Copying data annotation

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The experiment consists of two runs (plates): Run9 and Run10
The following samples were used:

• 10 control samples: control1, control2…
• 10 treated samples: treated1, treated2…
• 1 no template control: NTC
• 1 positive control: POS

The expression of the following genes was measured:

• 2 reference genes: Refgene1 and Refgene2
• 2 genes of interest: Gene1 and Gene2

There are two technical replicates per reaction.

Creating a new experiment

Create a new Experiment called CopyAnnotation in Project1
You can find the details on how to create a new experiment in Creating a project and an experiment

Loading the data

Import Run9 and Run10. These files are in qBase format.
You can find the details on how to import the data file in the Loading the data into qbase+ section of Analyzing data from a geNorm pilot experiment in qbase+

Close the Analysis wizard by clicking the Close wizard button and look at the data. Open both files simultaneously so that you can compare the annotation.

Do you see differences in annotation ?

Expand Runs in the Project Explorer and double click the names of the runs you want to open. You can visualize the two files simultaneously by: selecting the tab (red) of one of the files while pressing the mouse button, drag it to the right until you see a dark grey frame around the right half of the main window and a right pointing arrow (green) release the mouse button As you can see Run9 contains the data of the reference genes, while Run10 contains the data of the genes of interest. Both runs should contain the same set of samples. You see, however, that the names of the samples are different in the two runs. This means that we have to adjust sample annotation to match the names of the samples of the second run to the names of the samples of the first run.

Copying annotation

Fortunately, qbase+ allows to copy and paste run annotation in a very simple manner using Apply run layout.

Copy the names of the samples from the first run and paste them in the second run ?

Right click Run10 in the Project explorer select Apply run layout This opens the Source Run window where you can specify the run you want to copy the annotation from. Go to the current project: TrainingMarch Go to the current experiment: CopyAnnotation Select Run9 as the run to copy annotation from Click Next This opens the Copy properties window where you can specify which annotation you want to copy. We only want to copy sample annotation (the two runs measure different genes so we don't want to copy target annotation) so we select Sample: Click Finish. Now both runs contain the same sample annotation:

Nice ! However, qbase+ does not automatically clean up the samples list: the old sample names of the second run are still in the samples list.

Clean up the samples list.
Expand Samples in the Project explorer to view the samples list: the old names A1 to H12 are indeed still in the samples list Select the names of all the samples that you want to remove using the Shift key (up pointing arrow: ) on your keyboard Right click the selection Select Delete Confirm that you really want to delete the samples

We are going to add a custom property from this sample properties file to make a distinction between treated and control samples in later analysis steps.

Add the property.

To do this we go back to the Analysis wizard by clicking the Launch wizard button. Since the experiment already exists, choose to Analyse an existing qbase+ experiment. Click Next. Data has already been loaded so click Next again. In the Sample properties window select to Add samples and targets (red) and click Import samples (green). Browse to the sample properties file and select it Indicate that you are downloading Custom properties Click Finish You can find the details on how to add a custom property from a sample properties file in Creating a sample properties file

Analyzing the data

Choose the type of analysis you want to perform.

Check controls and replicates.

First set the minimum requirements for controls and replicates. You see that 14 replicates do not meet these requirements (red). Select to Show details and manually exclude bad replicates All negative controls pass the test (green) but none of the positive controls pass the test (blue). Select to Show details for positive and negative controls (purple). Qbase+ will now open the results for the failing replicates: as you can see the difference in Cq values between these replicates is not that big. They fail to meet the requirement just slightly. Click the +/- controls tab (red) to open the results for the failing controls. Select to view the results of the Failing positives (green): as you can see you don't have data for the positive controls (blue) which is why they fail.

Click the Continue wizard button.

Which amplification efficiencies strategy are you going to use ?
You don't have data of serial dilutions of representative template to build standard curves so the only choice you have is to use the default amplification efficiency (E = 2) for all the genes.

Appoint the reference genes as reference targets.
Appoint refgene1 and refgene2 as reference targets. As you can see the M- and CV values are shown in green meaning that the reference genes are stable in these samples: You can find the details on how to appoint reference targets in the Normalization section of Analyzing gene expression data in qbase+

Which scaling strategy are you going to use ?
Since you have a treated and a control group, it seems logical to use the average of the control group for scaling. You can find the details on how to specify the scaling strategy in the Scaling section of Analyzing gene expression data in qbase+

Look at the target bar charts.

