↓ Skip to main content

Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment

Overview of attention for article published in BMC Bioinformatics, January 2016
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
38 Mendeley
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
Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0870-z
Pubmed ID
Authors

Roman Hornung, Anne-Laure Boulesteix, David Causeur

Abstract

In the context of high-throughput molecular data analysis it is common that the observations included in a dataset form distinct groups; for example, measured at different times, under different conditions or even in different labs. These groups are generally denoted as batches. Systematic differences between these batches not attributable to the biological signal of interest are denoted as batch effects. If ignored when conducting analyses on the combined data, batch effects can lead to distortions in the results. In this paper we present FAbatch, a general, model-based method for correcting for such batch effects in the case of an analysis involving a binary target variable. It is a combination of two commonly used approaches: location-and-scale adjustment and data cleaning by adjustment for distortions due to latent factors. We compare FAbatch extensively to the most commonly applied competitors on the basis of several performance metrics. FAbatch can also be used in the context of prediction modelling to eliminate batch effects from new test data. This important application is illustrated using real and simulated data. We implemented FAbatch and various other functionalities in the R package bapred available online from CRAN. FAbatch is seen to be competitive in many cases and above average in others. In our analyses, the only cases where it failed to adequately preserve the biological signal were when there were extremely outlying batches and when the batch effects were very weak compared to the biological signal. As seen in this paper batch effect structures found in real datasets are diverse. Current batch effect adjustment methods are often either too simplistic or make restrictive assumptions, which can be violated in real datasets. Due to the generality of its underlying model and its ability to perform well FAbatch represents a reliable tool for batch effect adjustment for most situations found in practice.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
United States 1 3%
Unknown 36 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 34%
Researcher 6 16%
Student > Master 5 13%
Other 3 8%
Student > Bachelor 3 8%
Other 2 5%
Unknown 6 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 21%
Computer Science 8 21%
Agricultural and Biological Sciences 7 18%
Mathematics 4 11%
Neuroscience 2 5%
Other 1 3%
Unknown 8 21%

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 12 January 2016.
All research outputs
#8,195,566
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#3,494
of 4,169 outputs
Outputs of similar age
#226,974
of 330,976 outputs
Outputs of similar age from BMC Bioinformatics
#114
of 134 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. This one is in the 12th percentile – i.e., 12% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 7th percentile – i.e., 7% 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 330,976 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.