# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Data Processing
The chemometric method for extracting such information involves identifying spectral patterns. These patterns can be compared with each other (for example, when analyzing the metabolome of healthy humans and diseased ones), and only then metabolites are identified. However, there are examples of successful barcoding, when characteristic peak patterns can be used to create a digital image of a person without directly identifying compounds [188]. An alternative approach is used in targeted experiments, in which metabolites are first identified, and the result of the identification is then interpreted. In general, the identification pipeline is carried out as follows: preliminary data processing is performed (subtraction of the baseline, marking of signals with an acceptable signal-to-noise ratio, mass spectral  
Figure 6: Typical steps in the processing of GC-MS metabolomics data: noise filtering, baseline correction, peak detection, and normalization. The filtered data obtained after preliminary processing is compared with the spectral libraries. The resulting array of annotated spectra can be visualized and used in further statistical algorithms to build biological models. deconvolution, alignment, normalization, and calculation of retention indices [189; 190] after which the obtained data are compared with the library data (mass spectra, RI). Once metabolites are identified, downstream analysis can be performed to frame small molecules in terms of current omics knowledge. Multivariate analysis methods are used to extract meaningful information from large sets of experimental data. These methods can be divided into controlled (the data marked by the \"supervisor\") and uncontrolled (no \"supervisor\" required). Principal component analysis, PCA, is the gold standard for interpreting high-dimensional complex datasets. In a multivariate dataset [191], PCA allows identifying class differences in an uncontrolled manner, without information about the class of samples under study. A class can refer to any relevant characteristic, such as diseased patients and healthy subjects [192]. Controlled methods include orthogonal projection to latent structures-discriminant analysis (OPLS-DA), a linear regression method. The original data set is pre-clustered into certain groups, so it is possible to identify the metabolites responsible for their differences. PCA is considered descriptive, and OPLS-DA is deemed to be predictive. In addition to those described above, there are many different chemometric methods of analysis [193]. In addition to standard approaches for statistical modeling, enrichment analysis [194] and network inference [195] methods are used to help integrate metabolomics data into multi-omics models. The development of deep learning (DL) technologies is anticipated in metabolomics. DL is used for peak alignment and annotation, identification, quantification of compounds, integration data with other omics disciplines to build multi-omics models [196; 197]. Already, at the primary processing stage of raw data, using trained neural networks, it is possible to filter out up to 90% of false peaks from complex non-target data LC-MS sets without reducing true positive signals. [198]. In matters of building biological models, DL has not yet demonstrated a clear advantage over classical methods of analysis. However, in many studies, this approach showed decent results [196]. For example, comparing DL methods with classical statistical approaches in classification problems on ten clinical metabolic datasets, none of the DLs became the best, although they all showed good or excellent results [199]. There are several challenges in the processing of metabolomics data. On the one hand, the apparent simplicity of the operations performed hides a lack of transparency in data analysis processes because automated pipelines of data processing, in many cases, look like a \"black box.\" On the other hand, custom in-house solutions often fail to scale from one laboratory to another.