fitFeatureModel: Computes differential abundance analysis using a...

In HCBravoLab/metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Computes differential abundance analysis using a zero-inflated log-normal model

Description

Wrapper to actually run zero-inflated log-normal model given a MRexperiment object and model matrix. User can decide to shrink parameter estimates.

Usage

fitFeatureModel(obj, mod, coef = 2, B = 1, szero = FALSE, spos = TRUE)

Arguments

obj	A MRexperiment object with count data.
mod	The model for the count distribution.
coef	Coefficient of interest to grab log fold-changes.
B	Number of bootstraps to perform if >1. If >1 performs permutation test.
szero	TRUE/FALSE, shrink zero component parameters.
spos	TRUE/FALSE, shrink positive component parameters.

Value

A list of objects including:

• call - the call made to fitFeatureModel

• fitZeroLogNormal - list of parameter estimates for the zero-inflated log normal model

• design - model matrix

• taxa - taxa names

• counts - count matrix

• pvalues - calculated p-values

• permuttedfits - permutted z-score estimates under the null

See Also

cumNorm

Examples

data(lungData)
lungData = lungData[,-which(is.na(pData(lungData)$SmokingStatus))]
lungData=filterData(lungData,present=30,depth=1)
lungData <- cumNorm(lungData, p=.5)
s <- normFactors(lungData)
pd <- pData(lungData)
mod <- model.matrix(~1+SmokingStatus, data=pd)
lungres1 = fitFeatureModel(lungData,mod)

HCBravoLab/metagenomeSeq documentation built on March 17, 2024, 3:21 p.m.