SOHPIE_DNA {SOHPIE}

SOHPIE_DNA

Description

A pseudo-value regression approach for differential co-abundance network analysis that adjusts for additional covariates.

Usage

SOHPIE_DNA(OTUdat, clindat, groupA, groupB, c)

Arguments

OTUdat

An OTU table with subjects in rows and taxa in columns.

clindat

A subdata consisting of the clinical and demographic variables that the user wants to include in the regression. (e.g., binary group indicator for intervention vs. control, continuous age, ...)

groupA

Indices of the subjects in the first category (e.g., not living with a dog; see example below with American Gut Project sample data) of binary group variable.

groupB

Indices of the subjects in the second category (e.g., living with a dog; see example below with American Gut Project sample data) of binary group variable.

c

The choice of trimming proportion for the least trimmed estimator of robust regression. A value has to be between 0.5 and 1 as specified in ltsReg() function in robustbase package.

Value

A list containing three data frame objects returned from this SOHPIE_DNA main function. A user will see beta coefficients, p-values, and adjusted p-values (q-values) for each predictor variables that are included in the regression model.

References

Ahn S, Datta S. Differential Co-Abundance Network Analyses for Microbiome Data Adjusted for Clinical Covariates Using Jackknife Pseudo-Values. ArXiv [Preprint]. 2023 Mar 23:arXiv:2303.13702v1. PMID: 36994149; PMCID: PMC10055480.

Examples

# In this example, the subset of the American Gut Project data will be used.
data(combinedamgut) # A complete data containing columns with taxa and clinical covariates.

# Note: The line below will use a toy example with the first 30 out of 138 taxa.
OTUtab = combinedamgut[ , 8:37]
 #Clinical/demographic covariates (phenotypic data):
# Note: All of these covariates will be included in the regression, so
# please make sure that phenodat includes the variables that will be analyzed only.
phenodat = combinedamgut[, 1:7] # first column is ID, so not using it.
# Obtain indices of each grouping factor
# In this example, a variable indicating the status of living
# with a dog was chosen (i.e. bin_dog).
# Accordingly, Groups A and B imply living without and with a dog, respectively.
newindex_grpA = which(combinedamgut$bin_dog == 0)
newindex_grpB = which(combinedamgut$bin_dog == 1)

SOHPIEres <- SOHPIE_DNA(OTUdat = OTUtab, clindat = phenodat,
groupA = newindex_grpA, groupB = newindex_grpB, c = 0.5)