There is always a problem when merging metedata.tsv and feature-table.tsv. it says "The error message Invalid number of rows in lefse_input suggests that the lefse_input object doesn't have the expected number of rows after binding the feature names, class labels, and feature data. It might be due to a mismatch in dimensions or the way the data is being bound.", can you check the format of both files and manually correct it. sample of metadata and first few raws of feature-table is given here. sample_name treatment q2:types categorical Bac_1 Bacteria Bac_2 Bacteria Bac_3 Bacteria CT_1 Control CT_2 Control CT_3 Control SAL_1 Salicylic acid SAL_2 Salicylic acid SAL_3 Salicylic acid Bac+SAL_1 Bacteria + Salicylic acid Bac+SAL_2 Bacteria + Salicylic acid Bac+SAL_3 Bacteria + Salicylic acid Patho_1 Pathogen Patho_2 Pathogen Patho_3 Pathogen

and feature table OTU ID Bac_1 Bac_2 Bac_3 Bac+SAL_1 Bac+SAL_2 Bac+SAL_3 CT_1 CT_2 CT_3 Patho_1 Patho_2 Patho_3 SAL_1 SAL_2 SAL_3 48818bc55f13954529e0dd9e8b59325e 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 da414831ac25c55a0e28b0835ecc7ee9 0 0 0 81 0 0 0 0 0 0 0 0 0 0 0 7ccc8e9f26c9630205018bf22986f65b 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0

sample script as follows

Load necessary libraries

library(dplyr)

Step 1: Read the feature table and metadata

Replace the paths with the actual paths to your files

metadata <- read.delim("metadata.tsv", header = TRUE, sep = "\t", comment.char = "#") feature_table <- read.delim("feature-table.tsv", header = TRUE, row.names = 1, sep = "\t")

Step 2: Clean and standardize sample names

In feature table, replace hyphens with dots to match metadata

colnames(feature_table) <- gsub("-", ".", colnames(feature_table))

In metadata, replace underscores with dots and plus signs with dots to match the feature table

metadata$sample_name <- gsub("_", ".", metadata$sample_name) metadata$sample_name <- gsub("\+", ".", metadata$sample_name)

Step 3: Transpose the feature table so that samples are rows and features are columns

transposed_feature_table <- feature_table %>% t() %>% as.data.frame()

Step 4: Merge metadata with the transposed feature table based on sample names

transposed_feature_table$sample_name <- rownames(transposed_feature_table) merged_data <- transposed_feature_table %>% inner_join(metadata, by = "sample_name")

Step 5: Prepare feature data and LEfSe input

Remove sample_name and treatment columns to get feature data

feature_data <- merged_data %>% select(-sample_name, -treatment)

Add rownames (feature IDs) as a separate column

feature_data$Feature_ID <- rownames(feature_data)

Rearrange columns so that Feature_ID is the first column

feature_data <- feature_data[, c(ncol(feature_data), 1:(ncol(feature_data) - 1))]

Create LEfSe input file

lefse_input <- rbind( c("Feature ID", as.character(merged_data$treatment)), # First row: Feature ID and class labels (treatment) feature_data # Remaining rows: feature data )

Step 6: Write LEfSe input file to a TSV

Write the LEfSe-formatted data to a file

write.table(lefse_input, file = "lefse_input.tsv", sep = "\t", quote = FALSE, row.names = FALSE, col.names = FALSE)

At this point, you have a LEfSe input file ("lefse_input.tsv")

The rest of the LEfSe analysis will be performed using LEfSe (in a Linux environment)

Step 7 (Optional): If running LEfSe on a Linux machine, the next steps are command-line based.

The typical LEfSe pipeline involves these commands:

Format the input file for LEfSe

Run in the command line in Linux:

$ format_input.py lefse_input.tsv lefse_input.in -c 2 -o 1000000 -u 1 -v 0.05

Run the LEfSe analysis

$ run_lefse.py lefse_input.in lefse_output.res

Visualize the results

$ plot_res.py lefse_output.res lefse_LDA.png --dpi 300 --format png

# I can not pass step3, 4 and 5 without getting an error massage.
(package:dplyr" "package:stats" > # Load the metadata file > metadata <- read.delim("metadata.tsv", header = TRUE, sep = "\t", comment.char = "#") > # Load the feature table (assuming it's a TSV file) > feature_table <- read.delim("feature-table.tsv", header = TRUE, row.names = 1, sep = "\t") Error in read.table(file = file, header = header, sep = sep, quote = quote, : more columns than column names > # Load the feature table (assuming it's a TSV file) > feature_table <- read.delim("feature-table.tsv", header = TRUE, row.names = 1, sep = "\t") > # Ensure that the row names of the feature table correspond to sample names in the metadata > # The sample names in metadata should match the column names in the feature table > # Ensure column names in the feature table match sample names in metadata > colnames(feature_table) <- gsub("_", "-", colnames(feature_table)) > # Merge metadata with feature table > merged_data <- feature_table %>% + t() %>% # Transpose the feature table to have samples as rows + as.data.frame() %>% + mutate(sample_name = row.names(.)) %>% + inner_join(metadata, by = "sample_name") > # Reformat the data for LEfSe input > # Get the feature table part and ensure that it's in numeric format > feature_data <- merged_data %>% + select(-sample_name, -treatment) %>% + t() %>% + as.data.frame() > # Create a LEfSe input file with the following structure: > lefse_input <- rbind( + feature_names = rownames(feature_data), # First row with feature names + class_labels = merged_data$treatment, # Second row with class labels (treatment) + feature_data # Remaining rows with feature abundance data + ) > # Set proper row names > row.names(lefse_input)[1:2] <- c("Feature ID", "Class Label") Error in .rowNamesDF<-(x, value = value) : invalid )

sessionInfo(R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)
Matrix products: default
 )