So If I use the number of root tips of each plant as covariate in a model, and if it does not have significant effect in the model (P>0.5), can I draw the conclusion that the number of root tips of each plant does not affect sporangia production?
Generally speaking, you cannot draw that conclusion, especially with only 3 replications. Failure to detect significance of a factor does not mean that the factor has no effect, it just means that the study provides no evidence of an effect.
For the test above (set the number of root tips of each plant as a covariate), I think I have to use the total number of sporangia of each plant, if the test result is not significant, can I jump to analyzing the data using number of sporangia on each root tip?
I don't know what you mean here. Hopefully you are not thinking to use each root tip as an independent replication.
Also, I was wondering how can I test if there is a significant difference of sporangia production by the same isolate across different runs?
I refer to my response last Monday, where I said
If plants are replicates, then you can't assess whether different plants have an effect on the mean of the dependent variable. If you are specifically interested in these 6 plants (as opposed to thinking of them as representing "plants in general"), then it is possible to obtain a prediction (a BLUP) for each plant. I doubt that you are interested in these specific plants, but maybe you are.
I still doubt that the 6 plants that you used in this experiment are "special" in any way, and so I doubt that attempting to compare one plant to another is appropriate. If you disagree, describe why. Address why and how you chose these six plants to use in your experiment.
Basically, I see two analysis paths.
(1) Analyze the effect of isolate on total sporangia production per plant. The dataset would have six observations (one for each plant), and the statistical model would be a one-way in a completely randomized design where plant is the experimental unit to which the fixed effects factor isolate is randomly assigned.
(2) Analyze the effect of isolate on sporangia production per root tip. One way to do this is to use the dataset with six observations; the response variable is total number of sporangia, assumed to follow a Poisson (or negative binomial) distribution with a log link; isolate is a fixed effects factor, and log(number of root tips) is an offset (not a covariate; an offset and a covariate are not the same thing). The statistical model is still a one-way in a completely randomized design; adding the offset results in an analysis of the effect of isolate on number of sporangia per root tip, rather than total sporangia production per plant.
You want to avoid deciding what metric to analyze based on what you see in the data. Think about why you ran this experiment, and what biological mechanism you intended to examine. If you had not yet run the experiment, what do you think would be the best response metric (total sporangia, or number of sporangia per root tip)? Your best discussion partners for this topic will be your colleagues, not me because I do not know the biological details of your system.
As a disclaimer, the opinions I've expressed here are based on my best guess of your research objectives, based on my past experience with other researchers. I may have a correct vision, or I may be totally off-track. You get to decide.
HTH
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