Modeling of cytometry data in logarithmic space: When is a bimodal distribution not bimodal?-Reference-Cited by-同舟云学术

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Modeling of cytometry data in logarithmic space: When is a bimodal distribution not bimodal?

Published:2018-02-16 Issue:6 Volume:93 Page:611-619
ISSN:1552-4922
Container-title:Cytometry Part A
language:en
Short-container-title:Cytometry

Author:

Erez Amir¹,Vogel Robert²,Mugler Andrew³,Belmonte Andrew¹⁴,Altan-Bonnet Grégoire¹

Affiliation:

1. Immunodynamics Group, Cancer and Inflammation Program; National Cancer Institute, National Institutes of Health; Bethesda Maryland 20814

2. IBM T. J. Watson Research Center, Yorktown Heights; New York, New York 10598

3. Department of Physics and Astronomy; Purdue University; West Lafayette Indiana 47907

4. Department of Mathematics; Pennsylvania State University; University Park Pennsylvania 16802

Funder

Human Frontier Science Program

Intramural Research Program of the NCI, NIH

Publisher

Wiley

Subject

Cell Biology,Histology,Pathology and Forensic Medicine

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cyto.a.23333/fullpdf

Reference35 articles.

1. Dichotomy of cellular inhibition by small-molecule inhibitors revealed by single-cell analysis;Vogel;Nat Commun,2016

2. A new logicle display method avoids deceptive effects of logarithmic scaling for low signals and compensated data;Parks;Cytometry Part A,2006

3. Sometimes simpler is better: Vlog, a general but easy-to-implement log-like transform for cytometry;Bagwell;Cytometry Part A,2016

4. Optimizing transformations for automated, high throughput analysis of flow cytometry data;Finak;BMC Bioinformatics,2010

5. Interpreting flow cytometry data: A guide for the perplexed;Herzenberg;Nat Immunol,2006

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