Data Validation

High-quality analysis requires valid, well-formatted data. MicrobiomeStat provides data validation to check format compliance and consistency.

Function Overview

Working with microbiome data often presents challenges related to consistency, data structure, and correct formatting. After constructing the mStat data object, it's pivotal to ensure its structure and content align with the expected standards. To facilitate this crucial validation step, MicrobiomeStat offers the mStat_validate_data() function. This tool provides automated checks and rectifications, safeguarding the integrity and validity of your microbiome datasets. By integrating this validation early in your analysis pipeline, you lay a robust foundation for subsequent analytical steps, minimizing potential discrepancies and errors down the line.

data(peerj32.obj)

mStat_validate_data(
  data.obj = peerj32.obj
)

The function meticulously evaluates each part of your data object, specifically the feature.tab, meta.dat, and feature.ann components. It ensures these components adhere to a set of predefined rules, safeguarding against common formatting and alignment issues.

Note: The mStat_validate_data() function, while comprehensive, is not exhaustive in its current form. There are scenarios that might not be captured during the validation process. As such, even if the data passes the mStat_validate_data() checks, it doesn't guarantee that all functions will operate without issues. It simply indicates that the data does not have any apparent inconsistencies detected by the current validation rules. Users are encouraged to always be vigilant and explore their data further to ensure optimal compatibility across all functions in MicrobiomeStat.

Key Validation Steps:

1. List Verification: The initial step is to confirm that data.obj is a list, which is essential as microbiome data objects consist of various elements and data structures.

2. Metadata Structure Verification: Ensuring that meta.dat is a dataframe is crucial for its structured storage and ease of querying. If it isn't already a dataframe, the function converts it.

3. Row Name Matching: feature.tab and feature.ann should have identical row names. The function rectifies any inconsistencies by ensuring the row names of both components match.

4. Column-Row Alignment: An essential validation step is to ensure that the column names in feature.tab match the row names of meta.dat. This ensures alignment between feature abundances and their corresponding metadata.

5. Matrix Verification: The function confirms that both feature.tab and feature.ann are matrices. This validation ensures the data structures are optimally formatted for subsequent analysis within MicrobiomeStat.

6. Custom Checks: As datasets and requirements evolve, users can expect the integration of more rules to accommodate new data structures and elements.

Crucial Points to Remember:

• While mStat_validate_data() is adept at adjusting certain inconsistencies, it's crucial for users to ensure data components are structured as base R data.frame and matrix structures. Package-specific formats, such as phyloseq's formal class, might require additional preprocessing.

• A successful validation is an excellent first step, but it doesn't guarantee an absolute absence of data issues. Regular data exploration and supplementary validations are encouraged to ensure comprehensive data accuracy.

Benefits of Using mStat_validate_data():

• Analytical Compatibility: By conforming data to expected structures, users ensure their datasets are primed for analyses using MicrobiomeStat's suite of functions.

• Proactive Error Handling: The function anticipates and rectifies potential format issues, preventing errors in later stages of analysis.

• Data Quality Assurance: Regular validations ensure consistent data integrity, especially valuable when incorporating new data or making extensive modifications.

In summary, effective microbiome analysis starts with validated and well-structured data. The mStat_validate_data() function plays a pivotal role in this process, offering users a streamlined mechanism to validate and adjust their datasets. As the adage goes, "Garbage in, garbage out." With this function, MicrobiomeStat aims to ensure that only quality data drives your analyses.