I am working on the obesity research. I found a molecule A that is negatively control the generation of adipocytes. I have a cohort of patients, and collected their adipose tissue samples (n=29). I do found that A is negatively correlated with adipogenesis in lean patients (n=22). However, A is strongly correlated with obesity, and in obesity people (n=7), both adipogenesis and A is increased. I can not tell the effect of A on adipogenesis in those patients.

I tried multiple lineage regression on total samples, but it worked badly. I am going to try partially least square, do you think it is a appropriate way? Whether there is a better statistic model can deal with this situation?

Thanks a lot for your time. I appreciate any suggestions and comments. ]]>

I have a problem with evaluating how I should correct my alpha and conduct statistical texts when I have several different exeriments in the same project, each with more than two groups to compare.

- First off, I have a series of measurements that have a control group and three seperate experimental groups that are independent.

- I have a completely different experiment, with a series of measurements on the same four groups.

The results of these two experiments are independent of each other.

So right now, I set up a Holm-Bonferroni process with alpha corrected for all the p-values that were calculated for all the experiments. But I'm not sure if this is actually correct, or if I can get away with doing a seperate correction for either experiment (less stringent corrections).

Also, right now one-way ANOVAS with Dunett's test were applied to every measurement. However, this also corrects the p-values within that measurement, so in essence I am now correcting my p-values twice, once within the seperate anovas, and once more when I correct my alpha with the H-B process. So if I do the H-B is it than okay to do simple T-tests for every measurement instead of the anovas so the results aren't corrected twice?

Thank you! ]]>

Observed: -0.158 Expected: -0.0303 SD: 0.058844 Probability: 0.030002

On this basis is it correct to state that there is negative spatial autocorrelation significant at the 95% confidence?

Am I correct in that an observed value close to -1 would indicate perfect negative autocorrelation and as such the negative autocorrelation seen here is relatively low? ]]>