To be clear - raw RNA-seq data (or more specifically, the counts that may be derived from it using software such as HT-seq or Subread featureCount) is NOT absolute quantification. Should I position the stars on all the time points as indicated by the two-way ANOVA results? Or should I position them on the significant weeks only based on further one-way ANOVA analysis for the results of each week separately? They didn’t indicate at which time point (week) the results were significant and at which week they were not. Both the Statgraphics and the Graphpad prism have indicated the same results.
they indicated for example that the +ve control is significant from normal, or the treatment no 1 is significant from the +ve control. In the two-way repeated measures model, both programs have indicated the overall groups' significance i.e. Now, I hope if you would like to guide me to the right way to graphically represent the significance on the two- way analysis charts, as I know the two-way ANOVA should be graphically illustrated using the line chart type, but where should I position the significance marks (stars)? We have utilized Statgraphics Centurion 16.1.11, and the GraphPad prism version 6 software for the statistical evaluation. It involves comparing the effects of 5 to 7 treatments, normal control, and +ve control on weekly measured parameters over 6 weeks experimental period. I am working on experiment which requires me to apply the repeated measures two-way ANOVA analysis model. Is this therefore the incorrect test and which test should I then use? Or if this is correct, why should the bar for steroid A be effecting the statistical analysis of my remaining steroids? This seems incorrect to me as this means there is dependence between each response and each comparison to the control should in actual fact be independent to the remaining responses. When I include steroid A on the graph nothing is significantly different to my basal reading besides steroid A but as soon as Steroid A is removed everything else becomes significant. I have selected a One way ANOVA with a multiple comparison's test (comparing each column to my control, basal) with a Tukey's posthoc test (I have also tried the Fischer's LSD as to not correct for multiple comparison's). I would like to graph all my data on the same axis and compare whether each steroid response is significantly enhanced compared to my basal response. One steroid response is 10x greater than all the other steroid responses (let's call this steroid, steroid A). My experiment consists of a number of steroids I have assayed with a receptor, each independent of the other.
Based on previously published data a one way ANOVA would be best for what I am doing. I am assaying steroid hormones with a receptor and am struggling to choose the correct statistical test to determine significance.
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