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Caffery Yang edited this page Feb 18, 2023 · 1 revision

Welcome to the ggpicrust2 wiki!

ggpicrust2 Function

The ggpicrust2 function integrates pathway name/description annotations, ten of the most advanced differential abundance (DA) methods, and visualization of DA results.

Inputs

  • file: A character, the file path to store picrust2 export files
  • metadata: A tibble, consisting of sample information
  • group: A character, name of the group
  • pathway: A character, consisting of "EC", "KO", "MetaCyc"
  • daa_method: A character, the chosen differential abundance analysis (DA) method
  • ko_to_kegg: A character to control the conversion of KO abundance to KEGG abundance
  • p.adjust: A character, the method to adjust p-values
  • order: A character to control the order of the main plot rows
  • p_values_bar: A character to control if the main plot has the p_values bar
  • x_lab: A character to control the x-axis label name
  • select: A vector consisting of pathway names to be selected
  • reference: A character, a reference group level for several DA methods
  • colors: A vector consisting of colors number

Outputs

  • daa.results.df: A dataframe of DA results

Examples

Here is an example of using the ggpicrust2 function:

ggpicrust2(file = "picrust2_export.txt",
           metadata = sample_metadata,
           group = "treatment",
           pathway = "KO",
           daa_method = "ALDEx2",
           ko_to_kegg = FALSE,
           p.adjust = "BH",
           order = "group",
           p_values_bar = TRUE,
           x_lab = "pathway_name",
           select = c("pathway1", "pathway2"),
           reference = "control",
           colors = c("red", "blue"))

Pathway Information Annotation Function

This function annotates pathway information for three types of pathways: EC, KO, and MetaCyc.

Arguments

  • file: The address to store Picrust2 export files.
  • pathway: A character that specifies the pathway.
  • daa_results_df: A data frame that contains the results of the pathway_daa function.
  • ko_to_kegg: A character that determines whether to convert KO abundance to KEGG pathway abundance.

Process

  1. The function checks if the file and daa_results_df arguments are not null. If both are null, it throws an error.
  2. It checks the file format and reads the file.
  3. It adds a column named 'description' and annotates the pathways:
    • For 'KO' pathway, it loads the KO_reference.RData file, checks for matching KO_reference values, and fills in the 'description' column.
    • For 'EC' and 'MetaCyc' pathways, it loads the respective reference files and proceeds in a similar way.
  4. If the daa_results_df argument is not null, it checks whether to convert the KO abundance to KEGG pathway abundance:
    • If not, it proceeds in the same way as described above.
    • If yes, it filters the daa_results_df based on a p-value threshold of 0.05, adds four new columns, and connects to the KEGG database to get the latest results.
    • It then checks if the number of pathways with statistical significance is too many and prints a message accordingly. If not, it uses the keggGet function to retrieve the pathway information and fills in the new columns.
  5. Finally, it returns the annotated abundance or annotated daa_results_df, depending on the input.

Output

The annotated abundance or annotated daa_results_df.

ko2kegg_abundance function

This function converts the KO abundance in picrust2 export files to KEGG pathway abundance.

Input

  • file: A character string, the address of the file containing the KO abundance data obtained from picrust2 export files.

Output

  • A data frame, kegg_abundance

Functionality

  1. The function first determines the file format based on the file extension.
  2. It then reads the file using the appropriate delimiter based on the file format. If the file format is not one of the accepted formats (.tsv, .txt, or .csv), the function stops with an error message.
  3. The KEGG reference data is loaded from the package ggpicrust2.
  4. Sample names and KEGG pathway names are extracted from the abundance data and the reference data.
  5. A matrix is initialized to store the KEGG pathway abundance.
  6. The function then loops through each KEGG pathway and sample to calculate the abundance.
  7. The kegg pathways with zero abundance in all the samples are removed.
  8. The kegg abundance matrix is converted to a data frame.
  9. The updated kegg abundance data frame is returned as the final output.

Note: The calculation may take a long time, so the function provides a message to inform the user to be patient.

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