In the target bar charts group the samples according to treatment.
You can find the details on how to group the samples in the Visualization of the results section of Analyzing gene expression data in qbase+

The samples of each group are biological replicates so you might want to generate a plot that compares the average NRQ of the treated samples with the average NRQ of the untreated samples.

In the target bar charts plot the group averages instead of the individual samples.
You can find the details on how to group the samples in the Analyzing the data section of Second exercise on analyzing gene expression data in qbase+

Are there any genes for which you see a clear difference in expression between the two groups ?

For gene 1, the mean expression levels in the two groups are almost the same and the error bars overlap. When you look at the title of the Y-axis, you see that 95% confidence levels are used as error bars. In case of 95% confidence intervals you can use the following rules: if they do not overlap: you are certain that the difference between the means of the two groups is significant if they do not overlap: you know nothing with certainty: the means can be different or they can be the same So for gene 1 the means are very close but just based on the plot we may not make any conclusions with certainty. For gene 2, the mean expression levels in the two groups are very different and the error bars do not overlap. So the 95% confidence intervals do not overlap meaning that we can be certain that the difference between the means of the two groups is significant. Error creating thumbnail: Unable to save thumbnail to destination Note that the average expression level of the control group is indeed set to 1 by our scaling strategy

Use a statistical test to compare the expression levels between the two groups of samples ?

You have 10 replicates per group meaning that you can check wether the data is drawn from a normal distribution. To this end export the data to a csv file that can be imported in GraphPad Prism. To export click the up-pointing arrow (red) in the top menu: This opens the Export window where you need to indicate the data to you wish to export. In our case we want to export normalized expression levels (= CNRQs Calibrated Normalized Relative Quantities) Since qbase+ automatically log10 transforms the data before doing statistics we want to check if the log10 transformed expression levels (red) are coming from a normal distribution. Additionally tell qbase+ where to store the file with the exported data (green). Export to an Excel file. However, on the BITS laptops there's is no excel installed which prevents Prism from loading excel files. Therefore download the corresponding csv file here. Prism will not have any trouble loading this. Open Prism Expand File in the top menu Select New Click New Project File In the left menu select to create a Column table. Data representing different groups (in our case measurements for different genes) should always be loaded into a column table. Select Enter replicate values, stacked into columns (this is normally the default selection) since the replicates (measurements for the same gene) are stacked in the columns. Click Create Prism has now created a table to hold the data of the control samples but at this point the table is still empty. To load the data: Expand File in the top menu Select Import Browse to the resultslog.csv file, select it and click Open In the Source tab select Insert data only Since this is a European csv file commas are used as decimal separators so in contrast to what its name might imply, semicolons and not commas are used to separate the columns in the csv file (you can open the file in a text editor to take a look). In American csv files dots are used as decimal separator and the comma is used to separate the columns. Prism doesn't know the format of your csv file so you have to tell him the role of the comma in your file. Select Separate decimals In the View tab you can see what Prism makes of the data Go to the Filter tab and specify the rows you want to import. The csv file contains the results for all samples and we need to check the two groups (treated and control) separately. The last rows are these of the treated samples, you don't want to include them now. Click Import 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 Now we can check if the data come from a normal distribution: Click the Analyze button in the top menu Select to do the Column statistics analysis in the Column analyses section of the left menu Click OK In the Descriptive statistics and the Confidence intervals section deselect everything except Mean, SD, SEM. These statistics is not what we are interested in: we want to know if the data comes from a normal distribution. The only reason we select Mean, SD, SEM is because if we make no selection here Prism throws an error. In the Test if the values come from a Gaussian distribution section select the D'agostino-Pearson omnibus test to test if the data are drawn from a normal distribution. Although Prism offers three tests for this, the D'Agostino-Pearson test is the safest option. Click OK Prism now generates a table to hold the results of the statistical analysis: As you can see, all control data sets are normally distributed. Now do the same for the data of the treated data sets. Expand File in the top menu Select New Click New Data Table and Graph Create a new Column table Import the data of the treated samples Clear the header row and delete the first column Perform the column analysis Again all data come from a normal distribution, which means that we may tell qbase+ that our data is normally distributed and perform a less stringent t-test. Statistical analyses can be performed via the Statistics wizard. Open the Statistics wizard: and proceed by explaining the details of your analysis The p-value of gene2 is smaller than 0.05 so it has a statistically significant difference in expression levels in treated samples compared to comtrol samples. For gene1 the p-value is larger than 0.05 so we have no evidence to conclude that the expression of gene1 is different in treated compared to control samples.