Hi everyone. I have searched all around the internet and literature for the answer to this question but haven’t been able to find any info regarding my specific situation.
I have multiple experiments consisting of qPCR data but can’t figure out how to best analyse it. I have WT and KO cells which I apply 3 treatments to and I have a control (no treatment) for both genotypes, and I check 15 genes. What I really want to show is if the genes are up/downregulated when I add a treatment in ko vs wt, so I want to make my comparison between the genotypes. But I can’t compare them directly, because at baseline they have quite different expression levels already, so I want to take the control for each into consideration. Before I was plotting -Dct (normalised to housekeeping gene only) and would compare each treatment in each genotype to its own control. But my group didn’t like this, which I understand, because the graphs are cluttered and I don’t show the comparison I’m really trying to make. I worked with a bioinformatician with my idea to normalise the Dct for each genotype/treatment to its own control and in that way make DDct, and then I compare the -DDct between genotypes for each treatment using an unpaired t-test. I don’t do fold change. These graphs are much nicer to look at, but my supervisor says it doesn’t make statistical sense this way, and wants to keep the graphs the original way.
can anyone help me out? What is the best way to analyse and graph my data?
QPCR stands for quantitative polymerase chain reaction. It is a technique used to measure the amount of DNA or RNA in a sample. Multiple controls are used in QPCR to ensure that the results are accurate. These controls include positive controls, which are samples that contain known amounts of the target DNA or RNA, and negative controls, which are samples that do not contain the target DNA or RNA.
Did you run a reference gene stability study, i.e. geNorm, Normfinder, BestKeeper (etc)? With some of these reference gene stability methods, use of more than a single gene is permitted and may even be required for normalization. I'm not an expert on the delta Ct normalization method, but I just wanted to remind that these options do exist.
Ensuring that your data analysis and visualization methods are scientifically valid and effectively convey the information is crucial. Here's a suggestion for a statistical analysis that might better suit your needs:
Relative Expression Analysis:
Instead of using -Dct or -DDct, Take into account utilizing the approach referred to as 'Relative Expression'. The fold change in gene expression between treated samples and their corresponding controls is calculated within each genotype using this method. This way, you'll be comparing the change in expression due to treatment in both genotypes separately.
Relative Expression (RE) = 2^(-Dct) [where Dct = Ct(target gene) - Ct(housekeeping gene)]
Fold Change Calculation:
Calculate the fold change for each gene between treated samples and their controls within each genotype using the relative expression values obtained in step 1.
Fold Change Division of RE(treatment) by RE(control) RE(treatment) / RE(control)
Comparison Between Genotypes:
Having the fold change values for each gene within both genotypes, examine and contrast the fold changes between KO and WT genotypes across all treatments An adequate statistical test can be employed to perform this task, like employing either an unpaired t-test or a non-parametric alternative when the data doesn't satisfy the assumptions of a t-test.
Graphical Representation:
Your data can be visually represented using bar graphs, Displaying the fold change in gene expression for each treatment in both KO and WT cells. Visualizing the relative variations in gene expression among the genotypes post-treatment is facilitated by this.
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