Real Time QPCR Data Analysis Tutorial (part 2)

In this Bio-Rad Laboratories Real Time Quantitative PCR tutorial (part 2 of 2), you will learn how to analyze your data using both absolute and relative quantitative methods. The tutorial also includes a great explanation of the differences between Livak, delta CT and the Pfaffl methods of analyzing your results. For more videos visit www.americanbio...

[@jdprocknow]

You should clarify a little at 4:14. _Positive delta delta Ct_ "indicate a difference in expression that is lower than the control sample."

[@ifeanyichukwueke9942]

Great video! My first introduction to the world of RT-PCR data analysis!

[@mavisosei-wusu9333]

Great an timely video, just what I needed to start my gene expression experiment, thank you so much.

[@americanbiotech]

@waweel, Once you have the data you need to analyze it using standard statistical methodology. Which method you use really depends on a number of factors including how your experiment was run, how many samples were included and how many variables are being analyzed.

[@andrespun5940]

Min 4:13... How is it posible to get negative values in an exponential funtion? They should say: "values LOWER THAN 1 indicate a difference in expression that is lower than the control sample"

[@DA-sj2gw]

Thats what she said..

[@andrespun5940]

She says: "NEGATIVE values indicate a difference in expression that lower than the control sample". FYI: NEGATIVE means < 0

[@DA-sj2gw]

oh sorry heard it wrong haha

[@nouraseleem1]

negative ddCt indicates a more targeted gene mRNA after treatment and a fewer PCR cycles this is induction and in calculating fold change as 2^-(ddCt) it will give a positive value more than 1. the reverse is true for a positive ddCt, less mRNA, more cycles, inhibition, gives values less than 1 as fold changes.

[@folenspill]

Excellent video to recapitulate the basics. Thank you!

[@tadesekahsay]

I found it a valuable lesson to my work

[@americanbiotech]

b is the y intercept in the equation y=mx=b

[@KiranKumar-gs7mp]

Great video ✌

[@waweel]

After we calculate the fold difference in expression between the control sample and the tested samples, how could we know that the increase or decrease in expression is significant?

[@giovag.996]

Did you find the answer?

[@anandsagar1351]

IF IT IS GRETAER THAN 1 , IT IS UPREGULATION AND IF THE VALUE OF FOLD CHAIN IS LESS THAN 1, THE EXPRESSION IS DOWNREGULATION.

[@dr.aparnamb2440]

This was really helpful.....I am using BioRad CFX 96 in my lab and was looking forward for to learn the interpretation of my results

[@americanbiotech]

@ufukufi Typically, it is very difficult to obtain statistically significant differences with fold changes below 1. However, the p-value will set you free!

[@user-rf7ro2tt2w]

Can you explain how?

[@cvrvidal]

Great video!! Thanks!

[@gerlanebarros3961]

Excellent video!

[@anjalikrishnaa6940]

great explanation! thanks!

[@jojyo23]

I am sorry I meant Reaction efficiency!

[@hussaintouseef8]

Excellent one, but how to analyse the data for absolute quantification ? what does B stands while calulating Qauntity ?

[@MrPrabhubct]

fine

[@cassandravan5631]

Hi, I'm actually getting different fold changes when I use the Livak method or the deltaCt method. Does anyone know what this could mean? Is it an indication of my reaction efficiency?

[@tomtheginger]

This was really helpful thanks. So if you had multiple samples of control and treated, how would you go about comparing them? I'd assume you'd calculate the deltaCT for each sample in the control and treated groups, and then average them before performing the deltadeltaCT calculation?

[@xiuchuntian]

You should do the deltadeltaCT for each control and treated sample first to get the "relative expression" of each sample, then use statistics to determine the significance between controls and treated. It is not different than doing stats from temperature values of 10 control patients and compared them to temperatures of 10 aspirin-treated patients. The temperature value of each patient is the same as the deltadeltaCT for each sample, control or treated.

[@sdeee241]

this is impossible!!! your gapdh ct is higher than your target gene that means your housekeeping is express less than you target gene so the signs of dc becomes positive and i don't know how to explain the rest of that would you please help!

[@jojyo23]

Hi! Can you please tell me which is the Expression level value for target and calibrator

[@sdikwella7756]

Hello, I am struggling with the Ct values I got from my lab. For my gene of interest I got no amplification for the negative control cells, for the test cells I got 30.935 and control cells 33.759. For my reference gene I got 30.310 for negative control, 28.260 for test and 28.022 for control. I got wrong results since the negative control where there should not be amplification and the ct values are too high. However I need to calculate the delta ct for test (I got 2.67) and for control (I got 5.7) to compare house keeping and gene of interest. But what do these values really tell me? And for the double delta Ct I got 8. What does it mean? I hope to have a reply from you soon!! Best wishes, Natalie

[@DA-sj2gw]

0 min: ct = 34, stimulated 30 min: ct = 35, stimulated 60 min: ct = 32, RT- = 33 how do I calculate ?

[@jamalelldenadam6055]

good job

[@sivasankar372]

Nice..

[@shajidislam2222]

Thank you very much, I have a question. As you mention that in delta delta ct method, we can get only gene expression changes in tumor tissue but if I want to know the gene expression in my control sample, how to do it. please suggest me.

[@giovag.996]

Being a control should be known, isn't it?

[@meseretabebe1541]

Super!

[@5-Hydroxy-Tryptophan]

Does anybody knows where i can get a Reference for a paper where I compare my gene with a housekeeping gene?

[@roffigrandiosaofficial]

thanks

[@CarnEHge]

How do you measure efficiency?

[@monamrihiel3832]

If I dont know who's th control what I should do

[@ufukufi]

what about fold if it is less than 1? for example, by Livak method, we found 2^-ΔΔCt as 0,300. what does it mean?

[@--HiroshiChawla]

most probably , that may be control and test gene showed same expression level

[@SpunkyJ1]

Hi, May I know where can I get the slides?

[@afro7942]

likes it

[@mellorasharman421]

- 1.5 - (- 3.9) = +2.4 doesn't it? Not -2.4! Therefore wouldn't 2^-(ddCT) = 2^-2.4 = 0.18?

[@shamimrahman1949]

No, it's test - calibrator so it's - 3.9 - (- 1.5) = -2.4

[@nouraseleem1]

no. when you calculate ddCt = (dCt ref- dCt test) . then the fold change will be 2^ddct. not add a minus.

[@shamimrahman1949]

I got lost when comparing the delta Ct method with Livak (at kzbin.info/www/bejne/qpjTZZWYo89gpMkm30s). Where are you getting the two "control" values (both 2.8) from?

[@rachelsmith3258]

This calculation is demonstrating that you can obtain the same results using both methods when you have 100% reaction efficiency. The 2.8 is Ct ref - Ct target for the control calibrator sample. When you divide it by itself, you get a fold change of 1 - no change. When you divide the test sample by the control calibrator however, you get a 5.3 fold expression change, just like the Livak method.

[@shamimrahman1949]

Rachel Smith Thanks. I couldn't understand the need to divide the delta Ct control by itself - I see now. Makes sense.

[@idarmistorresfigueroa5680]