# How to interprete Maaslin output?

**URL:** <https://forum.biobakery.org/t/how-to-interprete-maaslin-output/2129>\
**Category:** MaAsLin\
**Created:** [May 22, 2021, 2:34pm UTC](https://forum.biobakery.org/t/how-to-interprete-maaslin-output/2129 "2021-05-22T14:34:19Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![DEEPCHANDA7](https://avatars.discourse-cdn.com/v4/letter/d/eb9ed0/32.png) [@DEEPCHANDA7](https://forum.biobakery.org/u/DEEPCHANDA7)\
**Post date:** [May 22, 2021, 2:34pm UTC](https://forum.biobakery.org/t/how-to-interprete-maaslin-output/2129/1 "2021-05-22T14:34:20Z")

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Hi- @sma @himel.mallick @Kelsey_Thompson I want to find differentially abundant bacteria associated with disease samples. My metadata is something like following:

| ID | Type | Age | Comorbidity | Gender |
| --- | --- | --- | --- | --- |
| SRP090252 | disease | 71.65 | T2D | Female |
| SRP090268 | control | 70.05 | T2D | Female |
| SRP090265 | control | 70.85 | NGT | Female |
| SRP090264 | control | 70.21 | IGT | Female |
| SRP090263 | control | 70.06 | NGT | male |
| SRP090258 | disease | 69.53 | NGT | Female |
| SRP090250 | disease | 70.02 | IGT | Female |
| SRP090244 | disease | 70.07 | IGT | male |
| SRP090242 | control | 69.78 | NGT | Female |
| SRP090227 | control | 69.74 | NGT | male |
| SRP090226 | control | 69.7 | NGT | Female |
| SRP090224 | disease | 69.67 | NGT | male |
| SRP090222 | disease | 71.4 | IGT | Female |
| SRP090217 | control | 71.53 | NGT | Female |

Now, I want to get the differentially abundant bacteria associated with `disease` samples after adjusting for confounders `Age`, `Comorbidity`, and `Gender`. In this context, how should I specify the `Random effect` and `Fixed effect` options?

As much as I got to know from other posts in this forum, I think I should provide `--fixed-effect "Type"` and `--random-effect "ID"`. Is this correct? Will this give me an output after adjusting for all the confounders I mentioned?  
Or, should I provide `--fixed effect "Type, Age, Comorbidity, Gender"`? and `--random effect "ID"?`

If none of the above correct, please suggest me what should I follow?

many Thanks,  
DC7

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<div class="post-metadata">

**Author:** ![himel.mallick](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/himel.mallick/32/1757_2.png) [@himel.mallick](https://forum.biobakery.org/u/himel.mallick)\
**Post date:** [May 22, 2021, 2:49pm UTC](https://forum.biobakery.org/t/how-to-interprete-maaslin-output/2129/2 "2021-05-22T14:49:03Z")

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Hi @DEEPCHANDA7 - it’s the latter one based on your description. If you do not have repeated `ID` (which seems to be the case in your example), you don’t need a random effect.

All the best,  
Himel

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<div class="post-metadata">

**Author:** ![DEEPCHANDA7](https://avatars.discourse-cdn.com/v4/letter/d/eb9ed0/32.png) [@DEEPCHANDA7](https://forum.biobakery.org/u/DEEPCHANDA7)\
**Post date:** [May 22, 2021, 7:12pm UTC](https://forum.biobakery.org/t/how-to-interprete-maaslin-output/2129/3 "2021-05-22T19:12:31Z")

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Thanks a lot, sir @himel.mallick for your fast response. Sir, I have two follow up queries-

1. 

> [@himel.mallick](#):
>
> (which seems to be the case in your example), you don’t need a random effect.

Yes, i don’t have a repeated `ID`. Do you mean that I should use ID as neither `random effect` nor `fixed effect`?

1. I am having hard time interpreting my output. Here’s my code, metadata, and output:

**Code:**  
library(Maaslin2)  
library(readr)  
setwd(“~/my\_output”)  
getwd()

```
input_data <- as.data.frame(read_tsv("merged_abundance_species_5.tsv"))
input_metadata <-as.data.frame(read_csv("metadata.csv"))
fit_data <- Maaslin2(
  input_data, input_metadata, 'my_output', transform = "NONE",
  fixed_effects = c('type', 'comorbidity', 'age'),
  reference = c('comorbidity,NGT'),
  normalization = 'NONE',
  min_abundance = 0.0,
  max_significance = 0.25,
  min_prevalence = 0.0,
  standardize = FALSE)

