diff_analysis with multiple groups
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- r
- Domain
- bioinformatics
Research direction
Start by reading the diff_analysis entry point and its handling of the lda, kruskal.test, and wilcox.test options. Determine why four-group and A/B-only comparisons produce different discriminative features, and clarify whether separate pairwise comparisons are expected. Done means documenting the behavior and recommended comparison approach.
Written by the indexing model from the issue text.
Description
Hi,
I was hoping to get a bit more understand of the diff_analysis, if possible. I am struggling to understand why I get different taxa as significant from the diff_analysis function if I compare my 4 groups vs if I subset the data and compare 2 of the groups.
I have a class group with 4 groups - A, B, C, D. When I use diff_analysis I get only 3 discriminative features after lda.
If I subset the class group to only have options A, B - when I use diff_analysis I get 78 features discriminative features after lda.
The code used was:
set.seed(50)
deres <- diff_analysis(obj = ps_sub_ran_for_tree, classgroup = "cluster",
mlfun = "lda",
filtermod = "pvalue",
firstcomfun = "kruskal.test",
firstalpha = 0.05,
strictmod = TRUE,
secondcomfun = "wilcox.test",
subclmin = 10,
subclwilc = TRUE,
secondalpha = 0.05,
lda=2)
The results when comparing just A and B match a lot of the ones found using random forest, while comparing A,B,C,D does not. Should I be doing each pairwise comparison separately?
Thank you for you help!
- Dominant language
- R
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- 195
- Forks
- 36
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