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Prueba gratis We have free trial that gives you an opportunity to evaluate the software before you purchase it. Asequible StatPlus: Requisitos StatPlus para mac requiere macOS Capturas de Pantalla. Basic Statistics Detailed descriptive statistics. Comparing means: Fisher F-test. One sample and two samples z-tests. Correlation coefficients Pearson, Fechner and covariation.

Using StatPlus

Normality tests incl. Frequency tables analysis for discrete and continuous variables. Data Classification Discriminant function analysis. Rank and percentile. Chi-square test. GGEbiplot, Version 5. Canoco, Version 4.

Related Interests

XLS-Biplot, Version 1. Universitat Pompeu Fabra, Barcelona, Spain. ViSta, Version 6. A package for multivariate analysis using biplots. Universidad de Salamanca. Journal of Statistical Software , 30 12 , An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stochastic Environmental Research and Risk Assessment , 28 7 , A methodology for biplots based on bootstrapping with R. Statistics and computing , 7 1 , Journal of Vegetation Science , 14 6 , Vistas Leer Editar Ver historial.

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These lists are usually generated manually without any visual confirmation or using the software AMDIS [ 8 ]. In addition to the above, two approaches have been adopted: A particular example of stand-alone applications is vendor software; these have several limitations [ 9 ], and do not clearly establish which methods are used for data handling, contrary to what should be the case [ 10 ].

Web-based applications perform computation in central servers using a web browser as interface, meaning that i they are easily accessible on different platforms, ii no or minimum installation is needed, iii fixes or updates are automatically incorporated, iv results can be shared among scientists in different locations and v more powerful servers can be used for hosting, thus increasing calculation capabilities [ 11 ]. On the other hand, web applications can be slower due to multiple and simultaneous access to the same resources, and because file uploading depends on the internet connection bandwidth.

How to Run Regression Analysis in Microsoft Excel (with Sample Analyses)

Moreover, analysts should be able to interact with the processed results, for example, to check correct peak assignment. However, classical web applications have limitations related to user interaction, since once information or action is provided, the web page needs to be reloaded due to the use of synchronous communication with the sever. In this web application model, user interaction is based on sending information to the server and then waiting until the server has processed the information and returns a new web page. In consequence, very few web applications are available for targeted metabolomics, and even fewer can interact directly with raw MS data, since classical web applications could not handle MS data interaction in real time.

On the other hand, web applications based on asynchronous communication with the server could allow a single-page interface which neither needs to wait for the server nor to reload the complete web page; therefore, these would behave similarly to stand-alone applications. This is possible because user interaction is performed through a software engine which can be independent of the communication with the server detailed information can be found in [ 12 ].

Furthermore, user interaction can be enriched with such web applications, allowing real-time interaction, and consequently improving MS data visua-lisation. Among the different possibilities for enriched visualisation, Silverlight [ 13 ] has been selected, since it allows the querying and visualisation of large datasets for a recent example see the update of the Frequency of INherited Disorders database FINDbase [ 14 ].

Recently, downstream web applications able to analyse metabolomics data, including MetaboAnalyst [ 15 ], MSEA [ 16 ], ProMetra [ 17 ] or metaP-server [ 18 ], have appeared. Moreover, mathematical modelling requires quantified data for validation and parameter calculation [ 19 ]. Moreover, pre-processing of LC-MS data is nowadays in the development process, since several steps in the data processing need to be improved, such as peak detection or alignment algorithms; therefore, misalignment, as well as errors in the peak detection, have been found in all the software recently analysed [ 6 , 20 ].

EasyLCMS is a web application designed to fulfil the connection between raw LC-MS data and targeted metabolomics quantification, automatically retrieving metabolite IDs from popular databases and recent non-linear algorithms [ 4 ] to carry out peak alignment. Furthermore, the quantification process is completely integrated, requiring minimal knowledge of data handling or programming.

Moreover, EasyLCMS implements an easy web-based interface with asynchronous communication to allow chromatogram visualisation and analyst interaction in real time. EasyLCMS consists of three interacting layers: However, manual intervention is allowed at all steps to check and correct automatic results when needed.

The quantification workflow schema is represented. The molecular weight of the compound is automatically obtained and converted into a quantification ion, taking into account the ionisation method, although this can also be manually established. Moreover, EasyLCMS checks for interconnectivity among the databases and is able to import additional IDs from different databases to avoid multiple searches.

If an internal standard is used, it can be easily and directly defined in the table, in which case, standard and sample areas are normalised as previously established [ 9 ] see Supplementary Material. The quantification ion list can be saved for future experiments. Raw data files can also be uploaded to the server using the following currently supported formats: Zip files are also supported.

For other formats, several converters are available [ 21 ]. Data processing Figure 4 is carried out in three steps: For raw data filtering, the Savitzky—Golay smoothing filter can be employed. At this point, previously established quantification ions are filtered to increase the speed of processing by avoiding the subsequent steps in every single chromatogram.