```

**Metadata:**

| ID | type | age | comorbidity |
| --- | --- | --- | --- |
| SR090136 | disease | 70.15 | IGT |
| SR090140 | disease | 70.15 | T2D |
| SR090147 | control | 71.39 | NGT |
| SR090150 | disease | 71.58 | IGT |
| SR090152 | disease | 71.24 | T2D |
| SR090154 | disease | 71.04 | IGT |
| SR090157 | disease | 70.96 | IGT |
| SR090161 | disease | 70.39 | T2D |
| SR090163 | disease | 70.14 | NGT |
| SR090167 | disease | 70.97 | T2D |
| SR090169 | disease | 68.96 | T2D |
| SR090170 | control | 71.11 | NGT |
| SR090171 | control | 70.55 | NGT |
| SR090173 | disease | 70.11 | T2D |
| SR090175 | disease | 71.02 | NGT |
| SR090177 | disease | 70.2 | IGT |
| SR090179 | disease | 70.16 | T2D |
| SR090193 | control | 70.25 | NGT |
| SR090205 | control | 70.63 | NGT |

**Output:**

`significant_results.tsv`

| feature | metadata | value | coef | stderr | N | N.not.0 | pval | qval |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Faecalitalea\_cylindroides | comorbidity | IGT | -0.377764650707978 | 0.043253216559351 | 19 | 5 | 4.86003286684536E-07 | 0.000209953419848 |
| Faecalitalea\_cylindroides | comorbidity | T2D | -0.368187348493346 | 0.041715679009967 | 19 | 5 | 4.28479422114103E-07 | 0.000209953419848 |
| Faecalitalea\_cylindroides | type | disease | 0.350656650707978 | 0.043253216559351 | 19 | 5 | 1.17270795116608E-06 | 0.000337739889936 |
| Collinsella\_stercoris | comorbidity | IGT | -0.109236524922356 | 0.025087649856961 | 19 | 18 | 0.00066062321169 | 0.142694613724944 |
| Roseburia\_inulinivorans | age | age | -1.58382545890012 | 0.385112459302547 | 19 | 18 | 0.001055772797088 | 0.15082184993556 |
| Ruminococcus\_bromii | type | disease | 18.7382134402279 | 4.63755246673051 | 19 | 11 | 0.001215556053757 | 0.15082184993556 |
| Ruminococcus\_bromii | comorbidity | IGT | -18.7258134402279 | 4.63755246673051 | 19 | 11 | 0.0012219362842 | 0.15082184993556 |
| Olsenella\_scatoligenes | comorbidity | IGT | -0.014838219463708 | 0.003842430774736 | 19 | 8 | 0.001727334716805 | 0.178134499426023 |
| Oscillibacter\_sp\_57\_20 | age | age | -0.310432279358586 | 0.081151146011419 | 19 | 11 | 0.001855567702354 | 0.178134499426023 |
| Bifidobacterium\_bifidum | type | disease | 3.61449351479 | 1.13571553919717 | 19 | 8 | 0.006646997243247 | 0.205410860127932 |
| Olsenella\_scatoligenes | type | disease | 0.012930219463708 | 0.003842430774737 | 19 | 8 | 0.004622838288644 | 0.205410860127932 |
| Olsenella\_scatoligenes | comorbidity | T2D | -0.01242486374167 | 0.003705842514556 | 19 | 8 | 0.004737715789044 | 0.205410860127932 |
| Collinsella\_intestinalis | comorbidity | IGT | -0.020799960994458 | 0.006581295428193 | 19 | 16 | 0.006945621531625 | 0.205410860127932 |
| Collinsella\_stercoris | type | disease | 0.080588524922356 | 0.025087649856961 | 19 | 18 | 0.006265644670361 | 0.205410860127932 |
| Collinsella\_stercoris | comorbidity | T2D | -0.079481101360263 | 0.024195850200212 | 19 | 18 | 0.005422718327775 | 0.205410860127932 |
| Alistipes\_onderdonkii | comorbidity | T2D | -0.096692174486469 | 0.030877949038327 | 19 | 3 | 0.007358245481338 | 0.205410860127932 |
| Clostridium\_sp\_CAG\_411 | type | disease | 0.015964789965694 | 0.005060519673996 | 19 | 1 | 0.007024676313767 | 0.205410860127932 |
| Clostridium\_sp\_CAG\_411 | comorbidity | IGT | -0.015964789965694 | 0.005060519673996 | 19 | 1 | 0.007024676313768 | 0.205410860127932 |
| Clostridium\_sp\_CAG\_411 | comorbidity | T2D | -0.017198735694422 | 0.004880631572321 | 19 | 1 | 0.003371414639798 | 0.205410860127932 |
| Lawsonibacter\_asaccharolyticus | type | disease | 0.477862895042796 | 0.151766856842129 | 19 | 18 | 0.007110491107549 | 0.205410860127932 |
| Lawsonibacter\_asaccharolyticus | comorbidity | IGT | -0.475124895042795 | 0.151766856842129 | 19 | 18 | 0.007370065583294 | 0.205410860127932 |
| Eubacterium\_eligens | age | age | -1.2467673088303 | 0.391416017038458 | 19 | 17 | 0.006611343727095 | 0.205410860127932 |
| Oscillibacter\_sp\_CAG\_241 | comorbidity | IGT | -2.64857972770573 | 0.763998086959495 | 19 | 17 | 0.003776853512783 | 0.205410860127932 |
| Oscillibacter\_sp\_CAG\_241 | comorbidity | T2D | -2.61370728597232 | 0.736839973880289 | 19 | 17 | 0.003218875403587 | 0.205410860127932 |
| Ruminococcaceae\_bacterium\_D16 | comorbidity | T2D | -0.106504807911724 | 0.031556086714996 | 19 | 4 | 0.004531946745981 | 0.205410860127932 |
| Ruminococcaceae\_bacterium\_D5 | comorbidity | IGT | -0.158426557912691 | 0.050505479793602 | 19 | 4 | 0.007279877600031 | 0.205410860127932 |
| Ruminococcaceae\_bacterium\_D5 | comorbidity | T2D | -0.170497579432457 | 0.048710143450794 | 19 | 4 | 0.003533466593531 | 0.205410860127932 |
| Ruminococcus\_bromii | comorbidity | T2D | -14.530958484276 | 4.4726997315575 | 19 | 11 | 0.005826462285798 | 0.205410860127932 |
| Firmicutes\_bacterium\_CAG\_534 | type | disease | 0.001257422965504 | 0.000374853309185 | 19 | 1 | 0.004722065633165 | 0.205410860127932 |
| Firmicutes\_bacterium\_CAG\_534 | comorbidity | IGT | -0.001257422965504 | 0.000374853309185 | 19 | 1 | 0.004722065633165 | 0.205410860127932 |
| Firmicutes\_bacterium\_CAG\_534 | comorbidity | T2D | -0.001166019578191 | 0.000361528264616 | 19 | 1 | 0.006106036469878 | 0.205410860127932 |
| Firmicutes\_bacterium\_CAG\_95 | age | age | -0.058657135094159 | 0.018943043595487 | 19 | 7 | 0.007887029446352 | 0.206496770959043 |
| Phascolarctobacterium\_succinatutens | age | age | -0.282982879719054 | 0.091233077220782 | 19 | 2 | 0.00780509739456 | 0.206496770959043 |
| Bifidobacterium\_bifidum | comorbidity | IGT | -3.49699551479 | 1.13571553919717 | 19 | 8 | 0.008164122157298 | 0.207464751291347 |
| Bacteroides\_ovatus | age | age | -0.790359006180556 | 0.265922716314961 | 19 | 16 | 0.010093623789579 | 0.249168312977036 |

