Dear Rayan,

I do not really catch what you explain but I will try to answer using what I did understand.

You used cell lines treated with increasing doses of a compound. You looked at any variation in miRNAs expressions upon treatment. Am I right?

I will used Delta Delta Ct values instead of Delta Ct.

To answer your question, I will recommend you to use both Fold change and pValues to identify statistically relevant differential expression. Regarding the fold change cutoff, I would probably lowered then to below 0,7 and over 1,3.

If you have questions or needs further explanations do not hesitate to contact me.

Hope to help !

Best

From what I understand from your question: You would want to use p-values.

A one-way ANOVA will determine the differences between means when you have one factor. ie. Treatment: treatment 1 vs treatment 2 vs treatment 3 (at a single concentration).

A two-way ANOVA will determine the differences between means when you have two factors. ie Treatment & concentration:

T1 C1

T1 C2

T1 C3

T2 C1

T2 C2

T3 C1

T3 C2

T3 C3

etc.

You will need one data value per exposure. Preferably fold change. However, if required you can use delta Ct.

So if you have one control value you would compare that to one value for each exposure. If you have multiple values for each exposure there are technical replicates. You would take the mean value of those three to represent that exposure.

You will need multiple n values, up to 6. This means you need to repeat that experiment 6 times using new flasks of cells and exposure preparations. Otherwise you cannot correctly apply statistics to your data.

When you do this you will have repeated observations for the same outcomes. This means you will use a repeated measures one, or two, way ANOVA. ANOVAs can be done in most statistical software above Excel. It can be done to a minimal degree using prism.

If you have dCt values from 50 miRNAs, I would suggest using limma (https://bioconductor.org/packages/release/bioc/html/limma.html). The advantage is that limma uses a moderated t-statistic for the tests, what increases power and robustness (by "borrowing" information about the variance from all genes for each test of one of the genes).

Thank you all for taking the time that was very helpfull

Arnaud Beurdeley Yes you are right concerning the experiment i will try the suggested thresholds

Jochen Wilhelm I will check the Limma out, thanks for the suiggestion

Lewis Williams thank you i will try the one-way ANOVA