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Comparison of pre-processing methods for multiplex bead-based immunoassays

Overview of attention for article published in BMC Genomics, August 2016
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Title
Comparison of pre-processing methods for multiplex bead-based immunoassays
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2888-7
Pubmed ID
Authors

Tanja K. Rausch, Arne Schillert, Andreas Ziegler, Angelika Lüking, Hans-Dieter Zucht, Peter Schulz-Knappe

Abstract

High throughput protein expression studies can be performed using bead-based protein immunoassays, such as the Luminex® xMAP® technology. Technical variability is inherent to these experiments and may lead to systematic bias and reduced power. To reduce technical variability, data pre-processing is performed. However, no recommendations exist for the pre-processing of Luminex® xMAP® data. We compared 37 different data pre-processing combinations of transformation and normalization methods in 42 samples on 384 analytes obtained from a multiplex immunoassay based on the Luminex® xMAP® technology. We evaluated the performance of each pre-processing approach with 6 different performance criteria. Three performance criteria were plots. All plots were evaluated by 15 independent and blinded readers. Four different combinations of transformation and normalization methods performed well as pre-processing procedure for this bead-based protein immunoassay. The following combinations of transformation and normalization were suitable for pre-processing Luminex® xMAP® data in this study: weighted Box-Cox followed by quantile or robust spline normalization (rsn), asinh transformation followed by loess normalization and Box-Cox followed by rsn.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 28%
Student > Ph. D. Student 8 21%
Other 2 5%
Student > Bachelor 2 5%
Student > Doctoral Student 1 3%
Other 2 5%
Unknown 13 33%
Readers by discipline Count As %
Immunology and Microbiology 4 10%
Chemistry 4 10%
Agricultural and Biological Sciences 4 10%
Biochemistry, Genetics and Molecular Biology 3 8%
Engineering 3 8%
Other 7 18%
Unknown 14 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 May 2017.
All research outputs
#16,102,263
of 25,450,869 outputs
Outputs from BMC Genomics
#6,114
of 11,268 outputs
Outputs of similar age
#221,375
of 369,449 outputs
Outputs of similar age from BMC Genomics
#145
of 265 outputs
Altmetric has tracked 25,450,869 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,268 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 41st percentile – i.e., 41% 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 369,449 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 265 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.