Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy.

ABSTRACT: Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented.

SUBMITTER: Lee JS

PROVIDER: S-EPMC2923818 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy.

Lee Jong Soo JS Cox Dennis D DD

Computational statistics & data analysis 20101201 12

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, an ...[more]

PMID: 20729976

Similar Datasets

AutoTuner: High Fidelity and Robust Parameter Selection for Metabolomics Data Processing.

Project description:Untargeted metabolomics experiments provide a snapshot of cellular metabolism but remain challenging to interpret due to the computational complexity involved in data processing and analysis. Prior to any interpretation, raw data must be processed to remove noise and to align mass-spectral peaks across samples. This step requires selection of dataset-specific parameters, as erroneous parameters can result in noise inflation. While several algorithms exist to automate parameter selection, each depends on gradient descent optimization functions. In contrast, our new parameter optimization algorithm, AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations. Here, we tested the accuracy and the run-time of AutoTuner in comparison to isotopologue parameter optimization (IPO), the most commonly used parameter selection tool, and compared the resulting parameters' influence on the properties of feature tables after processing. We performed a Monte Carlo experiment to test the robustness of AutoTuner parameter selection and found that AutoTuner generated similar parameter estimates from random subsets of samples. We conclude that AutoTuner is a desirable alternative to existing tools, because it is scalable, highly robust, and very fast (∼100-1000× speed improvement from other algorithms going from days to minutes). AutoTuner is freely available as an R package through BioConductor.

| S-EPMC7310949 | biostudies-literature

Two-Dimensional Fluorescence Lifetime Correlation Spectroscopy: Concepts and Applications.

Project description:We review the basic concepts and recent applications of two-dimensional fluorescence lifetime correlation spectroscopy (2D FLCS), which is the extension of fluorescence correlation spectroscopy (FCS) to analyze the correlation of fluorescence lifetime in addition to fluorescence intensity. Fluorescence lifetime is sensitive to the microenvironment and can be a "molecular ruler" when combined with FRET. Utilization of fluorescence lifetime in 2D FLCS thus enables us to quantify the inhomogeneity of the system and the interconversion dynamics among different species with a higher time resolution than other single-molecule techniques. Recent applications of 2D FLCS to various biological systems demonstrate that 2D FLCS is a unique and promising tool to quantitatively analyze the microsecond conformational dynamics of macromolecules at the single-molecule level.

| S-EPMC6278346 | biostudies-literature

Smoothing of, and parameter estimation from, noisy biophysical recordings.

Project description:Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We extend previous techniques to take the noisy nature of the measurements into account. Sequential Monte Carlo ("particle filtering") methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximization. Overall, we find that model-based smoothing is a powerful, robust technique for smoothing of noisy biophysical data and for inference of biophysical parameters in the face of recording noise.

| S-EPMC2676511 | biostudies-literature

Enhancing Single-Molecule Fluorescence Spectroscopy with Simple and Robust Hybrid Nanoapertures.

Project description:Plasmonic nanoapertures have found exciting applications in optical sensing, spectroscopy, imaging, and nanomanipulation. The subdiffraction optical field localization, reduced detection volume (~attoliters), and background-free operation make them particularly attractive for single-particle and single-molecule studies. However, in contrast to the high field enhancements by traditional "nanoantenna"-based structures, small field enhancement in conventional nanoapertures results in weak light-matter interactions and thus small enhancement of spectroscopic signals (such as fluorescence and Raman signals) of the analytes interacting with the nanoapertures. In this work, we propose a hybrid nanoaperture design termed "gold-nanoislands-embedded nanoaperture" (AuNIs-e-NA), which provides multiple electromagnetic "hotspots" within the nanoaperture to achieve field enhancements of up to 4000. The AuNIs-e-NA was able to improve the fluorescence signals by more than 2 orders of magnitude with respect to a conventional nanoaperture. With simple design and easy fabrication, along with strong signal enhancements and operability over variable light wavelengths and polarizations, the AuNIs-e-NA will serve as a robust platform for surface-enhanced optical sensing, imaging, and spectroscopy.

| S-EPMC9017716 | biostudies-literature

Automatic search intervals for the smoothing parameter in penalized splines.

Project description:The selection of smoothing parameter is central to the estimation of penalized splines. The best value of the smoothing parameter is often the one that optimizes a smoothness selection criterion, such as generalized cross-validation error (GCV) and restricted likelihood (REML). To correctly identify the global optimum rather than being trapped in an undesired local optimum, grid search is recommended for optimization. Unfortunately, the grid search method requires a pre-specified search interval that contains the unknown global optimum, yet no guideline is available for providing this interval. As a result, practitioners have to find it by trial and error. To overcome such difficulty, we develop novel algorithms to automatically find this interval. Our automatic search interval has four advantages. (i) It specifies a smoothing parameter range where the associated penalized least squares problem is numerically solvable. (ii) It is criterion-independent so that different criteria, such as GCV and REML, can be explored on the same parameter range. (iii) It is sufficiently wide to contain the global optimum of any criterion, so that for example, the global minimum of GCV and the global maximum of REML can both be identified. (iv) It is computationally cheap compared with the grid search itself, carrying no extra computational burden in practice. Our method is ready to use through our recently developed R package gps ( ≥ version 1.1). It may be embedded in more advanced statistical modeling methods that rely on penalized splines.Supplementary informationThe online version contains supplementary material available at 10.1007/s11222-022-10178-z.

| S-EPMC9672641 | biostudies-literature

A selective review of robust variable selection with applications in bioinformatics.

Project description:A drastic amount of data have been and are being generated in bioinformatics studies. In the analysis of such data, the standard modeling approaches can be challenged by the heavy-tailed errors and outliers in response variables, the contamination in predictors (which may be caused by, for instance, technical problems in microarray gene expression studies), model mis-specification and others. Robust methods are needed to tackle these challenges. When there are a large number of predictors, variable selection can be as important as estimation. As a generic variable selection and regularization tool, penalization has been extensively adopted. In this article, we provide a selective review of robust penalized variable selection approaches especially designed for high-dimensional data from bioinformatics and biomedical studies. We discuss the robust loss functions, penalty functions and computational algorithms. The theoretical properties and implementation are also briefly examined. Application examples of the robust penalization approaches in representative bioinformatics and biomedical studies are also illustrated.

| S-EPMC4570200 | biostudies-literature

Smoothing splines approximation using Hilbert curve basis selection.

Project description:Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size n is large. When the number of predictors d ≥ 2 , the computational cost for smoothing splines is at the order of O(n 3) using the standard approach. Many methods have been developed to approximate smoothing spline estimators by using q basis functions instead of n ones, resulting in a computational cost of the order O(nq 2). These methods are called the basis selection methods. Despite algorithmic benefits, most of the basis selection methods require the assumption that the sample is uniformly-distributed on a hyper-cube. These methods may have deteriorating performance when such an assumption is not met. To overcome the obstacle, we develop an efficient algorithm that is adaptive to the unknown probability density function of the predictors. Theoretically, we show the proposed estimator has the same convergence rate as the full-basis estimator when q is roughly at the order of O[n 2d/{(pr+1)(d +2)}] , where p ∈[1, 2] and r ≈ 4 are some constants depend on the type of the spline. Numerical studies on various synthetic datasets demonstrate the superior performance of the proposed estimator in comparison with mainstream competitors.

| S-EPMC9674117 | biostudies-literature

An asymptotic and empirical smoothing parameters selection method for smoothing spline ANOVA models in large samples.

Project description:Large samples are generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyse such large samples because of high computational costs. In particular, the daunting computational cost of selecting smoothing parameters renders smoothing spline ANOVA models impractical. In this article, we develop an asympirical, i.e., asymptotic and empirical, smoothing parameters selection method for smoothing spline ANOVA models in large samples. The idea of our approach is to use asymptotic analysis to show that the optimal smoothing parameter is a polynomial function of the sample size and an unknown constant. The unknown constant is then estimated through empirical subsample extrapolation. The proposed method significantly reduces the computational burden of selecting smoothing parameters in high-dimensional and large samples. We show that smoothing parameters chosen by the proposed method tend to the optimal smoothing parameters that minimize a specific risk function. In addition, the estimator based on the proposed smoothing parameters achieves the optimal convergence rate. Extensive simulation studies demonstrate the numerical advantage of the proposed method over competing methods in terms of relative efficacy and running time. In an application to molecular dynamics data containing nearly one million observations, the proposed method has the best prediction performance.

| S-EPMC8294473 | biostudies-literature

Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA.

Project description:Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online.

| S-EPMC3926212 | biostudies-literature

More efficient approximation of smoothing splines via space-filling basis selection.

Project description:We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size [Formula: see text], the smoothing spline estimator can be expressed as a linear combination of [Formula: see text] basis functions, requiring [Formula: see text] computational time when the number [Formula: see text] of predictors is two or more. Such a sizeable computational cost hinders the broad applicability of smoothing splines. In practice, the full-sample smoothing spline estimator can be approximated by an estimator based on [Formula: see text] randomly selected basis functions, resulting in a computational cost of [Formula: see text]. It is known that these two estimators converge at the same rate when [Formula: see text] is of order [Formula: see text], where [Formula: see text] depends on the true function and [Formula: see text] depends on the type of spline. Such a [Formula: see text] is called the essential number of basis functions. In this article, we develop a more efficient basis selection method. By selecting basis functions corresponding to approximately equally spaced observations, the proposed method chooses a set of basis functions with great diversity. The asymptotic analysis shows that the proposed smoothing spline estimator can decrease [Formula: see text] to around [Formula: see text] when [Formula: see text]. Applications to synthetic and real-world datasets show that the proposed method leads to a smaller prediction error than other basis selection methods.

| S-EPMC7430945 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data