↓ Skip to main content

Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat

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
2 tweeters

Citations

dimensions_citation
58 Dimensions

Readers on

mendeley
80 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
Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0478-3
Pubmed ID
Authors

Caleb K Stein, Pingping Qu, Joshua Epstein, Amy Buros, Adam Rosenthal, John Crowley, Gareth Morgan, Bart Barlogie

Abstract

Gene expression profiling (GEP) via microarray analysis is a widely used tool for assessing risk and other patient diagnostics in clinical settings. However, non-biological factors such as systematic changes in sample preparation, differences in scanners, and other potential batch effects are often unavoidable in long-term studies and meta-analysis. In order to reduce the impact of batch effects on microarray data, Johnson, Rabinovic, and Li developed ComBat for use when combining batches of gene expression microarray data. We propose a modification to ComBat that centers data to the location and scale of a pre-determined, 'gold-standard' batch. This modified ComBat (M-Combat) is designed specifically in the context of meta-analysis and batch effect adjustment for use with predictive models that are validated and fixed on historical data from a 'gold-standard' batch. We combined data from MIRT across two batches ('Old' and 'New' Kit sample preparation) as well as external data sets from the HOVON-65/GMMG-HD4 and MRC-IX trials into a combined set, first without transformation and then with both ComBat and M-ComBat transformations. Fixed and validated gene risk signatures developed at MIRT on the Old Kit standard (GEP5, GEP70, and GEP80 risk scores) were compared across these combined data sets. Both ComBat and M-ComBat eliminated all of the differences among probes caused by systematic batch effects (over 98% of all untransformed probes were significantly different by ANOVA with 0.01 q-value threshold reduced to zero significant probes with ComBat and M-ComBat). The agreement in mean and distribution of risk scores, as well as the proportion of high-risk subjects identified, coincided with the 'gold-standard' batch more with M-ComBat than with ComBat. The performance of risk scores improved overall using either ComBat or M-Combat; however, using M-ComBat and the original, optimal risk cutoffs allowed for greater ability in our study to identify smaller cohorts of high-risk subjects. M-ComBat is a practical modification to an accepted method that offers greater power to control the location and scale of batch-effect adjusted data. M-ComBat allows for historical models to function as intended on future samples despite known, often unavoidable systematic changes to gene expression data.

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 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Netherlands 1 1%
Sweden 1 1%
Denmark 1 1%
Unknown 76 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 31%
Student > Ph. D. Student 11 14%
Student > Master 11 14%
Other 7 9%
Student > Bachelor 6 8%
Other 13 16%
Unknown 7 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 28%
Agricultural and Biological Sciences 18 23%
Computer Science 11 14%
Medicine and Dentistry 6 8%
Physics and Astronomy 3 4%
Other 11 14%
Unknown 9 11%

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 25 February 2015.
All research outputs
#7,201,794
of 9,788,655 outputs
Outputs from BMC Bioinformatics
#3,315
of 4,160 outputs
Outputs of similar age
#135,283
of 210,246 outputs
Outputs of similar age from BMC Bioinformatics
#97
of 123 outputs
Altmetric has tracked 9,788,655 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 4,160 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 16th percentile – i.e., 16% 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 210,246 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% 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 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.