pr0c00198_si_003.xlsx (258.75 kB)
Workflow for Rapidly Extracting Biological Insights from Complex, Multicondition Proteomics Experiments with WGCNA and PloGO2
dataset
posted on 2020-05-22, 16:03 authored by Jemma X. Wu, Dana Pascovici, Yunqi Wu, Adam K. Walker, Mehdi MirzaeiWe describe a useful
workflow for characterizing proteomics experiments
incorporating many conditions and abundance data using the popular
weighted gene correlation network analysis (WGCNA) approach and functional
annotation with the PloGO2 R package, the latter of which we have
extended and made available to Bioconductor. The approach can use
quantitative data from labeled or label-free experiments and was developed
to handle multiple files stemming from data partition or multiple
pairwise comparisons. The WGCNA approach can similarly produce a potentially
large number of clusters of interest, which can also be functionally
characterized using PloGO2. Enrichment analysis will identify clusters
or subsets of proteins of interest, and the WGCNA network topology
scores will produce a ranking of proteins within these clusters or
subsets. This can naturally lead to prioritized proteins to be considered
for further analysis or as candidates of interest for validation in
the context of complex experiments. We demonstrate the use of the
package on two published data sets using two different biological
systems (plant and human plasma) and proteomics platforms (sequential
window acquisition of all theoretical fragment-ion spectra (SWATH)
and tandem mass tag (TMT)): an analysis of the effect of drought on
rice over time generated using TMT and a pediatric plasma sample data
set generated using SWATH. In both, the automated workflow recapitulates
key insights or observations of the published papers and provides
additional suggestions for further investigation. These findings indicate
that the data set analysis using WGCNA combined with the updated PloGO2
package is a powerful method to gain biological insights from complex
multifaceted proteomics experiments.