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Detect tissue heterogeneity in gene expression data with BioQC

Overview of attention for article published in BMC Genomics, April 2017
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3 tweeters

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25 Mendeley
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Title
Detect tissue heterogeneity in gene expression data with BioQC
Published in
BMC Genomics, April 2017
DOI 10.1186/s12864-017-3661-2
Pubmed ID
Authors

Jitao David Zhang, Klas Hatje, Gregor Sturm, Clemens Broger, Martin Ebeling, Martine Burtin, Fabiola Terzi, Silvia Ines Pomposiello, Laura Badi

Abstract

Gene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted. We introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies. Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR. Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity. Our experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data.

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

Geographical breakdown

Country Count As %
Germany 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 32%
Researcher 4 16%
Student > Bachelor 2 8%
Student > Doctoral Student 2 8%
Student > Postgraduate 2 8%
Other 4 16%
Unknown 3 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 24%
Agricultural and Biological Sciences 5 20%
Computer Science 5 20%
Medicine and Dentistry 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 3 12%
Unknown 3 12%

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 06 April 2017.
All research outputs
#5,169,240
of 9,652,674 outputs
Outputs from BMC Genomics
#3,928
of 6,595 outputs
Outputs of similar age
#137,039
of 262,389 outputs
Outputs of similar age from BMC Genomics
#65
of 91 outputs
Altmetric has tracked 9,652,674 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,595 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 35th percentile – i.e., 35% 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 262,389 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.