Journal: BMC Bioinformatics

Article Title: Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

doi: 10.1186/s12859-024-05938-9

Figure Lengend Snippet: MATLAB App Based GUI DPoP: the distribution of derivatives at a point, as well as the distribution of slopes, is represented in the GUI on the top left. The inputs/user controls are at the bottom left. The list of signals with significantly positive and negative derivatives is in the column in the middle. The column section on the right displays the results of the GO term enrichment analysis applied to the list of significant signals, with respect to the population of signals. Controls are included in the GUI to select the statistical significance, the normalization factor, the GO database file, GO database field identifier, the first and final timepoint of interest in the regression, the first and final time point of interest within the regression, and the threshold for filtering based on noise or number of consecutive non-zero signals

Article Snippet: We have packaged our DP method within a GUI-based MATLAB app, DPoP, or Derivative Profiling omics Package.

Techniques:

Journal: BMC Bioinformatics

Article Title: Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

doi: 10.1186/s12859-024-05938-9

Figure Lengend Snippet: Flowchart of derivative profiling workflow. Data is input in the first block labeled “organized-omics data”. Any arrow is a tunable process operation of the overall workflow. Lists can be output and analyzed independently without bioinformatic analysis

Article Snippet: This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP).

Techniques: Blocking Assay, Labeling