Category Metabolomics/Metabonomics>Metabolic Profiling/Analysis Systems/Tools

Abstract Automics is a highly integrated platform that provides functions covering almost all of the stages of NMR-based metabonomics studies. And it is specifically designed for NMR-based metabonomics studies.

Automics provides high throughput automatic modules with some of the most recently proposed algorithms and advanced manual modules for 1D NMR spectral processing.

In addition to spectral processing functions, advanced features for data organization, data pre-processing, and data analysis have been implemented.

Nine (9) statistical methods can be applied to analyses including: feature selection (Fisher’s criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM).

Automics also provides a user-friendly graphical interface for visualizing NMR spectra and data analysis results. It facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications.

Using Automics, users can complete spectral processing and data analysis within one software package, in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software product based on its existing infrastructure.

Automics Spectra format conversion --

Automics takes Bruker (Bruker Biospin, Germany) formatted raw flame ionization detectors (FIDs) and XWIN-NMR processed spectra by default. Automics can also convert raw FIDs from a variety of existing NMR FID formats, such as those from Varian (Varian Inc., USA) and JEOL (JEOL Ltd, Japan); to the Bruker FID data format and rewrite them in Bruker style directories.

Automics Manual spectral processing --

Automics provides an easy to use manual spectral processing module. For spectral visualization, features such as spectral editing, peak labeling, peak information browsing, moving and zooming, are supported.

To change the display properties of the visualized spectrum, a dialog can be used to set properties such as line color, line width, line style and background color.

Floating tool bars can be used to continuously adjust zero-order and first-order phases for the interactive phase correction, or adjust coefficients of a selected fitting function (polynomial function, sine function, and exponential function) for the interactive baseline correction.

Other commonly used features, such as referencing, peak picking and spectral derivative, are also supported.

Automics High throughput Automatic spectral processing --

1D NMR spectra can be automatically processed in batch mode by the following five (5) steps:

1) Fast Fourier Transform;

2) Phase correction;

3) Baseline correction;

4) Peak alignment; and

5) Bucket/binning.

These features are implemented via five dialogs that can be launched either individually or through a step-by-step custom wizard from a popup menu. The names of selected spectra are shown in a control list allowing multiple selections.

1) Fast Fourier Transform (FFT) -

FFT converts NMR signals from a time domain to a frequency domain. This module can perform both complex FFT and real FFT.

In addition, direct current (DC) offset, zero filling, a window function with a specific line broadening factor and removal of a potential digital filter imposed on FID (such as that from Bruker Avance spectrometer) can also carried out in this module.

2) Automatic phase correction -

Besides the two global methods (maximization of the spectrum integral and minimization of the spectrum entropy) implemented in Automics, the manufacturer’s have introduced another easier to implement method for automatic phase correction.

This method does Not require detection of isolated individual peaks and is efficient for processing large quantities of similar spectra in metabonomics studies.

3) Automatic baseline correction -

The current version of Automics provides two (2) methods for automatic baseline correction: linear fitting and non-parametric recognition.

The linear fitting method uses pre-defined positions of the spectrum to calculate coefficients of a linear function, which are then used to construct a baseline. A non-parametric method has also been implemented with a variant of Sergey’s algorithm.

4) Peak alignment -

Spectral referencing, which sets the inner reference peak (DSS/TSP) of each spectrum to zero (0) ppm, can be regarded as a simple global method for peak alignment. This method shifts the entire spectrum based on the same reference peak position. Thus, all the spectra with global peak misalignments are well aligned.

However, it is Not sufficient for correcting individual peak misalignments in spectra, such as those from urine samples with variant solution conditions. To solve this problem, Automics implements a fuzzy wrapping method.

This method detects the maximal position of peaks in each spectrum and aligns them to a reference spectrum using their similarity, which is determined by using a fuzzy Gaussian function.

5) Bucket/binning and normalization -

Automics provides both full resolution (data point) bucket/binning and traditional bucket/binning with equal bin width options.

For the traditional bucket/binning, the manufacturer’s implemented a method to determine an appropriate bin width for balancing resolution and dimensionality.

In addition to the above methods, an intelligent adaptive binning method has also implemented in Automics.

This method recursively identifies bin edges in existing bins and requires minimal user input, and it can largely circumvent problems such as the loss of information due to low resolution, the occurrence of artifacts caused by frequency shifts and the presence of noise variables.

Generally, normalization of each row vector produced from bucket/binning is required before further data analysis.

Four (4) normalization methods are available in Automics: normalizing against the total spectral area, normalizing against the maximum peak area, normalizing against the inner reference peak area, and normalizing against a specific peak area.

After bucket/binning and normalization, the produced data matrix can be saved into a comma-delimited text file or can be exported to a worksheet for further analysis in Automics.

Automics Data organization and data pre-processing --

A worksheet module was developed in Automics for data organization. Data pre-processing and data analysis procedures are all based on data in the active worksheet.

Automics can import/export data files in text format or Microsoft EXCEL format. Commonly used editing functions and some basic statistical analysis (column based statistics, row based statistics, and matrix standardization) are supported.

To remove undesirable systematic variations in the spectroscopic data before data analysis, four (4) commonly used data filter methods have been integrated into Automics: multiplicative signal correction (MSC), standard normal variate transform (SNV), direct orthogonal signal correction (DOSC) and orthogonal projections to latent structures (O-PLS).

Automics Data analysis module --

After data pre-processing, data analysis modules can be invoked to analyze the data and build data models. Automics provides nine (9) different pattern recognition methods for data analysis (as stated above...).

These methods include feature (variable) selection method - Fisher's criterion (FC); data reduction method - principal component analysis (PCA); linear discriminant analysis (LDA); uncorrelated linear discriminant analysis (ULDA); unsupervised clustering method - K-Mean Clustering (K-Mean); and supervised regression and classification methods - partial least squared analysis (PLS), K nearest neighbor classification (KNN), soft independent modeling of class analogy (SIMCA), and support vector machine (SVM).

Automics Additional features --

Additional features include an advanced expression calculator, a network resource browser and a statistical total correlation spectroscopy method.

System Requirements

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Manufacturer Web Site Automics

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G6G Abstract Number 20674

G6G Manufacturer Number 104314