Comparison of 3 different groups of mouse-derived heart samples using Relative label-free quantification (LFQ) or Redox Mass spectomety methods. The animal groups are A, B, and C. During comparison all were compared such as AvsB, AvsC, and BvsC. A general background of the animals cachexia then in addational treatment or genetic modification was introduced (coded as A, B and C) but since it is an unpublished data, I am not allowed to reveal the exact background. Upon publication, I am going to share the link to the original paper.
Using pathfindR Enrichment Workflow, I used the RA_input example dataset: Most important is the dataformat which is recognized by the command (all dataset must be transformed into that format). Gene.symbol, logFC, and adj.P.Val. The protein-protein interaction network (PIN) pin_name_path can be: “Biogrid”, “STRING”, “GeneMania”, “IntAct”, “KEGG”, “mmu_STRING”. Furthermore, the available gene sets in pathfindR are “KEGG”, “Reactome”, “BioCarta”, “GO-All”, “GO-BP”, “GO-CC” and “GO-MF”. Check out the Analysis_example_dataset.R to figure out what I used.
After loading LFQ datasets, and reformating to have the rigth column names and structure, the same method was used on all focusing on "KEGG" as gene_sets/pin_name_path, and "bonferroni" as adj_method. Some of the visualization function is not functional furthermore only AvsB and BvsC gave relevant results.
Different pathways were identified as affected ones comparing A, B and C conditions. Beside of the heatmaps, heatmap with interaction and Upset plots, a summarized excel was also experoted with all the relevant genes and their most important metrics. AvsB, BvsC
Example for BvsC:
