What were the top 5 ranked genes by p-value and what were the p-values and the corresponding fold changes (FC)

(Based on Pevsner Problems/Computer Lab 10-5 page 471)
Perform differential gene expression analysis using the digital differential display (DDD) tool:
Go to UniGene at NCBIFamiliarize yourself with DDD by reading the DDD tutorial, then select the link to use DDDUse homo sapiens as species Then:Perform an analysis between two different normal tissues and identify the differentially expressed genes. Discuss your results. Are then any genes that are expressed in only one of the tissues? Are the results expected, surprising, etc.? Suggestion: you can learn more about a gene by using genecards.org, NCBI, wikigenes, etc.
Perform an analysis between two different tumors and identify the differentially expressed genes. Discuss your results, as above. Hint: search for “cancer” or “tumor” in the cDNA library page to find cancer-related data sets.Formulate a hypothesis to test, based on your knowledge of gene expression. (E.g., what genes would one expect to see differentially expressed in pancreatic tumor samples vs. normal pancreatic samples). Select the appropriate libraries and test your hypothesis. Were your expectations confirmed or not?2. GEO2R (Based on Pevsner Problems/Computer Lab 11.1 page 532)
Visit NCBI GEO and select a gene expression dataset to analyze (e.g., search for a type of cancer). Copy its GEO accession number (usually a GSE number, e.g., GSE64670). Feel free to do the exercise using GSE64670, or use another accession number of your choice.Then analyze this data set using GEO2R. Begin by reading the GEO2R tutorial at https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html. First, you will need to define the (usually) 2 analysis groups. E.g., for GSE64670, 6 samples are NeuT and 6 samples are NeuN. Assign the samples to the 2 groups and run the test. Explore the “Options” tab and include various “columns”, such as Gene Ontology (GO) annotations. Discuss your results. What were the top 5 ranked genes by p-value and what were the p-values and the corresponding fold changes (FC). Were there any interesting genes, such as tumor-related genes, etc.?