I'm not sure whether I have to use parametric or non-parametric tests.

Question

[rahulgenexpress]

I have obtained NGS data from stool samples of disease subjects and healthy controls, performed QIIME analysis. To compare gut microbiota composition and to know significant difference, which statistical tests I should perform?

[Nicolas Rosewick]

You should ask your question to http://stats.stackexchange.com/ as it's not really a bioinfo question

Answer 1

[EagleEye]

If your data follows normal distribution, use Parametric otherwise non-paramertic.

More information,

http://blog.minitab.com/blog/adventures-in-statistics/choosing-between-a-nonparametric-test-and-a-parametric-test

Finding the distribution,

http://www.originlab.com/doc/Origin-Help/Normality-Test

Perform normality test using R,

http://m.dummies.com/how-to/content/how-to-test-data-normality-in-a-formal-way-in-r.html

Answer 2

[agata88]

It depends how many samples you have for analysis. Sometimes if the number of samples is small the distribution of this group is not normal. I had the same problem with my analysis. One group was normally distributed and second was not. I wanted to find statistical difference between groups. In that case I used two test: student test and Mann-Whitney test. Student test for normally distributed data, and Mann-Whitney test for not normally distributed data. I am not 100% sure that is correct solution but as far as I know it is not necessary to do a normalization of not normally distributed group, since there is small amount of samples (the biggest amount of data the more probably that group have normal distribution). But in case you have a large number of samples and still your data is NOT normally distributed I would go straight for non-parametric test.

What is your opinion about that?

I am also looking forward for some tips.

Best,

Agata

Answer 3

[rahulgenexpress]

Thank you very much Agata. As per your suggestion I checked my data, it has large number of samples and not normally distributed; I am going for non-parametric test.