By fitting the full model
~FAB + Sex + Age + TMB + WBC + BM_percentage you can estimate the effect of FAB (or any of the other variables) on RNA expression after adjusting for differences caused by Sex, Age, TMB, WBC, and BM percent. See this post for exactly what it means to adjust for other variables
When you fit a reduced model (in your case
~Sex + Age) and then conduct a likelihood ratio test (LRT), you are comparing the fit of the full model (
~FAB + Sex + Age + TMB + WBC + BM_percentage) to the fit of the reduced model (
~Sex + Age). If the fit is substantially better, the likelihood of the full model will be larger than the reduced model and the p-value will be significant. This means that adding the extra variables that are in the full model (FAB, TMB, WBC, BM_percentage) improved the fit of the model to the data because together these variables have some type of relationship with RNA expression even after adjusting for the variables in the reduced model.
The challenge with using a LRT is that it doesn't tell you which of the variables added in the full model are responsible for the improved fit - it just indicates that the full model is better. This type of test is useful when you are trying to make broad statements about several variables having a relationship with RNA expression. From the DESeq2 vignette:
The LRT is therefore useful for testing multiple terms at once, for example testing 3 or more levels of a factor at once, or all interactions between two variables. The LRT for count data is conceptually similar to an analysis of variance (ANOVA) calculation in linear regression, except that in the case of the Negative Binomial GLM, we use an analysis of deviance (ANODEV), where the deviance captures the difference in likelihood between a full and a reduced model.
Definitely read the DESeq2 vignette for more details. In your case if the LR test is significant, it means that FAB, TMB, WBC, and BM percent combined are associated with RNA expression.
Most people are interested in specific comparisons when performing differential expression analyses with DESeq2. These comparisons are usually like do males have higher gene expression than females (comparing sex differences), do patients with higher TMB have greater expression than patients with lower TMB (comparing effect of TMB on RNA expression). If you want to do a specific comparison, you will likely want to use the Wald test, which calculates p-values for individual coefficients (male vs female coefficient, TMB coefficient etc). The Wald test determines whether a coefficient is equal to 0 or some other null hypothesis (see
The Wald statistic and accompanying p-values are reported by default when using
results() and not specifying
There are some additional differences between Wald tests vs LRT, including accuracy differences when operating on small sample sizes and computational efficiency issues when operating with large sample sizes. Discussion on Wald vs LRT
TL;DR Use LRT to test if several variables or factor levels are associated with RNA expression. Use the Wald test to determine if specific comparisons are significant.
rpkm_orderedtelling about this i suppose its just dataframe i named in my code which i didn't change.. $\endgroup$
rpkm_orderedconfusion since once i had to reorder my dataframe...which I did quite long time back so its just in my code.. $\endgroup$