Can any one suggest the best way to Q-PCR data representation including the +-SE..?. In many cases the expression fold is represented as usual log 2 base 2^-ddct but error bar is plotted in case of control also ... In that case from where the deviation is represented in control also? because double derivative of Ct value makes the control 1 and the log conversion makes the control 0.... So when the data is normalized to control how the error bar comes in case of control?
Showing data that is normalized to a control AND showing the control (normalized to itself !!) with error bars is at best mixing different concepts of variation (what is not a sound idea) and at worst simply unneccesary and silly.
The information of the controls is used up in the calculation of the ddCt values of the other groups. It makes no sense to explicitely normalize the control group itself to itself (there is no information provided, so it is an absolutely pointless thing to do).
The information that is left over is:
"all other (ddCt-)values are refere to 0 as reference"
This means it makes perfect sense here (with ddCt) to draw the horizontal axis at y=0, and one immediately sees that positive values indicate an induction (relative to the controls) and negative values indicate a repression (relative to the controls). If data is normalized to a control, then there is no control group to be shown in the plot. Instead, the reference line should be drawn to indicate the (constant!) reference value. So there is actually no questions like "what is the error bar of the control group?" - it makes no sense.
This is different for dCt values. When dCt values are shown, then there is either NO reference line at all or the mean dCt of the control group can serve as the reference line. Either the horizontal axis as the reference line should then be placed at y=mean(dCt[controls]) or all the dCt values should be shifted by -mean(dCt[controls])) and the horizontal axis be plasec at y=0. This way, all the groups (including the controls) are represented on the plot, but the actual change in expression must be inferred implicitely by visually comparing the different dCt values - there is no information in the plot about the uncertainty with the estimates of these differences (i.e. ddCt values).
NB1: if dCt values are to be shown I'd prever to show all individual points rather than summary statistics like mean and SE. Only if there are really many groups or genes or a hell lot of individual values, so that such a plot would become "too busy" I would use a boxplot instead.
NB2: if ddCt values are shown (that are available only as means), then I'd prefer to show the confidence interval rather than the SE as a measure of uncertainty or precision of this estimate.
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