4,195 outputs
Outputs of similar age
#114,470
of 210,702 outputs
Outputs of similar age from BMC Bioinformatics
#88
of 123 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 210,702 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.