Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP.

ABSTRACT:

Background

Derivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.

Results

The results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the result

SUBMITTER: Edwards H 

PROVIDER: S-EPMC11437665 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature