Online citations, reference lists, and bibliographies.
Please confirm you are human
(Sign Up for free to never see this)
← Back to Search

Hyperlog—A Flexible Log‐like Transform For Negative, Zero, And Positive Valued Data

C. Bagwell
Published 2005 · Computer Science, Medicine, Mathematics

Save to my Library
Download PDF
Analyze on Scholarcy
Share
The remarkable success of cytometry over the past 30 years is largely due to its uncanny ability to display populations that vastly differ in numbers and fluorescence intensities on one scale. The log transform implemented in hardware as a log amplifier or in software normalizes signals or channels so that these populations appear as clearly discernible peaks. With the advent of multiple fluorescence cytometry, spectral crossover compensation of these signals has been necessary to properly interpret the data. Unfortunately, because compensation is a subtractive process, it can produce negative and zero valued data. The log transform is undefined for these values and, as a result, forces computer algorithms to truncate these values, creating a few problems for cytometrists. Data truncation biases displays making properly compensated data appear undercompensated; thus, enticing many operators to overcompensate their data. Also, events truncated into the first histogram channel are not normally visible with typical two‐dimensional graphic displays, thus hiding a large number of events and obscuring the true proportionality of negative distributions. In addition, the log transform creates unequal binning that can dramatically distort negative population distributions.
This paper references
An alternative display transform for cytometry data. ISAC; Montpellier France
C B Bagwell
An alternative display transform for cytometry data
CB Bagwell (2003)
INFRARED ARRAY PHOTOMETRY OF BULGE GLOBULAR CLUSTERS. I. COMBINED GROUND BASED JK AND HST VI PHOTOMETRY OF NGC 6553
Guarnieri (1998)
STATISTICAL ANALYSIS OF HIGH DENSITY OLIGONUCLEOTIDE ARRAYS: A SAFER APPROACH
D. Holder (2001)
10.1080/01621459.1981.10477649
An Analysis of Transformations Revisited
P. Bickel (1981)
10.1093/bioinformatics/btg107
Approximate Variance-stabilizing Transformations for Gene-expression Microarray Data
David M. Rocke (2003)
A ‘consistency’ test for determining the significance of gene expression changes on replicate samples and two convenient variance-stabilizing transformations
P. Munson (2001)
10.1111/j.1749-6632.1993.tb38775.x
Fluorescence Spectral Overlap Compensation for Any Number of Flow Cytometry Parameters
C. Bagwell (1993)
10.4018/978-1-5225-3270-5.ch006
[Exploratory data analysis].
M. Braga (1988)
10.1214/AOMS/1177706875
On the Comparative Anatomy of Transformations
J. W. Tukey (1957)
10.1177/25.7.330738
Two-color immunofluorescence using a fluorescence-activated cell sorter.
M. Loken (1977)
Numerical recipes in C
W. Press (2002)
10.1080/01621459.1988.10478575
Alternative Transformations to Handle Extreme Values of the Dependent Variable
John Burbidge (1988)
10.1109/TCS.1979.1084580
A new algorithm for computing a single root of a real continuous function
C. Ridders (1979)
10.1093/BIOMET/36.1-2.149
Systems of frequency curves generated by methods of translation.
N. Johnson (1949)
10.1111/0002-9092.00284
Alternative Approaches for Modeling Concave Willingness to Pay Functions in Conjoint Valuation
D. Layton (2001)
IEEE Trans Circuits Syst
Cjf Ridders (1979)
On the compariative anatomy of transformations
JW Tukey (1964)
10.1214/AOMS/1177706645
A Note on the Generation of Random Normal Deviates
G. P. Box (1958)
10.1111/J.2517-6161.1964.TB00553.X
An Analysis of Transformations
G. Box (1964)
Cytometry Development Workshop
DR Parks (2002)



