# No associations/results from large dataset; multiple warnings produced **URL:** \ **Category:** MaAsLin\ **Created:** [February 23, 2022, 8:45pm UTC](https://forum.biobakery.org/t/no-associations-results-from-large-dataset-multiple-warnings-produced/3210 "2022-02-23T20:45:55Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![mmanus](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/mmanus/32/1266_2.png) [@mmanus](https://forum.biobakery.org/u/mmanus)\ **Post date:** [February 23, 2022, 8:45pm UTC](https://forum.biobakery.org/t/no-associations-results-from-large-dataset-multiple-warnings-produced/3210/1 "2022-02-23T20:45:56Z")
Hi there, I have successfully run MaAsLin on a dataset of 408 human microbiome samples using the attached metadata file and feature table. Currently I’m looking at just 1 variable in the metadata file (“source”) and I have set a reference for this variable. As you can see below, the code works but then I receive a message that there are no associations to plot. I find this hard to believe, and wonder if the problem stems from the warnings that I receive (see below). Any advice? I’m not sure if there is something going on with my dataset, or perhaps a different parameter to tweak in the code? Many thanks, Melissa taxa = read.delim(“maaslin\_feature\_table2\_trimzero\_a.txt”, sep = “\t”, row.names = 1) taxa = data.frame(t(taxa), check.names = F) meta = read.delim(“maaslin\_metadata\_merged\_T2a\_source.txt”, sep = “\t”, row.names = 1) meta = data.frame(t(meta), check.names = F) fit\_data = Maaslin2(taxa, meta, “my\_output”, transform = “AST”, fixed\_effects = c(‘source’), reference = c(‘source,infant’), normalization = “NONE”, standardize = F, min\_prevalence = 0.1, min\_abundance = 0.001) #It seems to run alright, but then I receive the message: [1] “There are no associations to plot!” There were 50 or more warnings (use warnings() to see the first 50) #If I check on the warnings, I see this: > warnings() > Warning messages: > 1: In asin(sqrt(abs(x))) : NaNs produced > 2: In asin(sqrt(abs(x))) : NaNs produced > 3: In asin(sqrt(abs(x))) : NaNs produced > 4: In asin(sqrt(abs(x))) : NaNs produced > 5: In asin(sqrt(abs(x))) : NaNs produced > 6: In asin(sqrt(abs(x))) : NaNs produced > 7: In asin(sqrt(abs(x))) : NaNs produced > 8: In asin(sqrt(abs(x))) : NaNs produced > 9: In asin(sqrt(abs(x))) : NaNs produced > 10: In asin(sqrt(abs(x))) : NaNs produced > 11: In asin(sqrt(abs(x))) : NaNs produced > 12: In asin(sqrt(abs(x))) : NaNs produced > 13: In asin(sqrt(abs(x))) : NaNs produced > 14: In asin(sqrt(abs(x))) : NaNs produced > 15: In asin(sqrt(abs(x))) : NaNs produced > 16: In asin(sqrt(abs(x))) : NaNs produced > 17: In asin(sqrt(abs(x))) : NaNs produced > 18: In asin(sqrt(abs(x))) : NaNs produced > 19: In asin(sqrt(abs(x))) : NaNs produced > 20: In asin(sqrt(abs(x))) : NaNs produced > 21: In asin(sqrt(abs(x))) : NaNs produced > 22: In asin(sqrt(abs(x))) : NaNs produced > 23: In asin(sqrt(abs(x))) : NaNs produced > 24: In asin(sqrt(abs(x))) : NaNs produced > 25: In asin(sqrt(abs(x))) : NaNs produced > 26: In asin(sqrt(abs(x))) : NaNs produced > 27: In asin(sqrt(abs(x))) : NaNs produced > 28: In asin(sqrt(abs(x))) : NaNs produced > 29: In asin(sqrt(abs(x))) : NaNs produced > 30: In asin(sqrt(abs(x))) : NaNs produced > 31: In asin(sqrt(abs(x))) : NaNs produced > 32: In asin(sqrt(abs(x))) : NaNs produced > 33: In asin(sqrt(abs(x))) : NaNs produced > 34: In asin(sqrt(abs(x))) : NaNs produced > 35: In asin(sqrt(abs(x))) : NaNs produced > 36: In asin(sqrt(abs(x))) : NaNs produced > 37: In asin(sqrt(abs(x))) : NaNs produced > 38: In asin(sqrt(abs(x))) : NaNs produced > 39: In asin(sqrt(abs(x))) : NaNs