Analyzing gene expression data in qbase+
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Once runs are imported, you can start analyzing the data. Data consist of Cq values for all the wells.
Contents
Specifying the aim of the experiment
On the Aim page you tell the software what type of analysis you want to do. Different types of analyses require different parameters, parameter settings and different calculations. By selecting the proper analysis type, qbase+ will only show the relevant parameters and parameter settings.
Since we are doing a gene expression analysis in this exercise, this the option we should select.
Click the Next button on the bottom of the page to go to the Technical quality control page.
Checking the quality of technical replicates and controls
The Technical quality control page handles the settings of the requirements that the data have to meet to be considered high quality. For instance the maximum difference between technical replicates is defined on this page. If there are technical replicates in the data set, qbase+ will detect them automatically (they have the same sample and target name) and calculate the average Cq value. In theory, technical replicates should generate more or less identical signals.
| How to set the maximum difference in Cq values for technical replicates ? |
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| The quality criterium that the replicates must meet to be included for further analysis is one of the parameters in qbase+. You can set it on the Technical quality control page:
 The default maximum allowed difference in Cq values between technical replicates is 0.5 |
Additionally, you can do quality checks based on the data of the positive and negative controls.
| How to set quality requirements for the control samples ? |
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On the same Technical quality control page you can define the minimum requirements for a well to be included in the calculations:
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Warning:
Wells that do not meet one of these criteria are flagged but not automatically excluded.
Wells that do not have a signal (typically negative controls) are automatically excluded.
Excluded means that the data are ignored in the calculations.
| How to check if there are wells that do not meet these criteria ? |
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| You can see flagged and excluded data by ticking the Show details… options (blue) on the Technical quality control page and clicking the Next button (purple) at the bottom of the page.
Qbase+ will open the results of the quality checks for the replicates (red) and the controls (blue) on two different tabs. These tabs show lists of samples that failed the quality control criteria.
 When the difference in Cq between technical replicates exceeds 0.5, the wells end up in the flagged or failing list. They are included in calculations unless you exclude them by unticking them. |
If you are finished checking the data quality, click Next to go to the Amplification efficiencies page.
Taking into account amplification efficiencies
Qbase+ calculates an amplification efficiency (E) for each primer pair (= gene). Genes have different amplification efficiencies because:
- some primer pairs anneal better than others
- the presence of inhibitors in the reaction mix (salts, detergents…) decreases the amplification efficiency
- inaccurate pipetting
Qbase+ has a parameter that allows you to specify how you want to handle amplification efficiencies on the Amplification efficiencies page.
| How to specify the amplification efficiencies strategy you want to use ? |
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Since we have included a dilution series for creating a standard curve in our qPCR experiment, we will select
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Amplification efficiencies are calculated based on the Cq values of a serial dilution of representative template, preferably a mixture of cDNAs from all your samples. Since you know the quantity of the template in each dilution, you can plot Cq values against template quantities for each primer pair. Linear regression will fit a standard curve to the data of each gene, and the slope of this curve is used to calculate the amplification efficiency.
| How to check the amplification efficiencies of the genes ? |
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| Once you have made this selection, qbase+ starts calculating the efficiencies and the results are immediately shown in the calculation efficiencies table.
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In this way, one amplification efficiency (E) for each gene is calculated and used to calculate Relative Quantities (RQ):
∆Cq is calculated for each well by subtracting the Cq of that well from the average Cq across all samples for the gene that is measured in the well. So ∆Cq is the difference between the Cq value of a gene in a given sample and the average Cq value of that gene across all samples.
Cq is subtracted from the average because in this way high expression will result in a positive ∆Cq and low expression in a negative ∆Cq.
Click Next to go to the Normalization page.
Normalization
Differences in amplification efficiency are not the only source of variability in a qPCR experiment.
