15th Feb, 2022

Independent Academician

Question

Asked 7th Nov, 2017

I have 3 groups. 1. Control 2. Disease 3. Treatment. I want to lookup the gene expression btw these groups, compared with control (whether is upregulated or downregulated).

I did real-time qPCR and have ct values. I calculated ∆Ct = Ct[Target]-Ct[Housekeeping] ... and ∆∆Ct = (∆Exp.)-(∆Control) and got the -∆∆Ct log-fold-change. It looks all the values are almost same and not much different between the groups.

My questions are,

1. What I did was right?

2.If I plot a graph what should I mention in y-axis?

3. Is there any other better way to calculate the gene expression results better?

4.How to calculate log2 fold change and does it helps to see the results more clearer?

p.s I have attached the .xls file for your reference.

Thanks in advance...!!!

Fold change is often used in analysis of gene expression data in micro array and RNA-Seq experiments, for measuring change in the expression level of a gene.[6] A disadvantage to and serious risk of using fold change in this setting is that it is biased [7] and may miss deferentially expressed genes with large differences (B-A) but small ratios (A/B), leading to a high miss rate at high intensities.

Let's say there are 50 read counts in control and 100 read counts in treatment for gene A. This means gene A is expressing twice in treatment as compared to control (100 divided by 50 =2) or fold change is 2. This works well for over expressed genes as the number directly corresponds to how many times a gene is over-expressed. But when it is other way round (i.e, treatment 50, control 100), the value of fold change will be 0.5 (all under expressed genes will have values between 0 to 1, while over expressed genes will have values from 1 to infinity). To make this leveled, we use log2 for expressing the fold change. I.e, log2 of 2 is 1 and log2 of 0.5 is -1.

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looks you calculated Fold change 2^(ΔΔ*CT* )

If you want to calculate log2 fold change, use the same take log base 2.

I added log 2 fold change calculation in your excel sheet data and graph.

- 16.27 KBHowtocalculatefoldchange.xlsx

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Hello i would like to share an article with you ...you just need to follow simle examples given in this article...

Regards

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Fold change is often used in analysis of gene expression data in micro array and RNA-Seq experiments, for measuring change in the expression level of a gene.[6] A disadvantage to and serious risk of using fold change in this setting is that it is biased [7] and may miss deferentially expressed genes with large differences (B-A) but small ratios (A/B), leading to a high miss rate at high intensities.

Let's say there are 50 read counts in control and 100 read counts in treatment for gene A. This means gene A is expressing twice in treatment as compared to control (100 divided by 50 =2) or fold change is 2. This works well for over expressed genes as the number directly corresponds to how many times a gene is over-expressed. But when it is other way round (i.e, treatment 50, control 100), the value of fold change will be 0.5 (all under expressed genes will have values between 0 to 1, while over expressed genes will have values from 1 to infinity). To make this leveled, we use log2 for expressing the fold change. I.e, log2 of 2 is 1 and log2 of 0.5 is -1.

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Thank you Dr.Amal Hassan.

A very detailed explanation with example. Easy for understanding.

Thanks again...!!!

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I am not sure how the answer of my previous colleague relates to the question asked, but one important issue to consider is the choice of an apropriate reference gene, or best if validated using a second reference gene. RT-qPCR certainly has its drawbacks, and may be replaced by newer technologies such as droplet digital PCR in the future, but in my opinion not as 'notorious' if planned carefully, as my colleague seems to make it. Technology has moved on since 1990.

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Thanks Dr.Franco Harald Falcone for your suggestion.

Yes, I am using 3 reference genes for normalization (Validated).

I agree with your point, I will try the droplet digital PCR.

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Statistical measures of differential expression

Let xij and yij denote the log2 expression levels of gene i in replicate j in the control and treatment, respectively. We define the ordinary two-sample t-statistic as Ti = xi· − yi· si (2.1) where si is the standard deviation of the replicates for gene i. The modified t-statistic is defined as T 0 i = xi· − yi· si + so (2.2) where so is a constant chosen to minimize the coefficient of variation of T 0 i . In our analysis, we chose so and computed the modified t-statistic using Significance Analysis of Microarrays (Tusher et al. 2001). **There are two definitions of fold-change in the literature. The standard definition of the fold-change for gene i is (e.g. Tusher et al. 2001) F Ci = x 0 i· /y 0 i· **(2.3) where x 0 ij and y 0 ij are the raw expression levels of gene i in replicate j in the control and treatment, respectively. On the other hand, in Guo et al. (2006) and Choe et al. (2005), the fold-change for gene i is defined as F Ci = xi· − yi· (2.4) We will refer to these versions of fold-change as FCratio and FCdif ference, respectively. It is worth noting that as so in the denominator of the modified t-statistic is increased, the resulting gene ordering approaches that obtained using FCdif ference.

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Amal Hassan In my case I have 3 conditions: my control condition and two diferent treatment conditions, in one of both I have a gene upregulated and in the other, the same gene is downregulated. I have a doubt, when a gene is downregulated you do the log2 but only for the downregulated values? or all of them? (up and downregulated).

Thanks

Laura, you would still use log2 for all of the values. That way, a gene that is upregulated relative to the control will have a positive log2 fold change value, and a gene that is downregulated relative to the control will have a negative log2 fold change value.

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Log2 aids in calculating fold change, and measure the up-regulated vs down-regulated genes between samples. Usually, Log2 measured data more close to the biologically-detectable changes.

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Md. Nazim Uddin _{<you may use NetworkAnalyst software for differential expression analysis, }_{https://www.networkanalyst.ca/}_{ >}
Thank you for your sharing, so can you please share more about the basic steps for that network analysis, which will be more helpful for a beginner

Thanks a lot

Looking forward,

Dr- Hafiz Muhammad Rizwan THERE ARE MANY TUTORIALS ON THIS SOFTWARE. PLEASE FOLLOW THIS LINK.....

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Md. Nazim Uddin Thank you very much, i will check

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- Dec 2017

Integrating gene expression into protein-protein interaction network (PPIN) leads to the construction of tissue-specific (TS) and housekeeping (HK) sub-networks, with distinctive TS- and HK-hubs. All such hub proteins are divided into multi-interface (MI) hubs and single-interface (SI) hubs, where MI hubs evolve slower than SI hubs. Here we explore...

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- Jan 2015

A common practice in gene expression studies is to use ‘housekeepers’, i.e., genes expected to be expressed at relatively constant levels across experimental conditions, to normalize data. The process is to divide an expression value by some composite of one or more stable housekeepers to remove the effect of processing and nuance variables. Despit...

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