On the Proper Normalization Constraint

1985 ◽  
Vol 32 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Tse-Chiang Chang
2019 ◽  
Vol 317 (5) ◽  
pp. F1098-F1110 ◽  
Author(s):  
Erik H. Koritzinsky ◽  
Jonathan M. Street ◽  
Rohit R. Chari ◽  
Deonna M. Glispie ◽  
Tiffany R. Bellomo ◽  
...  

Numerous candidate biomarkers in urine extracellular vesicles (EVs) have been described for kidney diseases, but none are yet in clinical use, possibly due to a lack of proper normalization. Proper normalization corrects for normal biological variation in urine flow rate or concentration, which can vary by over one order of magnitude. Here, we observed inter- and intra-animal variation in urine excretion rates of small EVs (<200 nm in diameter) in healthy rats as a series of six 4-h fractions. To visualize intra-animal variation, we normalized a small EV excretion rate to a peak excretion rate, revealing a circadian pattern for each rat. This circadian pattern was distinct from urine volume, urine albumin, urine creatinine, and urine albumin-to-creatinine ratio. Furthermore, urine small EV excretion was not significantly altered by sex, food/water deprivation, or ischemic acute kidney injury. Urine excretion of the exosomal/small EV marker protein tumor susceptibility gene 101 (TSG101) displayed a similar circadian pattern to urine small EV excretion; both measurements were highly correlated ( R2 = 0.85), with an average stoichiometry of 10.0 molecules of TSG101/vesicle in healthy rats. The observed stoichiometry of TSG101/vesicle in rat urine translated to human spot urine samples (10.2 molecules/vesicle) and cultured kidney-derived cell lines (human embryonic kidney-293 and normal rat kidney 52E cells). Small EV number and its surrogate, TSG101 protein, can normalize for circadian variation when testing candidate biomarkers in small EVs. Just as creatinine has emerged as the customary normalization factor for liquid-phase urine biomarkers, vesicle number and its surrogate, molecules of exosome/small EV-associated TSG101, should be considered as viable, normalizing factors for small EV biomarkers.


2017 ◽  
Vol 217 ◽  
pp. 53-70 ◽  
Author(s):  
Samuel Rosat ◽  
Issmail Elhallaoui ◽  
François Soumis ◽  
Driss Chakour

2019 ◽  
Vol 62 (8) ◽  
pp. 1519-1552 ◽  
Author(s):  
Yan-Xia Ren ◽  
Renming Song ◽  
Rui Zhang

2020 ◽  
Vol 26 (3) ◽  
pp. 165-174 ◽  
Author(s):  
Biswapriya B Misra

Data normalization is a big challenge in quantitative metabolomics approaches, whether targeted or untargeted. Without proper normalization, the mass-spectrometry and spectroscopy data can provide erroneous, sub-optimal data, which can lead to misleading and confusing biological results and thereby result in failed application to human healthcare, clinical, and other research avenues. To address this issue, a number of statistical approaches and software tools have been proposed in the literature and implemented over the years, thereby providing a multitude of approaches to choose from – either sample-based or data-based normalization strategies. In recent years, new dedicated software tools for metabolomics data normalization have surfaced as well. In this account article, I summarize the existing approaches and the new discoveries and research findings in this area of metabolomics data normalization, and I introduce some recent tools that aid in data normalization.


1999 ◽  
Vol 14 (32) ◽  
pp. 2229-2243 ◽  
Author(s):  
L. CHEKHOV ◽  
K. PALAMARCHUK

We investigate the two-logarithm matrix model with the potential XΛ+α log (1+X)+β log (1-X) related to an exactly solvable Kazakov–Migdal model. In the proper normalization, using Virasoro constraints, we prove the equivalence of this model and the Kontsevich–Penner matrix model and construct the 1/N-expansion solution of this model.


2005 ◽  
Vol 85 (8) ◽  
pp. 1040-1050 ◽  
Author(s):  
Janine Antonov ◽  
Darlene R Goldstein ◽  
Andrea Oberli ◽  
Anna Baltzer ◽  
Marco Pirotta ◽  
...  

2019 ◽  
Vol 48 (4) ◽  
pp. 877-904
Author(s):  
Cees H. Elzinga ◽  
Matthias Studer

We explore the relations between the notion of distance and a feature set–based concept of similarity and show that this concept of similarity has a spatial interpretation that is complementary to distance: it is interpreted as “direction.” Furthermore, we show how proper normalization leads to distances that can be directly interpreted as dissimilarity: Closeness in normalized space implies and is implied by similarity of the same objects, while remoteness implies and is implied by dissimilarity. Finally, we show how, in research into destandardization of the life course, properly normalizing may drastically and unequivocally change our interpretation of intercohortal distances.


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