Several factors are responsible for noise in qPCR experiments e.g. differences in:
- amount of template cDNA between wells
- RNA integrity of samples
- efficiency of enzymes used in the PCR or in the reverse transcription
Noise: variability between samples that has no biological relevance
Normalization will eliminate this noise as much as possible. In this way it is possible to make a distinction between genes that are really upregulated and genes with high expression levels in one group of samples simply because higher cDNA concentrations were used in these samples.
In qPCR analysis, normalization is done based on housekeeping genes.
Housekeeping genes: genes with constant expression levels in all cell types, tissues and conditions that are studied in the experiment
Housekeeping genes are measured in all samples along with the genes of interest. In theory, a housekeeping gene should have identical RQ values in all samples. In reality, noise generates variation in the expression levels of the housekeeping genes. This variation is a direct measure of the noise and is used to calculate a normalization factor for each sample.
Normalization Factor (NF): factor that is multiplied to the RQ values so that the measured expression levels of the housekeeping genes are equalized across all samples. There is one NF for each sample.
These normalization factors are used to adjust the RQ values of the genes of interest accordingly so that the variability is eliminated.
These adjusted RQ values are called Normalized Relative Quantities (NRQs).
In qbase+ housekeeping genes are called reference genes. In our data set there are three reference genes: Stable, Non-regulated and Flexible. On the Normalization page we can define the normalization strategy we are going to use, appoint the reference genes and check their stability of expression.
| How to specify the normalization strategy you want to use ? |
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| You can specify the normalization strategy you want to use on the Normalization method page:
We have incorporated 3 housekeeping genes in our experiment so we select the Reference genes strategy. |
| How to appoint reference targets ? |
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| You have to indicate which targets should be used as reference genes since qbase+ treats all genes as targets of interest unless you explicitly mark them as reference genes on the Normalization method page:
 We have measured 3 housekeeping genes: Stable, Flexible and Non-regulated so we tick the boxes in front of their names. |
It's not because you have appointed genes as reference genes that they necessarily are good reference genes. They should have stable expression values over all samples in your study. Fortunately, qbase+ checks the quality of the reference genes.
For each appointed reference gene, qbase+ calculates two indicators of expression stability
- M (geNorm expression stability value): calculated based on the pairwise variations of the reference genes.
- CV (coefficient of variation): the ratio of the standard deviation of the NRQs of a reference gene over all samples to the mean NRQ of that reference gene.
| Are Flexible, Stable and Nonregulated good reference targets ? |
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| M and CV values of the appointed reference genes are automatically calculated by qbase+ and shown on the Normalization method page:
 The default limits for M and CV were determined by checking M-values and CVs for established reference genes in a pilot experiment that was done by Biogazelle. Based on the results of this pilot experiment, the threshold for CV and M was set to 0.2 and 0.5 respectively. If a reference gene does not meet these criteria it is displayed in red. As you can see the M and CV values of all our reference exceed the limits and are displayed in red. |
It should be noted that for some experiments (heterogeneous samples, samples from fly or plant) the limits for CV and M-values may be increased to 0.5 and 1 respectively.
If the quality of the reference genes is not good enough, it is advised to remove the reference gene with the worst M and CV values and re-evaluate the remaining reference genes.
| Which reference target are you going to remove ? |
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| Both the M-value and the CV are measures of variability. The higher these values the more variable the expression values are. So we will remove the gene with the highest M and CV. |
You can remove a reference gene simply by unticking the box in front of its name.
| Are the two remaining reference genes good references ? |
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| After removing Flexible as a reference gene the M and CV values of the two remaining reference genes decrease drastically to values that do meet the quality criteria. M and CV values that meet the criteria are displayed in green.
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This exercise shows the importance of using a minimum of three reference genes. If one of the reference genes does not produce stable expression values as is the case for Flexible, you always have two remaining reference genes to do the normalization.
See how to select reference genes for your qPCR experiment.
So after normalization you have one NRQ value for each gene in each sample.
Click Next to go to the Scaling page.
