AutoDecon, a deconvolution algorithm for identification and characterization of luteinizing hormone secretory bursts: Description and validation using synthetic data

Abstract

Hormone signaling is often pulsatile, and multi-parameter deconvolution procedures have long been utilized to identify and characterize secretory events. However, the existing programs have serious limitations, including the subjective nature of initial peak selection, lack of statistical verification of presumed bursts, and user-unfriendliness of the application. Here, we describe a novel deconvolution program, AutoDecon, which addresses these concerns. We validate AutoDecon for application to serum luteinizing hormone (LH) concentration time series using synthetic data mimicking real data from normal women and then comparing the performance of AutoDecon to the performance of the widely-employed hormone pulsatility analysis program Cluster. The sensitivity of AutoDecon is higher than Cluster: ~96% vs. ~80% (p = 0.001). However, Cluster had a lower false-positive detection rate than AutoDecon: 6% vs 1%, p = 0.001. Further analysis demonstrated that the pulsatility parameters recovered by AutoDecon were indistinguishable from those characterizing the synthetic data and sampling at 5-or 10-minute intervals was optimal for maximizing the sensitivity rates for LH. Accordingly, AutoDecon presents a viable non-subjective alternative to previous pulse detection algorithms for the analysis of LH data. It is applicable to other pulsatile hormone-concentration time series and many other pulsatile phenomena. The software is free and downloadable at

Acupuncture for ovulation induction in polycystic ovary syndrome: a randomized controlled trial

“…The concentrations of LH and cortisol in blood samples taken every 10 min for 12 h were analyzed with the Autodecon pulse detection algorithm (17) in the Pulse XP software package to detect and quantify secretory events. Three truncated LH data series, due to missing blood samples, were recalculated to 12 h for the time-dependent variables before statistical analysis.…”

Validation of a Deconvolution Procedure (AutoDecon) for Identification and Characterization of Fasting Insulin Secretory Bursts

“…26,27 Previously published hormone pulsatility analysis approaches 19,20,29 necessitate prior knowledge or presumption of the number of secretion events, and initial estimates of the secretion event positions and amplitudes are required to be provided by the user. Many require a fixed predetermined estimate of the elimination halflife(s).…”

“…26,27 The series were constructed in a manner such that the locations and sizes of the synthetic secretion events as well as the half-lives and the secretion standard deviations (SD) were known a priori. 26,27 When these synthetic time series were analyzed with AutoDecon 26,27 and Cluster, 28 comparisons between the correct answers and those produced by the algorithms could be made, thus yielding true-positive and false-positive rates for each algorithm and for the specific hormone concentration-time series being considered.…”

“…26,27 In this study, we analyzed and validated the performance of AutoDecon using synthetic fasting serum insulin concentration-time series, which were created by mimicking serum insulin concentration-time series obtained from normal fasting subjects. The synthetic time series were designed to reflect differing sampling intensities and sampling intervals, to simulate differing numbers of replicates within the assay system and measurement uncertainties of common assay systems.…”

“…The synthetic time series were designed to reflect differing sampling intensities and sampling intervals, to simulate differing numbers of replicates within the assay system and measurement uncertainties of common assay systems. 26,27 The operating characteristics of AutoDecon were then compared to those of Cluster, 28 a standard pulse detection algorithm widely applied to analyze insulin pulsatility.…”