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TruSeq-Based Gene Expression Analysis of Formalin-Fixed Paraffin-Embedded (FFPE) Cutaneous T-Cell Lymphoma Samples: Subgroup Analysis Results and Elucidation of Biases from FFPE Sample Processing on…

Overview of attention for article published in Frontiers in Medicine, September 2017
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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Title
TruSeq-Based Gene Expression Analysis of Formalin-Fixed Paraffin-Embedded (FFPE) Cutaneous T-Cell Lymphoma Samples: Subgroup Analysis Results and Elucidation of Biases from FFPE Sample Processing on the TruSeq Platform
Published in
Frontiers in Medicine, September 2017
DOI 10.3389/fmed.2017.00153
Pubmed ID
Authors

Philippe Lefrançois, Michael T. Tetzlaff, Linda Moreau, Andrew K. Watters, Elena Netchiporouk, Nathalie Provost, Martin Gilbert, Xiao Ni, Denis Sasseville, Madeleine Duvic, Ivan V. Litvinov

Abstract

Cutaneous T-cell lymphomas (CTCLs) are a heterogeneous group of malignancies with courses ranging from indolent to potentially lethal. We recently studied in a 157 patient cohort gene expression profiles generated by the TruSeq targeted RNA gene expression sequencing. We observed that the sequencing library quality and depth from formalin-fixed paraffin-embedded (FFPE) skin samples were significantly lower when biopsies were obtained prior to 2009. We also observed that the fresh CTCL samples clustered together, even though they included stage I-IV disease. In this study, we compared TruSeq gene expression patterns in older (≤2008) vs. more recent (≥2009) FFPE samples to determine whether these clustering analyses and earlier described differentially expressed gene findings are robust when analyzed based on the year of biopsy. We also explored biases found in FFPE samples when subjected to the TruSeq analysis of gene expression. Our results showed that ≤2008 and ≥2009 samples clustered equally well to the full data set and, importantly, both analyses produced nearly identical trends and findings. Specifically, both analyses enriched nearly identical DEGs when comparing benign vs. (1) stage I-IV and (2) stage IV (alone) CTCL samples. Results obtained using either ≤2008 or ≥2009 samples were strongly correlated. Furthermore, by using subgroup analyses, we were able to identify additional novel differentially expressed genes (DEGs), which did not reach statistical significance in the prior full data set analysis. Those included CTCL-upregulated BCL11A, SELL, IRF1, SMAD1, CASP1, BIRC5, and MAX and CTCL-downregulated MDM4, SERPINB3, and THBS4 genes. With respect to sample biases, no matter if we performed subgroup analyses or full data set analysis, fresh samples tightly clustered together. While principal component analysis revealed that fresh samples were spatially closer together, indicating some preprocessing batch effect, they remained in the proximity to other normal/benign and FFPE CTCL samples and were not clustering as outliers by themselves. Notably, this did not affect the determination of DEGs when analyzing ≥2009 samples (fresh and FFPE biopsies) vs. ≥2009 FFPE samples alone.

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 16%
Student > Master 3 16%
Student > Doctoral Student 2 11%
Student > Ph. D. Student 2 11%
Professor 1 5%
Other 2 11%
Unknown 6 32%
Readers by discipline Count As %
Medicine and Dentistry 6 32%
Biochemistry, Genetics and Molecular Biology 4 21%
Veterinary Science and Veterinary Medicine 1 5%
Agricultural and Biological Sciences 1 5%
Neuroscience 1 5%
Other 0 0%
Unknown 6 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 October 2017.
All research outputs
#13,027,782
of 23,305,591 outputs
Outputs from Frontiers in Medicine
#1,934
of 5,970 outputs
Outputs of similar age
#147,734
of 319,360 outputs
Outputs of similar age from Frontiers in Medicine
#22
of 58 outputs
Altmetric has tracked 23,305,591 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 5,970 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. This one has gotten more attention than average, scoring higher than 66% of its peers.
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 319,360 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.