My goal is to demonstrate amplification of a target miRNA in the exosomes produced in a cell line of murine astrocytes. Because there are no universal controls for exosomal miRNA, I was told that by with the method of global mean normalization, I would not need any positive controls.
I have performed qPCR and obtained Cq values for my experimental and control samples. I haven't been able to find, however, the direct set steps I would need to perform to use global mean normalization. Could anyone help me analyzing my data?
Any advice would be highly appreciated! Thanks!
Hi Indrani..
I have almost a similar issue of estimating gene expression in the cancer cells.We dont have any control for this like your...
I found that absolute gene expression is the method have to apply if you have no control.
I have no idea about global mean normalization.. If some one explains which method to use, it will be great...
Hi
Global mean ... means u will take all the samples of that miR and determine its gmean and minice it from all glonlbal mean of miR normalizer
Check this
http://www.ncbi.nlm.nih.gov/pubmed/22144205
For global mean normalization you would need to determine Ct values for large number of miRNAs tested per each sample (or even the whole miRNome). High Ct values (above certain threshold) will have to be removed (noise) and the remaining Ct values should be used to calculate mean Ct across all miRNAs per each sample separately. Then you would calculate the difference Ctnorm(miRNA1, sample A) = Ct(miRNA1, sample A) - Ct (mean, sample A) for all miRNAs and you would get global mean normalized values for each miRNA and given sample A.
If you want to calculate fold change e.g. for miRNA1 for two sample classes (e.g. treatment vs control), you would need to do the following:
1. Calculate averages across all replicated treatments (e.g. tr1-tr3 if you have 3 replicates):
x=1/3[(Ctnorm(miRNA1, tr1)+ (Ctnorm(miRNA1, tr2)+(Ctnorm(miRNA1, tr3)]
2. Calculate averages across all replicated controls (e.g. ct1-ct3 if you have 3 replicates):
y=1/3[(Ctnorm(miRNA1, ct1)+ (Ctnorm(miRNA1, ct2)+(Ctnorm(miRNA1, ct3)]
3. Fold difference will be:
F(miRNA1, tr/ct) = 2 ^ [ - (x-y) ]
The hypothesis behind global mean normalization is that when a large unbiased set of gene targets is quantified, their mean expression level reflects the input amount and could be used for normalization. This method is appropriate for omics data and uses information from all of the measured data (except genes with too low expression). You cannot be a priori sure that mean Ct of any 5 miRNAs that you choose is biologically invariant across your experimental groups and only reflects the total miRNA input amount. If you have an a priori knowledge that some specific combination of 5 miRNAs is good for normalization in your experiment, you can use it, but it will no longer be a global mean normalization, but a normalization to a set of reference genes.
Reference:
https://genomebiology.biomedcentral.com/articles/10.1186/gb-2009-10-6-r64
@ Roman Mezencev
after you calculate fold difference using formula as you mentioned. How to convert it to log fold change ?
3. Fold difference will be:
F(miRNA1, tr/ct) = 2 ^ [ - (x-y) ]
How to calculate log fold change ? what is the meaning of
-4.2 log fold difference compared to 4.2 log fold difference calculated using treatment as nominator (treatment/control)
Thanks
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