Scaling
Rescaling means that you calculate NRQ values relative to a specified reference level.
Warning: Scaling only changes the scale, so the expression levels will be different but not the fold changes between the samples
Qbase+ allows you to rescale the NRQ values using one of the following as a reference:
- the sample with the minimal expression
- the average expression level of a gene across all samples
- the sample with the maximal expression
- a specific sample (e.g. untreated control)
- the average of a certain group (e.g. all control samples): this is often how people want to visualize their results
- positive control: only to be used for copy number analysis
After scaling, the expression values of the choice you make here will be set to 1 e.g. when you choose average the average expression level across all samples will be set to 1 and the expression levels of the individual samples will be scaled accordingly.
| How to scale to the average of the untreated samples ? |
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You can specify the scaling strategy on the Scaling page. Select Scale to group and set the Scaling group to the untreated samples (red). This is one of the reasons why you need the grouping annotation.
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Rescaling to the average of a group is typically used to compare results between 2 groups, e.g. treated samples against untreated controls. After rescaling, the average of the NRQs across all untreated samples is 1 and the NRQs of the treated samples are scaled accordingly.
Click Next to go to the Analysis page.
Visualization of the results
One of the things you can select to do on the Analysis page is viewing the relative expression levels (= scaled NRQs) of each of the genes in a bar chart per gene. It is recommended to visualize your results like this.
It is possible to view the relative expression levels of all genes of interest on the same bar chart. You can use this view to see if these genes show the same expression pattern but you cannot directly compare the heights of the different genes because each gene is independently rescaled!
| How to visualize single gene expression bar charts ? |
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| Select Visually inspect results For individual targets on the Analysis page and click Finish
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| How to visualize the expression levels of Palm in each sample ? |
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| Select Visually inspect results For individual targets on the Analysis page and click Finish
The Target select box allows you to select the gene you want to view the expression levels of. Relative expression levels are shown for each sample. Error bars are shown and represent the technical variation in your experiment (variation generated by differences in amounts pipetted, efficiency of enzymes, purity of the samples...).
 You see that Palm has a low expression level and a very large error bar in Sample05 because the two replicates of this sample had very different Cq values. |
You can group and colour the bars according to a property.
| How to group the bars of Palm according to treatment (so treated at one side and untreated at the other side) |
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| In the Grouping section you can specify the property you want to group by.
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| How to view average expression levels in each group ? |
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| In the Grouping section you can choose to plot individual samples as shown above but you can also choose to plot group average expression levels.
 The error bars that you see here represent biological variation and will be used later on in the statistical analysis. The error bars are 95% confidence intervals which means that they represent the range that will contain with 95% certainty the real average expression level in that group of samples. |
The nice characteristic of 95% confidence intervals is the following:
- if they do not overlap you are sure that the expression levels in the two groups are significantly different, in other words the gene is differentially expressed
- if they do overlap you cannot say that you are sure that the expression levels are the same. You simply don’t know if the gene is differentially expressed or not.
| Assess the effect of switching the Y-axis to a logarithmic scale for Palm. |
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| In the Y axis section you can specify if you want a linear or logarithmic axis.
 As you can see you do not change the expression values, you just change the scale of the Y axis. |
Warning: Setting the Y-axis in logarithmic scale does not mean that you log transform the NRQs !
Switching the Y-axis to a logarithmic scale can be helpful if you have large differences in NRQs between different samples
| Assess the effect of switching the Y-axis to a logarithmic scale for Flexible. |
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| Switch to the bar charts of Flexible.
 By switching the Y-axis to logarithmic you can now see more clearly the differences between samples with small NRQs. |
Warning: Never directly compare the heights of the bars of different genes because each gene is independently rescaled!
Statistical analysis
Once you generate target bar charts you leave the Analysis wizard and you go to the regular qbase+ interface. Suppose that you want to perform a statistical test to prove that the difference in expression that you see in the target chart is significant.
