# Q-values in Maaslin2

**URL:** <https://forum.biobakery.org/t/q-values-in-maaslin2/4558>\
**Category:** MaAsLin\
**Created:** [December 21, 2022, 7:42pm UTC](https://forum.biobakery.org/t/q-values-in-maaslin2/4558 "2022-12-21T19:42:34Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![svd](https://avatars.discourse-cdn.com/v4/letter/s/3ab097/32.png) [@svd](https://forum.biobakery.org/u/svd)\
**Post date:** [December 21, 2022, 7:42pm UTC](https://forum.biobakery.org/t/q-values-in-maaslin2/4558/1 "2022-12-21T19:42:34Z")

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looking at the results file of my data analysis (GO terms molecular functions in copies-per-million [880 functions], 8 control individuals and 8 exposed ones, exposure is the only variable), I am not sure if p.adjust function performs as expected:

1. for correction = “fdr” I get an error - “ERROR::Please select a correction method from the list of available options : BH, holm, hochberg, hommel, bonferroni, BY  
Error: Option not valid”;
2. when I use correction = “BH” or do not use correction argument (default is BH), all q - values are 0.73 regardless of p-values that range from 0.004 to 0.999;
3. when I use correction = “BY” option, all q-values = 1.

Is this a possible outcome of the p-value correction or something is wrong?

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**Author:** ![andrewGhazi](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/andrewghazi/32/2950_2.png) [@andrewGhazi](https://forum.biobakery.org/u/andrewGhazi)\
**Post date:** [January 3, 2023, 9:08pm UTC](https://forum.biobakery.org/t/q-values-in-maaslin2/4558/2 "2023-01-03T21:08:20Z")

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1. Maaslin2 does a bit of pattern matching to allow flexible capitalization of the correction argument, but “fdr” doesn’t resolve to one of the allowed choices like it does with `p.adjust()`. “fdr” resolves to “BH” inside of `p.adjust()` will give you the same thing as what you originally intended.

2. Depending on your distribution of p-values, it is possible to have the adjusted q-values collapse to a smaller number of unique values. You can see this independently via a one-line simulation:

```auto
> runif(1000) |> p.adjust(method = "BH") |> unique() |> length()
[1] 35

```

This is a natural consequence of how BH q-values are calculated, the complexity of which is often underappreciated, so I’ll point out the source from `p.adjust()` here:

```auto
lp <- length(p)
i <- lp:1L
o <- order(p, decreasing = TRUE)
ro <- order(o)
pmin(1, cummin(n/i * p[o]))[ro]

```

Specifically that rolling cumulative minimum can output the same value as it marches along the sorted p-value vector.

1. A similar sort of situation as 2. You can see the source for BY adjustment by entering `p.adjust` (no parentheses) in the R console.