What are some of the best approaches to find out functional modules in gene expression data .
Do clustering in combination with gene ontology identification. It will help you to look into the functional aspect of your data.
After the statistical analyses filter the data for a minimum expression level and a minimum fold change. Then perform clustering and GO enrichment data. The empirical cutoffs are important to remove data that is statistically significant but likely to be error prone as determined by independent experimental confirmations of specific genes. With the GO enrichment data look for related overarching themes (don't forget to perform a FDR test using a suitable gene background for the analysis).
You could first cluster, and generate a list of differentially expressed genes. You could then sort out those genes with most significant level of expression based on p value and fold change. Other than GO enrichment, you could also look for pathway association using DAVID analyses. There may be chance that some of your candidate genes may already be identified in some processes, pathways or human diseases.
first sort the genes on the basis of pvalue and log2foldchange value. then after getting the significant genes u can annotate those genes using Kegg pathways and check whether ur genes are taking part in any pathways or not and u can also do the GO of those genes.
- Badges
- Science topic
- Similar topics
- Biological Science
- Genetics
- Gene Expression