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Figure 1.6 Mass spectrometry data in systems biology. Mass spectrometry is
the technique of choice for identification and quantification of biological
molecules except mRNA. Data from proteomics and transcriptomics experiments
(usually performed by microarray) can be correlated using statistical coefficients
[43,74] for a comprehensive description of the biological system. Statistical
correlation of metabolite pairs (measured by mass spectrometry) is another
way to follow biological perturbations [93]. Integration of data acquired at
mRNA, protein, and metabolite level will involve high-throughput data
generated by mass spectrometry and microarray experiments together with
statistical methods for correlation of these data sets. Color code: dark gray, data
are presented in an array format, ready for complex statistical analysis (i.e.,
clustering); light gray, data are presented in an array format that describes the
trend in changing of concentrations, which might be potentially used for
monitoring enzyme activites; white, data are derived from profiling
experiments for further comparison with metabolites determined in the same
set of experiments rather than comparison with other large omics data sets.
overall (Spearman rank 0.45) for 678 loci. Close examination of the log-
log plot indicated analysis of data both by loci and by biological
pathway. Better correlation has been found across loci involved in the
same biological pathway.
In another study involving the integrated analysis of mitochondria,
Mootha et al. [91] used a significance test rather than a correlation coef-
ficient. In this study, genes at the transcript level (mRNA) from four
tissues (brain, heart, kidney, and liver), that is, from multicellular envi-
ronments, were compared with protein identification from proteomics
experiments. Statistics of these two data sets (mRNAs and proteins)
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