produced > 40: In asin(sqrt(abs(x))) : NaNs produced > 41: In asin(sqrt(abs(x))) : NaNs produced > 42: In asin(sqrt(abs(x))) : NaNs produced > 43: In asin(sqrt(abs(x))) : NaNs produced > 44: In asin(sqrt(abs(x))) : NaNs produced > 45: In asin(sqrt(abs(x))) : NaNs produced > 46: In asin(sqrt(abs(x))) : NaNs produced > 47: In asin(sqrt(abs(x))) : NaNs produced > 48: In asin(sqrt(abs(x))) : NaNs produced > 49: In asin(sqrt(abs(x))) : NaNs produced > 50: In asin(sqrt(abs(x))) : NaNs produced > [maaslin\_metadata\_merged\_T2a\_source.txt](https://forum.biobakery.org/uploads/short-url/9kyYHYetj2RXA4J2ztoUgpfonYl.txt) (6.7 KB) > [maaslin\_feature\_table2\_trimzero\_a.txt](https://forum.biobakery.org/uploads/short-url/A8AY1j6wclhetGSTfchqet7eJ67.txt) (55.1 KB) ---
**Author:** ![Kelsey\_Thompson](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/kelsey_thompson/32/65_2.png) [@Kelsey\_Thompson](https://forum.biobakery.org/u/Kelsey_Thompson)\ **Post date:** [February 23, 2022, 10:11pm UTC](https://forum.biobakery.org/t/no-associations-results-from-large-dataset-multiple-warnings-produced/3210/2 "2022-02-23T22:11:39Z")
Hi @mmanus, Thanks for the question. I believe what is happening here is that for the function `asin(sqrt(abs(x)))` the underlying data needs to be between 0 and 1 (well really -1 and 1) but proportional data is required for this to work. It looks like your current feature table is count based data. To get this to run without the warning and to get significant results you can simply get rid of the `normalization = "NONE"` flag. Alternatively, you could choose a transformation method that works with count data see the [tutorial](https://github.com/biobakery/biobakery/wiki/maaslin2#32-running-maaslin-2) for more information. I hope this helps! Best, Kelsey ---
**Author:** ![mmanus](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/mmanus/32/1266_2.png) [@mmanus](https://forum.biobakery.org/u/mmanus)\ **Post date:** [February 28, 2022, 3:50pm UTC](https://forum.biobakery.org/t/no-associations-results-from-large-dataset-multiple-warnings-produced/3210/3 "2022-02-28T15:50:55Z")
Thank you, @Kelsey_Thompson this is very helpful! I’ve tried running it again with different combinations of transformation and normalization methods. It seems to work, except I’m not sure if the resulting plots should show transformed data on the y-axis. I had assumed so, but maybe I’m wrong about that if the input is still count data? For example, this code yields box plots with a y-axis that still seems like count data (ranging from 0 - 10,000 on some plots): fit\_data = Maaslin2(taxa, meta, “my\_output\_infants2”, analysis\_method = “LM”, transform = “AST”, fixed\_effects = c(‘bodysite’), reference = c(‘bodysite,AP’), standardize = F, normalization = “TSS”, min\_prevalence = 0.1, min\_abundance = 0.001) Thanks! ---
**Author:** ![Kelsey\_Thompson](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.biobakery.org/kelsey_thompson/32/65_2.png) [@Kelsey\_Thompson](https://forum.biobakery.org/u/Kelsey_Thompson)\ **Post date:** [February 28, 2022, 4:17pm UTC](https://forum.biobakery.org/t/no-associations-results-from-large-dataset-multiple-warnings-produced/3210/4 "2022-02-28T16:17:30Z")
Hi @mmanus, MaAsLin plots the raw data, not the transformed data. For the next iterations of MaAsLin, we have been discussing ways to improve this functionality. However, for now, it is the raw count data you supplied. It should be relatively easy to replicate the plots in ggplot2 after normalizing using something like phyloseq or pulling the codes out of MaAsLin’s code to transform/normalize the data. I hope this helps! Best, Kelsey