Error from pathway_errorbar

[lingvv]

Good day, Chen!

Thank you so much for creating this amazing tool!

I encountered an error code, as shown below, and unfortunately, I'm unsure how to resolve it:

Error in pathway_errorbar(abundance = abundance, daa_results_df = daa_sub_method_results_df,  : 
  Visualization with 'pathway_errorbar' cannot be performed because there are no features with statistical significance. For possible solutions, please check the FAQ section of the tutorial.

Please refer to the attachment for the details, and feel free to reach out if you require any additional information.
ggpicrust2 error code.txt

Your inputs are greatly appreciated!

CODE

abundance_file_ec <- ko_abundance_file <- "pred_metagenome_unstrat_ko_cecum.tsv"

metadata <- read_delim(
  "meta-rform-test.txt",
  delim = "\t",
  escape_double = FALSE,
  trim_ws = TRUE
)
metadata
# Run ggpicrust2 with input file path
results_file_input <- ggpicrust2(file = abundance_file_ec,
                                 metadata = metadata,
                                 group = "TreatmentType",
                                 pathway = "KO",
                                 daa_method = "limma voom",
                                 ko_to_kegg = TRUE,
                                 order = "pathway_class",
                                 p_values_bar = TRUE,
                                 x_lab = "pathway_name",
                                 reference = "Cecum_UC")

[cafferychen777]

Dear @lingvv,

Thank you for reaching out and for your interest in ggpicrust2. Based on the error message you encountered, it appears that the pathway_errorbar visualization could not be performed due to the absence of features with statistical significance in your analysis. This situation can arise for several reasons, and I'd like to offer some insights and suggestions that might help you address this issue:

1. Small or Non-existent True Difference: If the microbial communities or pathways you are comparing are very similar, it is indeed possible and expected that no significant differences will be found. This outcome suggests that the true difference between your groups may be minimal.

2. Sample Size: A small sample size may not provide sufficient statistical power to detect differences. Increasing the number of samples in your analysis could enhance your ability to identify significant differences.

3. Intra-group Variation: High variation within groups can obscure differences between groups. If your data show considerable variation within a single group, it might be worthwhile to investigate the presence of outliers or subgroups contributing to this variability. Cleaning your data or stratifying your analysis to accommodate these variations may be necessary.

To address these challenges, you might consider the following strategies:

• Increase Your Sample Size: More samples can increase statistical power, improving your chances of detecting significant differences.

• Decrease Intra-group Variation: Identifying and addressing outliers or subgroups within your data can help reduce variability and clarify any differences between groups.

• Change Statistical Methods or Adjust Parameters: Depending on your data and research questions, different statistical approaches or adjustments to your current method's parameters might yield better results. If you are using a parametric test, exploring a non-parametric option, or vice versa, could be beneficial.

It's crucial to remember that not finding significant results is also informative. It may indicate the absence of substantial differences between the groups you are studying. Such findings should be interpreted within the context of your specific study objectives, acknowledging that forcing statistical significance is not advisable.

I hope these suggestions help you move forward with your analysis. Please feel free to reach out if you need further assistance or have any more questions.

Best regards,
Chen YANG