How do you integrate omics data from different sources and platforms?

A major challenge with any platform is data hormonization, for example, RNA seq data sets from Illumina and Affymetrix, bulk transcriptomics and single cell transcriptomics, different normalization methods, etc., which makes accurate interpretation difficult. It becomes even more difficult to compare datasets across platforms such as transcriptomics, proteomics, and metabolomics. Data hormonization has several methods, but finding something intuitively useful is rare. So I ended up innovating my own methods for example, to interpret the expression of metabolic genes in disease without normal specimens, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576537/ and harmonize large/different datasets https://doi.org/10.3390/biomedicines10112720.

> Ensure omics data adheres to common standards for compatibility. > Use platforms supporting seamless integration of diverse datasets. > Standardize metadata for consistent integration. > Apply robust techniques for cross-platform data normalization. > Utilize bioinformatics tools for efficient multi-omics merging. > Apply systems biology for holistic molecular insights. > Implement algorithms for automated pattern recognition. > Foster shared methodologies and best practices. > Enforce measures to ensure data reliability and accuracy. > Develop flexible approaches for evolving technologies.

Continuing from previous section above, as a result of transforming all numbers in the omics dataset into directional vectors based on median shifts, numbers between 0 and 0.5 exhibit receding and progressive effects, respectively. Therefore, every number in a data matrix acts as a Doppler effect. This format allows for the seamless integration of multiplatform or multi-omics datasets, with each data point telling its own biological story very precisely. Its ability to provide directionality allows it to forecast past and future biological states, as well as possible mutations that may occur, providing one of the most powerful ways of planning future therapies for the same patient. (see https://doi.org/10.3390/biomedicines10112720).

Data interpretation requires intuitive thinking and biologically sound judgments. Statistical analysis, such as pathway enrichment analysis, can be incredibly useful, but mostly riddled with redundancy. Therefore, interpreting such enrichments, needs intellectual/manual collation of the results into a meaningful biological summary. A key power of omics is gaining a bird's eye view of everything happening inside a disease and understanding its heterogeneity. Instead of finding answers themselves, omics researchers seek tailored solutions. Biology is a much more complex process, since (cell) divisions in biology only result in multiplication unlike fractions as in mathematics.

Despite most omics data representations looking fancy in publications, the ultimate goal must be to render intuitive summaries that can be easily understood by readers. Instead of showing massive heatmaps or furballs, one glance at the figure should tell the story. Thus, learning how to consolidate data and convey the findings into a simple summary is the most effective way to communicate the main findings. See Figure 6, in this research https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000527 and Figure 5E in this research https://doi.org/10.3390/biomedicines10112720)

This is a very good article, we are just starting this process. What software would be best to use? Is there anything off the shelf? Any feedback will be great. Many thanks!