At some point, qbase+ will ask you if your data is coming from a normal distribution. If you don't know, you can select I don't know and qbase+ will assume the data are not coming from a normal distribution and perform a stringent non-parametric test.
However, when you have 7 or more replicates per group, you can check if the data is normally distributed using a statistical test. If it is, qbase+ will perform a regular t-test. The upside is that the t-test is less stringent than the non-parametric tests and will find more DE genes. However, you may only perform it on normally distributed data. If you perform the t-test on data that is not normally distributed you will generate false positives i.e. qbase+ will say that genes are DE while in fact they are not. Performing a non-parametric test on normally distributed data will generate false negatives i.e. you will miss DE genes.
Checking if the data is normally distributed can be easily done in GraphPad Prism. To this end you have to export the data.
| How to export the data ? |
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| To export the results click the upward pointing arrow in the qbase+ toolbar:
 You want to export the normalized data so select Export Result Table (CNRQ):
 You will be given the choice to export results only (CNRQs) or to include the errors (standard error of the mean) as well (red). We don't need the errors in Prism so we do not select this option. The scale of the Result table can be linear or logarithmic (base 10) (green). Without user intervention, qbase+ will automatically log10 transform the CNRQs prior to doing statistics. So we need to check in Prism if the log transformed data are normally distributed. Additionally, you need to tell qbase+ where to store the file containing the exported data. Click the Browse button for this (blue).
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Exporting will generate an Excel file in the location that you specified. However, the file contains the results for all samples and we need to check the two groups (treated and untreated) separately. The sample properties show that the even samples belong to the treated group and the odd samples to the untreated group.
This means we have to generate two files:
- a file containing the data of the untreated samples
- a file containing the data of the treated samples
Now we can open these files in Prism to check if the data is normally distributed.
| How to import the data of the untreated samples in Prism ? |
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 Prism has now created a table to hold the data of the untreated samples but at this point the table is still empty. To load the data:
 As the file is opened in Prism you see that the first column containing the sample names is treated as a data column. Right click the header of the first column and select Delete
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| How to check if the data of the untreated samples comes from a normal distribution ? |
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 Prism now generates a table to hold the results of the statistical analysis:
 As you can see, the data for Palm are not normally distributed. |
Since we found that there's one group of data that does not follow a normal distribution, it's no longer necessary to check if the treated data are normally distributed but you can do it if you want to.
We will now proceed with the statistical analysis in qbase+
Statistical analyses can be performed via the Statistics wizard.
| How to open the Statistics wizard ? |
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You can open it in the Project Explorer (window at the left):
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This opens the Statistics wizard that allows you to perform various kinds of statistical analyses.
| Which kind of analysis are you going to do ? |
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| On the Goal page: Select Mean comparison since you want to compare expression between two groups of samples so what you want to do is comparing the mean expression of each gene in the treated samples with its mean expression level in the untreated samples. Click Next. |
| How to define the groups that you are going to compare ? |
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| On the Groups page: specify how to define the two groups of samples that you want to compare. Select Treatment as the grouping variable to compare treated and untreated samples. Click Next.
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| How to define the genes that you want to analyze ? |
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| On the Targets page: specify for which targets of interest you want to do the test. Deselect Flexible since you do not want to include it in the analysis. It's just a bad reference gene. Click Next.
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On the Settings page you have to describe the characteristics of your data set, allowing qbase+ to choose the appropriate test for your data.
The first thing you need to tell qbase+ is whether the data was drawn from a normal or a non-normal distribution. Since we have 8 biological replicates per group we can do a test in Prism to check if the data are normally distributed.
| Which gene(s) is/are differentially expressed ? |
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| On the Settings page you describe the characteristics of your data set so that qbase+ can choose the ideal test for your data. For our data set we can use the default settings. Click Next. In the results Table you can see that the p-value for Palm is below 0.05 so Palm is differentially expressed.
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