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Analysis principles​

Dataset behaviour before interpretation

Interpretation begins with dataset structure, sample composition, proteome depth, and data completeness.

Replicate coherence and global structure

Replicate consistency and overall data structure are examined early to judge how reliably differences can be interpreted.

Explicit filtering and threshold decisions

Filtering, completeness thresholds, and imputation are treated as analytical choices that shape interpretation.

Quantitative differences in context

Statistical results are considered alongside effect size, replicate consistency, and coverage depth.

Conditional downstream analysis

Functional and enrichment analyses are applied selectively, guided by the strength of the quantitative structure.

Explicit communication of limits

Red flags and limitations are stated clearly, and conclusions stay within what the data can support.