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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium
|
|---|---|
| Published in |
Scientific Reports, February 2016
|
| DOI | 10.1038/srep20686 |
| Pubmed ID | |
| Authors |
Greg Finak, Marc Langweiler, Maria Jaimes, Mehrnoush Malek, Jafar Taghiyar, Yael Korin, Khadir Raddassi, Lesley Devine, Gerlinde Obermoser, Marcin L. Pekalski, Nikolas Pontikos, Alain Diaz, Susanne Heck, Federica Villanova, Nadia Terrazzini, Florian Kern, Yu Qian, Rick Stanton, Kui Wang, Aaron Brandes, John Ramey, Nima Aghaeepour, Tim Mosmann, Richard H. Scheuermann, Elaine Reed, Karolina Palucka, Virginia Pascual, Bonnie B. Blomberg, Frank Nestle, Robert B. Nussenblatt, Ryan Remy Brinkman, Raphael Gottardo, Holden Maecker, J Philip McCoy |
| Abstract |
Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping. |
X Demographics
The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 44% |
| Switzerland | 1 | 6% |
| Austria | 1 | 6% |
| United Kingdom | 1 | 6% |
| Italy | 1 | 6% |
| Unknown | 5 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 81% |
| Scientists | 3 | 19% |
Mendeley readers
The data shown below were compiled from readership statistics for 515 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | <1% |
| Austria | 1 | <1% |
| South Africa | 1 | <1% |
| Portugal | 1 | <1% |
| Canada | 1 | <1% |
| United Kingdom | 1 | <1% |
| Spain | 1 | <1% |
| Argentina | 1 | <1% |
| Unknown | 503 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 131 | 25% |
| Student > Ph. D. Student | 80 | 16% |
| Student > Master | 55 | 11% |
| Other | 43 | 8% |
| Student > Bachelor | 40 | 8% |
| Other | 71 | 14% |
| Unknown | 95 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 100 | 19% |
| Agricultural and Biological Sciences | 87 | 17% |
| Immunology and Microbiology | 83 | 16% |
| Biochemistry, Genetics and Molecular Biology | 65 | 13% |
| Computer Science | 13 | 3% |
| Other | 62 | 12% |
| Unknown | 105 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 26. 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 20 July 2021.
All research outputs
#1,286,814
of 23,313,051 outputs
Outputs from Scientific Reports
#12,585
of 126,013 outputs
Outputs of similar age
#25,150
of 402,910 outputs
Outputs of similar age from Scientific Reports
#382
of 3,320 outputs
Altmetric has tracked 23,313,051 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 126,013 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.3. This one has done particularly well, scoring higher than 90% 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 402,910 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 3,320 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.