Use the unfiltered Excel data.
Remove any miRNA that is detected in less than 3 samples.
Replace any undetected value (Inf) with the average of the expression of that miRNA in the rest of the samples.
You should end up with data that is similar to the filtered Excel data.
Use the filtered Excel data.
For each sample, find the CT0 values by averaging the CT values of RNU44, RNU48, and MammU6. Subtract the CT values of that sample with the CT0 of that sample.
The result of the previous step will give you normalized deltaCT values.
For each miRNA, find the average of all healthy deltaCT values and separately, find the average of the patient deltaCT values.
For each miRNA, subtract the average patient deltaCT values from the healthy deltaCT values. This gives you the deltadeltaCt.
Calculate 2^-deltadeltaCT, which are the fold change values.
Replace any fold change value x that is less than 1, with its negative inverse (-1/x).
Print the names and fold changes of the top-10 most changing miRNAs (either up or down regulation).
Using the deltaCT values, find the significantly different miRNAs between controls and patients. List the ten miRNAs with the greatest fold change (include up and downregulation).
Print the names and p-values of the top-10 most significantly different miRNAs (ordered by pvalue).
Repeat the above analysis for normalization using the mean CT of all miRNAs instead of the endogenous control.
Compare the resulting list of significant miRNAs between the two normalization methods.
Use the enrichment_example.xlsx file to determine if the MAPK signaling pathway is enriched in the example target list. Calculate the p-value of enrichment using the
hypergeometric distribution assuming a background of 35000 genes. Use the MATLAB function hygecdf.
Find which mRNAs are predicted targets of the significant miRNAs from the CRPS study using TargetScan.
Perform enrichment of the targets using the DAVID webservice. List the top 3 most significantly enriched pathways and top 3 most significantly enriched Gene Ontology terms, along with their p-values and the number of genes from your gene list found in these pathways and terms.
Make sure to review mirtarbasedb.demo().
You can use mirtarbasedb or targetscandb to get the targets for your micro-RNAs. You should try to end up with several hundred genes. Gene set enrichment with many thousands of genes will not be meaningful. For targetscandb, you can control the confidence score threshold to control how many genes you get. You can also control the number of most-different micrornas to limit the result set.
If you are not getting any target genes at all, make sure to use flexible -3p,-5p matching.