What is a “unimodal” cell population? Using statistical tests as criteria for unimodality in automated gating and quality control
(2017) In Cytometry Part A 91(9). p.908-916- Abstract
Many automated gating algorithms for flow cytometry data are based on the concept of unimodal cell populations. However, in this article, we show that criteria previously used to make decisions on unimodality cannot adequately distinguish unimodal from bimodal densities. We show that dip and bandwidth tests for unimodality, taken from the statistics literature, can do this with consistent and low error rates. These tests also have the possibility to adjust the significance level to handle the trade-off between failing to detect a second mode and seeing a second mode when there is none. The differences between the dip and bandwidth tests are elucidated using real data from the FlowCAP I challenge, also guidelines for flow cytometry data... (More)
Many automated gating algorithms for flow cytometry data are based on the concept of unimodal cell populations. However, in this article, we show that criteria previously used to make decisions on unimodality cannot adequately distinguish unimodal from bimodal densities. We show that dip and bandwidth tests for unimodality, taken from the statistics literature, can do this with consistent and low error rates. These tests also have the possibility to adjust the significance level to handle the trade-off between failing to detect a second mode and seeing a second mode when there is none. The differences between the dip and bandwidth tests are elucidated using real data from the FlowCAP I challenge, also guidelines for flow cytometry data preprocessing are given.
(Less)
- author
- Johnsson, Kerstin LU ; Linderoth, Magnus LU and Fontes, Magnus LU
- organization
- publishing date
- 2017-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- automated gating, bandwidth test, calibration, data analysis, dip test, flow cytometry, quality control, unimodality
- in
- Cytometry Part A
- volume
- 91
- issue
- 9
- pages
- 9 pages
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85026477395
- pmid:28759711
- ISSN
- 1552-4922
- DOI
- 10.1002/cyto.a.23173
- language
- English
- LU publication?
- yes
- id
- 9f0c3646-9c96-4e7f-97cc-e4090526f34e
- date added to LUP
- 2018-01-18 09:03:08
- date last changed
- 2024-04-15 00:43:41
@article{9f0c3646-9c96-4e7f-97cc-e4090526f34e,
abstract = {{<p>Many automated gating algorithms for flow cytometry data are based on the concept of unimodal cell populations. However, in this article, we show that criteria previously used to make decisions on unimodality cannot adequately distinguish unimodal from bimodal densities. We show that dip and bandwidth tests for unimodality, taken from the statistics literature, can do this with consistent and low error rates. These tests also have the possibility to adjust the significance level to handle the trade-off between failing to detect a second mode and seeing a second mode when there is none. The differences between the dip and bandwidth tests are elucidated using real data from the FlowCAP I challenge, also guidelines for flow cytometry data preprocessing are given.</p>}},
author = {{Johnsson, Kerstin and Linderoth, Magnus and Fontes, Magnus}},
issn = {{1552-4922}},
keywords = {{automated gating; bandwidth test; calibration; data analysis; dip test; flow cytometry; quality control; unimodality}},
language = {{eng}},
month = {{09}},
number = {{9}},
pages = {{908--916}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Cytometry Part A}},
title = {{What is a “unimodal” cell population? Using statistical tests as criteria for unimodality in automated gating and quality control}},
url = {{http://dx.doi.org/10.1002/cyto.a.23173}},
doi = {{10.1002/cyto.a.23173}},
volume = {{91}},
year = {{2017}},
}