Here, I just want to get the bacteria that are significantly associated with the `control` and `disease` group after adjusted for confounders. What are those? Are they all in this `significant_results.tsv` output? Or, only those with denoted with `disease` under the **value** column?

thanks,  
DC7

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<div class="post-metadata">

**Author:** ![himel.mallick](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/himel.mallick/32/1757_2.png) [@himel.mallick](https://forum.biobakery.org/u/himel.mallick)\
**Post date:** [May 23, 2021, 7:09am UTC](https://forum.biobakery.org/t/how-to-interprete-maaslin-output/2129/4 "2021-05-23T07:09:33Z")

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Hi @DEEPCHANDA7 - that’s correct. you don’t need `ID` in the model. You will be looking at the `metadata` column to filter this table by the main variable of interest (i.e. `type`).

Since your metadata variable (`type`) is binary, MaAsLin 2 internally models this as a dummy variable with `type = control` as the reference level (as a rule of thumb, the reference level usually does not appear in the `value` column).

In your case, the results indicate that the coefficients (and their signs) should be interpreted with `type = control` as the reference e.g. feature X is more abundant in `type = disease` as compared to `type = control` (if the corresponding coefficient is positive) and vice versa. I hope it makes sense.

For more information on how to interpret binary categorical variables in a regression setting, feel free to check out the tutorial [here](http://www.sthda.com/english/articles/40-regression-analysis/163-regression-with-categorical-variables-dummy-coding-essentials-in-r#categorical-variables-with-two-levels). Also the visualization plots should provide sufficient information to connect the dots.

All the best,  
Himel