I have two sets of gene expression data from agilent microarray (SurePrint G3 Human Gene Expression v3 8x60K Microarray), one for a gene perturbation sample and the other as control. And now I need to identify the significantly differentiated expressed genes and the regulatory networks affected by the gene perturbation. I have good knowledge on Python and intermediate on R. Online search I found lots of suggestions. But I wanted to know If anyone has experience and suggestions for a good point to start. Thank you
This workflow will definitely put you in the right direction: https://www.bioconductor.org/help/workflows/arrays/
One option I would recommend to investigate co-expression networks changing between the two conditions is the R package WGCNA (Weighted Gene Co-expression Network Analysis). Several tutorials and published application examples are available and there is also very active and informative support.
Analyze your own microarray data in R/Bioconductor (R-code to identify DE genes based on Affymetrix microarray)