This paper is referenced by
10.1002/cpcy.17
Fluorescent Proteins for Flow Cytometry
T. Hawley (2017)
10.1007/978-1-59745-451-3_3
Fluorochromes and Fluorescence
D. Mccarthy (2007)
10.1101/151738
Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes
Jia-Ren Lin (2018)
10.1201/9780429109157-13
Microflow Cytometer Electronics
Jeffrey S. Erickson (2010)
10.1186/1751-0473-3-10
Flow: Statistics, visualization and informatics for flow cytometry
J. Frelinger (2008)
10.1002/cyto.a.22510
NetFCM: A semi‐automated web‐based method for flow cytometry data analysis
Juliet Frederiksen (2014)
10.1111/cei.12118
Lost therapeutic potential of monocyte-derived dendritic cells through lost tissue homing: stable restoration of gut specificity with retinoic acid
D. Bernardo (2013)
10.1182/BLOOD-2006-10-054247
Checkpoint-apoptosis uncoupling in human and mouse embryonic stem cells: a source of karyotpic instability.
C. Mantel (2007)
10.1002/cyto.a.22622
gEM/GANN: A multivariate computational strategy for auto‐characterizing relationships between cellular and clinical phenotypes and predicting disease progression time using high‐dimensional flow cytometry data
D. Tong (2015)
10.1101/151738
A simple open-source method for highly multiplexed imaging of single cells in tissues and tumours
Jia-Ren Lin (2017)
10.1007/82_2014_364
Cytobank: providing an analytics platform for community cytometry data analysis and collaboration.
T. Chen (2014)
10.1371/journal.pcbi.1003365
Flow Cytometry Bioinformatics
K. O'Neill (2013)
10.3389/fonc.2016.00188
Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples
A. Azad (2016)
10.1002/cyto.a.20592
Flow cytometry histograms: Transformations, resolution, and display
D. Novo (2008)
10.1002/cyto.a.20619
Tracking antigen‐driven responses by flow cytometry: Monitoring proliferation by dye dilution
P. Wallace (2008)
10.14288/1.0135595
Automated analysis of single cell leukemia data
K. O'Neill (2014)
10.1201/9781420003710.CH6
Understanding Clinical Flow Cytometry
A. Donnenberg (2008)
10.1158/1078-0432.CCR-08-2806
Characterization of the Class I-Restricted gp100 Melanoma Peptide-stimulated Primary Immune Response in Tumor-Free Vaccine-draining Lymph Nodes and Peripheral Blood
E. Walker (2009)
10.1002/CYTO.B.21106
Validation of cell‐based fluorescence assays: Practice guidelines from the ICSH and ICCS – part III – analytical issues
B. H. Davis (2013)
10.1002/cyto.b.21106
Validation of cell‐based fluorescence assays: Practice guidelines from the ICSH and ICCS – part III – analytical issues
Shabnam Tangri (2013)
10.1016/J.CHEMOLAB.2015.12.001
FLOOD: FLow cytometric Orthogonal Orientation for Diagnosis
J. Jansen (2016)
Development , application and computational analysis of high-dimensional fl uorescent antibody panels for single-cell fl ow cytometry
Jolanda Brummelman (2019)
10.1002/cyto.a.22018
FCS 3.1 Implementation Guidance
Chris M Bray (2012)
10.1101/553636
A quantitative tri-fluorescent yeast two-hybrid system: from flow cytometry to in-cellula affinities
D. Cluet (2019)
Flow Cytometry: Principles and Applications
M. Macey (2007)
10.1097/CJI.0000000000000103
Identification of Melanoma-reactive CD4+ T-Cell Subsets From Human Melanoma Draining Lymph Nodes
Mei Zhang (2016)
10.1242/jeb.033357
Neural responses to one- and two-tone stimuli in the hearing organ of the dengue vector mosquito
Ben J. Arthur (2010)
10.1002/0471142956.cy1016s42
Alternatives to Log‐Scale Data Display
J. Trotter (2007)
10.1093/nar/gky795
Synthetic control systems for high performance gene expression in mammalian cells
G. Lillacci (2018)
10.1002/cyto.a.20583
Statistical mixture modeling for cell subtype identification in flow cytometry
C. Chan (2008)
10.1186/s12859-016-1083-9
flowVS: channel-specific variance stabilization in flow cytometry
A. Azad (2016)
10.1111/ijlh.12344
The evolution of guidelines for the validation of flow cytometric methods
L. Du (2015)
See more
Semantic Scholar Logo Some data provided by SemanticScholar