Finally, the remaining chromatograms are divided into individual peaks deconvolution by an algorithm which identifies local minima in the chromatogram as border points between peaks. All the data processing algorithms have been developed by the MZmine 2 application crew [ 4 ]. To simplify data processing, three configurations using pre-established parameters have been chosen based on the optimisation of peak detection and minimisation of the time needed. Each step of data processing can be defined for advanced users. The peak area is the parameter usually taken in LC to establish a mathematical relationship between the metabolite concentration and the signal of the instrument.

The mathematical relationship between the peak area A and the concentration C or calibration curve can be linear or non-linear. Standards samples of known concentrations are required to calculate the regression parameters and to construct the calibration curve, which will be used to determine the unknown concentration of the metabolites in samples. Although the theoretical minimum number of standard samples is generally two, with exception of quadratic polynomial and cubic polynomial which are three and four, respectively, six non-zero standard samples covering the expected range of concentration are highly recommended [ 22 ].

Replicates of standard samples are not required. To start the calibration process, standard concentrations and raw data files should be added. The first time that a calibration process is carried out, the retention time of every metabolite must be provided by selecting a peak on the interactive chromatogram visualisation, which can be done in any standard sample. For the remaining standards and samples, the application suggests peaks candidates based on recent alignment algorithms [ 4 ], although the automatic selection can be manually changed afterwards.

This step needs only be carried out once for the same analytical method; after this, the platform can be left unattended. To the best of our knowledge, featuring online chromatogram visualisation in web applications has been performed previously [ 2 , 23 ], but none of these application are able to represent the peak area used for quantification or allow manual integration. The calibration results can be pre-visualised at this point.

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However, candidates can be also manually selected using visualisation of the chromatogram; moreover, quantification is performed in real time, and thus it is performed simultaneously as a peak is selected. In this or previous steps, peak selections can be saved for subsequent modification. Once the peaks are selected, the area under the peak are utilised to calculate sample concentrations using the linear or non-lineal regressions of the standard curves generated in the previous step with standard samples of known concentrations.

Prior to the quantification step, EasyLCMS automatically suggests which regression has the best fit based on regression coefficients. EasyLCMS allows visualisation of the results by bar diagrams discrete data or representation versus a given condition continuous data. The quantitative metabolomics export format is usually in the form of comma-separated values CSV files with several columns, where the first column is used to provide the compound IDs.

However, these applications have strong deficiencies [ 9 ]; for example, total ion chromatogram TIC is generally used for automated purposes, while using specific mass ions requires manual intervention. Therefore, they are time-consuming and clearly not suitable for automated simultaneous quantification.

Although several software tools and web applications have been developed recently for targeted metabolomics data management, only a few are sufficiently specialised for HPLC-MS, for example, stand-alone applications such as MAVEN [ 24 ], MZmine 2 [ 4 ], XCMS [ 25 ] with a recent online version or the web service metaP-Server [ 18 ], among others. Surprisingly, almost all of these finish the quantification procedure at the point at which relative areas or heights from the compound peaks are provided MetaQuant is the only exception. Therefore, manual intervention will be necessary to complete regression analysis for calibration using statistics software such as MS-Excel or SigmaPlot [ 7 ], especially if nonlinear regression is needed.

However in MetaQuant, i metabolic IDs from common databases cannot be automatically retrieved, ii only netCDF and CSV formats are allowed, iii quantifier ions are manually added and iv no adjustment of retention time is performed for alignment, although the precision of peak alignment is reduced for HPLC-MS, as previously reported [ 4 — 6 ]. Firstly, the intracellular amino acid content of the human cell line CCL in a batch reactor was measured using an internal standard.

Secondly, the time course evolution of two different human cell lines—CCL S and CCLR R —cultivated in batch reactors were analysed to quantify 29 metabolites intracellular as well as extracellular. In this case, no internal standard was used to check the reliability of EasyLCMS even in these conditions.

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Additionally, samples were acquired and analysed with Chemstation software by a human analyst for comparison with the EasyLCMS results. EasyLCMS performance using calibration standards. Linear regressions red lines and regression coefficients R 2 are represented for the 29 metabolites analysed. As described in the Materials and Methods section of the Supplemental Material, six different cultivations numbers 1 to 6 were performed and three different extraction protocols ACN, MeOH and Chloro were followed. From these samples, 21 internal amino acids were analysed.

Two-dimensional clustering representation of relative errors comparing EasyLCMS and Chemstation software with an internal standard. Twenty-one intracellular amino acids were quantified in samples from six different batch reactors of the human CCL cell line numbers 1—6 , which were obtained with three different extraction procedures ACN, MeOH and Chloro. See Supplemental Material for details. For this comparison, two different human cell lines R, S were cultured and samples were harvested at six different times 0, 23, 46, 71, 95 and h.

From these experiments, 29 internal metabolites were analysed, including amino acids and organic acids.

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  5. Additionally, the extracellular content of these metabolites was also quantified, obviously without any extraction procedure. For further details, see the Materials and Methods section in the Supplemental Material. Two-dimensional clustering representation of relative errors comparing EasyLCMS and Chemstation software without an internal standard.