MicrobTiSDA (version 0.1.0)

Design: Create Design Matrix for Regression Analysis

Description

Design creates the design matrix of dummies for fitting regression models of microbiota in time.

Usage

Design(metadata, Group_var = NULL, Pre_processed_Data, Sample_Time, Sample_ID)

Value

An object of class Design, which contains:

data

A data frame ready for regression modeling.

params

A list of parameters used to construct the design.

Arguments

metadata

A data frame containing information for all samples, which should be identical to the metadata received by other functions in MicrobTiSDA.

Group_var

A string or a vector. Same as the Group_var in Data.trans.

Pre_processed_Data

The transformed data output from the Data.trans function. A pre-processed OTU data frame with sample IDs as row names and OTU IDs as column names.

Sample_Time

A character string indicating the column name in metadata that contains sample time information.

Sample_ID

A character string indicating the column name in metadata that contains sample identifiers.

Author

Shijia Li

Details

The main functionality of Design is to add user-selected sample information to the pre-processed OTU/ASV table as independent variables for fitting the OTU time series regression models. One necessary independent variable for fitting is Time, so the default output of this function is the transformed OTU/ASV table with added sample Time information. If the user also inputs other qualitative variables such grouping, gender, etc., the function will define dummy variables to distinguish each group based on the number of qualitative variables entered by the user and the grouping situation of samples based on qualitative variables. Moreover, the subject ID of each sample will be added as a column to the generated design matrix.

Examples

# \donttest{
# Example metadata with grouping variables
metadata <- data.frame(
  TimePoint = c(1, 2, 3, 4),
  Sample = c('S1', 'S2', 'S3', 'S4'),
  GroupA = c('A', 'A', 'B', 'B'),
  GroupB = c('X', 'Y', 'X', 'Y')
)

# Example pre-processed data (e.g., transformed abundance data)
Pre_processed_Data <- data.frame(
  Feature1 = rnorm(4),
  Feature2 = rnorm(4)
)

# Create design matrix using grouping variables
design_data <- Design(metadata, Group_var = c('GroupA', 'GroupB'), Pre_processed_Data,
                      Sample_Time = 'TimePoint', Sample_ID = 'Sample')

# Create design data without grouping variables
design_data_no_group <- Design(metadata, Group_var = NULL, Pre_processed_Data,
                               Sample_Time = 'TimePoint', Sample_ID = 'Sample')
# }