pvalueIndAnalysis: Calculation p-value for each gene and study

In Juananvg/DExMA: Differential Expression Meta-Analysis

Calculation p-value for each gene and study

Description

This function uses t-test based on limma package in other to obtain the individual p-values for each study and gene

Usage

pvalueIndAnalysis(objectMA, missAllow = 0.3)

Arguments

objectMA	A list of list. Each list contains two elements. The first element is the expression matrix (genes in rows and sample in columns) and the second element is a vector of zeros and ones that represents the state of the diffenrent samples of the expression matrix. 0 represents one group (controls) and 1 represents the other group (cases). The result of the CreateObjectMA can be used too.
missAllow	a number that indicates the maximun proportion of missing values allowed in a sample. If the sample has more proportion of missing values the sample will be eliminated. In the other case the missing values will be imputed using the K-NN algorithm.

Value

A list formed by two elements:

• First element (p) is a dataframe were columns are each of the studies (datasets) and rows are the genes. Each element of the dataframe represents the p-value.

• Second element (logFC) is a dataframe were columns are each of the studies (datasets) and rows are the genes. Each element of the dataframe is the logFC.

• Third element (weights_z) is a dataframe were columns are each of the studies (datasets) and rows are the genes. Each element of the dataframe represents the necessary weights for Stouffer's method.

Author(s)

Juan Antonio Villatoro Garcia, [e-mail]

See Also

createObjectMA, metaAnalysisDE

Examples

data(DExMAExampleData)

pvalues <- pvalueIndAnalysis(objectMA=maObject, missAllow=0.3)
pvalues

Juananvg/DExMA documentation built on Dec. 5, 2023, 1:12 p.m.