Multi-Omics Integrative Analysis

We integrate any combination of genomics, transcriptomics, proteomics, metabolomics, lipidomics, and epigenomics (e.g., DNA methylation). Data can come from various platforms including next-generation sequencing, mass spectrometry, and microarrays. We work with raw files (FASTQ, spectra) or pre-processed abundance matrices. Specific requirements are scoped during consultation.

We apply batch-effect removal algorithms such as ComBat and limma on each omic dataset before integration. When combining data across platforms, we use normalization methods that make the distribution comparable, such as quantile normalization or median scaling. The choice of method depends on data characteristics and is detailed in the analysis plan.

Yes. Our service is project-scoped. We can design custom comparisons (e.g., disease vs. control), integrate external metadata (e.g., clinical variables), and focus on particular pathways or molecule types. The analysis plan is discussed and agreed upon before the project starts.

You receive normalized data matrices, correlation/network files, enrichment tables, and a detailed report with visualizations and biological interpretation. Raw output files are provided in common formats (CSV, TSV, Cytoscape.sif, RData). All methods and results are documented to ensure reproducibility.

Yes. For organisms without well-annotated genomes or pathways, we use homology-based functional annotation (e.g., GO mapping via orthologs) and context-specific network inference. We also work with custom reference databases provided by the client. Feasibility is evaluated during the initial scope discussion.