Creating pathway error bar plots... Error in $<-.data.frame(*tmp*, "group", value = c(2L, 1L, 2L, 2L, : replacement has 179 rows, data has 1 In addition: Warning message: In cbind(sample = colnames(sub_relative_abundance_mat), group = Group, : number of rows of result is not a multiple of vector length (arg 2)

[Liviacmg]

Hey guys,

I am facing this error, does anyone know what can it be?

Creating pathway error bar plots...

Error in $<-.data.frame(*tmp*, "group", value = c(2L, 1L, 2L, 2L, :
replacement has 179 rows, data has 1
In addition: Warning message:
In cbind(sample = colnames(sub_relative_abundance_mat), group = Group, :
number of rows of result is not a multiple of vector length (arg 2)

[jveix]

Hi, I had the same error. In fact, it's a bug. In my case it seems to happen when I have only one significant feature (p_adjust < threshold). My workaround was to look for that feature (daa_results_df %>% filter(p_adjust < 0.05) %>% select(c("feature","p_adjust")), as suggested in the package), set the p threshold to something like 0.5 and manually select the significant feature and other random feature, as the code needs two features to create the plot.

[Liviacmg]

Hi, I had the same error. In fact, it's a bug. In my case it seems to happen when I have only one significant feature (p_adjust < threshold). My workaround was to look for that feature (daa_results_df %>% filter(p_adjust < 0.05) %>% select(c("feature","p_adjust")), as suggested in the package), set the p threshold to something like 0.5 and manually select the significant feature and other random feature, as the code needs two features to create the plot.

Thank you!!

[cafferychen777]

Hi @Liviacmg,
Thank you for reporting this issue. The error you're encountering in the pathway_errorbar function typically occurs when there's a mismatch between your abundance data samples and the Group vector length. This usually happens in two scenarios:

1. Only one significant feature exists (p_adjust < threshold)
2. Sample mismatch between abundance data and group assignments

Here are the detailed solutions:

1. For single significant feature cases:

# Option 1: Increase p-value threshold
p <- pathway_errorbar(
  abundance = kegg_abundance,
  daa_results_df = daa_results_df,
  Group = metadata$Diagnosis,
  p_values_threshold = 0.1,  # Higher threshold to include more features
  ko_to_kegg = TRUE
)

# Option 2: Manually select multiple features
significant_features <- daa_results_df %>% 
  arrange(p_adjust) %>% 
  slice(1:2) %>%  # Get top 2 features
  pull(feature)

p <- pathway_errorbar(
  abundance = kegg_abundance,
  daa_results_df = daa_results_df,
  Group = metadata$Diagnosis,
  select = significant_features,
  ko_to_kegg = TRUE
)

2. For sample mismatch cases:

# Verify sample matching
print("Abundance samples:")
print(colnames(kegg_abundance))
print("Group assignments:")
print(metadata$Diagnosis)

# Ensure sample order consistency
matching_samples <- intersect(colnames(kegg_abundance), metadata$SampleID)
kegg_abundance <- kegg_abundance[, matching_samples]
metadata <- metadata[match(matching_samples, metadata$SampleID), ]

Please let me know if you need any clarification or encounter other issues!

Best regards,
Chen