Of course, in metabolomics analysis, as in the other omics
analyses, large amounts of data are already routinely produced. It
is easily predictable that this “data deluge” will not decrease in the
future, requiring careful attention in the analysis of the results of
each experiment.
Of equal—or even greater—importance will be the consider-
ation of the quality of measurements. For instance, when acquiring
continuous data streams for hours or even days, electronic drift can
bias the results, so equipment checks must be scheduled
periodically.
Statistical methods will be necessary to demonstrate compli-
ance to accuracy and precision requirements. Also reference sam-
ples—especially for the elusive metabolomics domain—need to be
developed in conjunction with such statistical methods.
In the last 5 years, there has been a great increase in the number
of molecular bioimaging tools and bioimaging control software
which support the microscopy hardware to perform the analyses
very quickly and flawlessly.
To efficiently analyze the ever increasing amounts of data, and
extracting information from them, it is essential to have Big Data
systems that can quickly find the correct information, process it,Fig. 5The typical pipeline to obtain 3D in silico model starting from MRI 3D DICOM image set
352 Garima Verma et al.