Hello, I’m doing a maaslin3(version 1.0.0) analysis and my results show “All associations had errors or were insignificant”。 My code and data and result file
all_results (1).tsv (4.1 MB)
are as follows:
library(maaslin3)
packageVersion(“maaslin3”)
ph_TAN ← read.csv(“/data/work/QTP/maaslin3/TAN/Maaslin3_qtp_TA_255sample.csv”, head = TRUE, row.names = 1,check.names=FALSE)
SGB_TAN ← read.csv(“/data/work/QTP/maaslin3/TAN/qtp_TA_255sample_relabu.csv”, head = TRUE, row.names = 1,check.names=FALSE)
SGB_TAN ← SGB_TAN / 100
output_dir ← “/data/work/QTP/maaslin3/TAN/elevation_group/SGB”
input_TAN_data = as.data.frame(SGB_TAN)
input_TAN_metadata = as.data.frame(ph_TAN)
fit_out ← maaslin3(input_data = input_TAN_data,
input_metadata = input_TAN_metadata,
output = output_dir,
fixed_effects = c(“elevation_group”),
reference = c(“elevation_group,4-4.5KM”),
random_effects = c(“Latitude”, “Longitude”, “sampled_place”, “KP994558.1_s_Potentilla_acaulissta”, “KJ020646.1_s_Potentilla_parvifoliasta”, “MH854502.1_s_Fagopyrum_dibotryssta”, “HE577530.1_s_Chenopodium_hybridumsta”, “FJ640034.1_s_Leontopodium_pusillumsta”, “AB480625.1_s_Orostachys_fimbriatasta”, “KU750607.1_s_Senecio_scandenssta”),
normalization = “TSS”,
transform = “LOG”,
warn_prevalence = TRUE,
augment = TRUE,
standardize = TRUE,
max_significance = 0.1,
median_comparison_abundance = TRUE,
median_comparison_prevalence = FALSE,
max_pngs = 250,
cores = 10)
qtp_TA_255sample_relabu.csv (9.8 MB)
Maaslin3_qtp_TA_255sample.csv (29.7 KB)
Can you tell me what is the reason for this problem?
Best wishes
Hi,
I’m pretty sure the issue is in how you’re using the random effects. I would recommend only using 1 random effect if any. Looking at your data, I would recommend using elevation_group as a fixed effect and sampled_place as a random or fixed effect. It looks like sampled_place perfectly determines all the other variables you have in your random effects, so if you just want to control for those but not analyze anything about them in particular, using sampled_place is sufficient. In fact, those other variables are so highly collinear that I don’t think a model will even fit with both those variables and sampled_place included.
Will
Thank you for your answer, in addition to sample_place as a random variable, I also want to make feeding habits (what these hosts eat) as a random variable, for example, “YES” in the KP994558.1_s_Potentilla_acaulissta means that the host ate this plant, and “NO” does not eat it, so how do I add this random variable to the analysis?
Best wishes
If you want to include feeding habit, I would include it in fixed_effects since I suspect you’ll run into fitting issues with multiple random_effects, especially when sample_place completely explains the feeding habit (from what it looked like in the data). I would also include each feeding habit in a separate model if you’re going to include them since it looks like all the feeding habits together will be perfectly collinear with your main effect and therefore result in one or the other not being estimated.
Will
Thank Will. sample_place is not my focus, my focus is to introduce eating habits as a random variable, if I merge the random variable of feeding habit into a diet_group (as shown in the figure below), and use one such input as a random variable for feeding habit, do you think it’s okay?
To clarify, whatever you use as a random effect (whether it’s diet_group or sample_place) will not show up in your results table because random effects are only to control for grouping. Based on your data, I think each sample_place had a unique combination of diet items, so making one categorial variable out of each diet combination will likely give you the exact same result as if you had only included sample_place.
